Analysis Of Features Research Articles

Safeguarding public facilities: a study on enhancing safety in mosque amenities through slip-resistance and surface innovations

Purpose This study critically examines the safety aspects of ablution spaces in United Arab Emirates (UAE) mosques, emphasising the evaluation of smooth ceramic tile floorings prone to water accumulation. As an integral practice in Islamic worship, ablution spaces cater to a diverse demographic, making their safety a paramount concern. This study aims to meticulously assess slip-resistance properties and surface characteristics to provide practical recommendations for enhanced safety measures. Design/methodology/approach The investigation involves in situ measurements of traction properties and an extensive analysis of surface features in thirty mosques across the UAE. This comprehensive approach allows for identifying the unique challenges posed by ceramic tile floorings in ablution spaces. The study uses a portable tribometer for dynamic friction coefficient measurements, ensuring accurate slip-resistance evaluations. Surface texture analysis uses a portable profilometer to quantify roughness parameters. Findings The study’s findings significantly advance the comprehension of safety considerations in ablution spaces. Through empirical evidence and evidence-based insights, the research identifies immediate safety concerns and provides practical recommendations to create secure and inclusive environments within mosques. The emphasis lies on safeguarding the well-being of worshippers and contributing to the broader goal of safety in religious spaces. Originality/value The original contribution of this study lies in its dual focus on immediate safety concerns and the deeper understanding of unique safety challenges within ablution spaces. By offering evidence-based insights and practical recommendations, the research strives to make meaningful contributions to creating secure and inclusive environments within mosques.

Multiomic Signatures of Traffic-Related Air Pollution in London Reveal Potential Short-Term Perturbations in Gut Microbiome-Related Pathways.

This randomized crossover study investigated the metabolic and mRNA alterations associated with exposure to high and low traffic-related air pollution (TRAP) in 50 participants who were either healthy or were diagnosed with chronic pulmonary obstructive disease (COPD) or ischemic heart disease (IHD). For the first time, this study combined transcriptomics and serum metabolomics measured in the same participants over multiple time points (2 h before, and 2 and 24 h after exposure) and over two contrasted exposure regimes to identify potential multiomic modifications linked to TRAP exposure. With a multivariate normal model, we identified 78 metabolic features and 53 mRNA features associated with at least one TRAP exposure. Nitrogen dioxide (NO2) emerged as the dominant pollutant, with 67 unique associated metabolomic features. Pathway analysis and annotation of metabolic features consistently indicated perturbations in the tryptophan metabolism associated with NO2 exposure, particularly in the gut-microbiome-associated indole pathway. Conditional multiomics networks revealed complex and intricate mechanisms associated with TRAP exposure, with some effects persisting 24 h after exposure. Our findings indicate that exposure to TRAP can alter important physiological mechanisms even after a short-term exposure of a 2 h walk. We describe for the first time a potential link between NO2 exposure and perturbation of the microbiome-related pathways.

Robust prediction of workability properties for 3D printing with steel slag aggregate using bayesian regularization and evolution algorithm

This study addresses the growing interest in utilizing steel slag as a sustainable alternative to river sand in additive manufacturing of concrete, driven by the increasing scarcity of natural resources. The rheological properties of fresh material significantly impact the quality of 3D-printed filament, necessitating suitable workability for printability. The research focuses on evaluating the influence of steel slag aggregate and key admixtures, such as silica fume and superplasticizer, on the workability properties of fresh concrete. Through an extensive series of 90 slump tests, optimal combinations ensuring the desired workability for 3D printing applications were identified. To enhance the manufacturing of fresh concrete and develop user-friendly tools, a novel soft computing approach is introduced—Adaptive Elitist Differential Evolution coupled with Bayesian Regularization Artificial Neural Network (aeDE-BRANN). This advanced model considers five critical input parameters: silica fume to Portland cement ratio, steel slag to cement ratio, water to cement ratio, cement content, and superplasticizer dosage. The model outputs crucial workability metrics, including slump flow and slump. In a comprehensive comparative study, the aeDE-BRANN framework demonstrates superior performance in terms of both simplicity and efficiency when compared to other forecasting models. Feature analysis techniques, including Shapley values from game theory and partial dependence plots, provide valuable insights into the intricate relationships between input variables and workability properties. The findings underscore the water to cement ratio as the most influential factor on workability, followed by silica fume to Portland cement ratio and steel slag to cement ratio. This study contributes a reliable tool for predicting workability properties, aiding in the selection of suitable steel slag aggregate and admixtures. Ultimately, it facilitates the sustainable and innovative evolution of 3D concrete printing practices.

Tongue feature recognition to monitor rehabilitation: deep neural network with visual attention mechanism

ObjectiveWe endeavor to develop a novel deep learning architecture tailored specifically for the analysis and classification of tongue features, including color, shape, and coating. Unlike conventional methods based on architectures like VGG or ResNet, our proposed method aims to address the challenges arising from their extensive size, thereby mitigating the overfitting problem. Through this research, we aim to contribute to the advancement of techniques in tongue feature recognition, ultimately leading to more precise diagnoses and better patient rehabilitation in Traditional Chinese Medicine (TCM).MethodsIn this study, we introduce TGANet (Tongue Feature Attention Network) to enhance model performance. TGANet utilizes the initial five convolutional blocks of pre-trained VGG16 as the backbone and integrates an attention mechanism into this backbone. The integration of the attention mechanism aims to mimic human cognitive attention, emphasizing model weights on pivotal regions of the image. During the learning process, the allocation of attention weights facilitates the interpretation of causal relationships in the model’s decision-making.ResultsExperimental results demonstrate that TGANet outperforms baseline models, including VGG16, ResNet18, and TSC-WNet, in terms of accuracy, precision, F1 score, and AUC metrics. Additionally, TGANet provides a more intuitive and meaningful understanding of tongue feature classification models through the visualization of attention weights.ConclusionIn conclusion, TGANet presents an effective approach to tongue feature classification, addressing challenges associated with model size and overfitting. By leveraging the attention mechanism and pre-trained VGG16 backbone, TGANet achieves superior performance metrics and enhances the interpretability of the model’s decision-making process. The visualization of attention weights contributes to a more intuitive understanding of the classification process, making TGANet a promising tool in tongue diagnosis and rehabilitation.

Large-scale bacterial genomic and metagenomic analysis reveals Pseudomonas aeruginosa as potential ancestral source of tigecycline resistance gene cluster tmexCD-toprJ

BackgroundThe global dissemination of the multidrug resistance efflux pump gene cluster tmexCD-toprJ has greatly weakened the effects of multiple antibiotics, including tigecycline. However, the potential origin and transmission mechanisms of the gene cluster remain unclear. MethodsHere, we concluded a comprehensive bioinformatics analysis on integrated 73,498 bacterial genomes, including Pseudomonas spp., Klebsiella spp., Aeromonas spp., Proteus spp., and Citrobacter spp., along with 1,152 long-read metagenomic datasets to trace the origin and propagation of tmexCD-toprJ. ResultsOur results demonstrated that tmexCD-toprJ was predominantly found in Pseudomonas aeruginosa sourced from human hosts in Asian countries and North American countries. Phylogenetic and genomic feature analyses showed that tmexCD-toprJ was likely evolved from mexCD-oprJ of some special clones of P. aeruginosa. Furthermore, metagenomic analysis confirmed that P. aeruginosa is the only potential ancestral bacterium for tmexCD-toprJ. A putative mobile genetic structure harboring tmexCD-toprJ, int-int-hp-hp-tnfxB-tmexCD-toprJ, was the predominant genetic context of tmexCD-toprJ across various bacterial genera, suggesting that the two integrase genes play a pivotal role in the horizontal transmission of tmexCD-toprJ. ConclusionsBased on these findings, it is almost certain that the tmexCD-toprJ gene cluster was derived from P. aeruginosa and further spread to other bacteria.

Paleo-geomorphic features of pre-Jurassic and its oil-controlling effect in Wuqi–Dingbian area

The Fuxian-Yan10 layers are the main oil-producing reservoirs of Jurassic in Wuqi–Dingbian area of Ordos Basin. However, due to the lack of understanding of the pattern and distribution characteristics of oil reservoirs, the benefits of exploration and development are restricted. In order to provide theoretical guidance for the study of similar geological features, based on the analysis of paleogeomorphic features and evolution, the analysis focuses on the influence of palaeo-geomorphology on oil reservoir distribution, and summarizes the main types of reservoir models in the study area. The results show that there are four types of palaeo-geomorphic units in the Wuding area: palaeo-river, slope, highland and interriver hill. In the study area, the Jurassic paleogeomorphology controls the sedimentary development and distribution from Fuxian Formation to Yan 9 Formation. The compacted structure and lithologic barrier provide good trapping conditions for the paleogeomorphic oil. Moreover, the swampy coal measures and mudstone at the top of Yan 9 play a sealing role for oil accumulation, and the bottom water was obviously driven. In addition, the pre-Jurassic deep valley was the main channels for oil migration. On this basis, it is concluded that there are four reservoir-forming models in Wuding area: slope type, river hill type, ancient river type and highland type.

Diagnostics of the influence of medical and demographic indicators on regional socio-economic systems

The article presents approaches to monitoring and assessing social aspects of regional systems with identification of elements of the health system and demographic components that potentially affect the economic efficiency of functioning of territories. The relevance of the article is determined by objective features of the development of mesoterritories. The analysis of spatial features of the systems’ functioning was conducted on the materials of the Central Federal District. The purpose of the study is to assess the features of the regional genesis using regression analysis and the resulting indicator – gross regional product (hereinafter referred to as GRP), which allows us to give a more reliable characterisation of the mesoterritorial transformation. The subjects were grouped according to the quality of medical and demographic characteristics on the basis of the cluster analysis with further allocation of flagship, catching-up and lagging regions. To substantiate the reliability of the territories’ affiliation to a specific cluster group, a statistical analysis was performed on average values with consideration to identification of the level of medical and demographic development. The conclusion is made about the influence of some factor criteria on the regional space with the construction of a scatter plot. It displays the affiliation to the selected confidence interval and distribution of the elements of the set in the plane between the resulting and factor values, describing the dependence of the GRP on the medical and demographic indicator. These econometric and analytical models can be used by regional governments in the process of strategic planning.

Hydrogel-based flexible degradable triboelectric nanogenerators for human activity recognition

The development of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has led to an increased demand for wearable electronic devices that can be used in a variety of contexts. Hydrogel sensors have been regarded as an ideal material for the fabrication of multifunctional flexible wearable devices. However, it is difficult to create hydrogel sensors with ideal electrical and mechanical properties. In this study, a conductive hydrogel prepared by a simple method has been introduced. The hydrogel was immersed in a binary solvent of glycerol, water and sodium citrate to form a transparent, long-term stable and highly conductive gelatin-NaCl organohydrogel (GNOH). A stretchable and durable gelatin Ecoflex triboelectric nanogenerator (GE-TENG) was created by combining an organic hydrogel with Ecoflex elastomer. It has high electrical and mechanical performance, with an open-circuit voltage of 142 V, current of 3.8 μA, power output of 19.36 μW and response time of 85 ms. Moreover, the single-electrode mode of the GE-TENG provides high electrical output for powering portable electronic devices and can capture biomechanical energy in temperatures as low as −20 °C. As a result of these capabilities, GE-TENG can effectively identify human movements with precision and transmit signals wirelessly, thereby broadening its potential applications. The GE-TENG-based self-powered intelligent sensing system (GSIS) for human-computer interaction demonstrates a wearable intelligent system for controlling the movement of a tank model and a manipulator by integrating the signal acquisition, signal processing and signal output components. The system achieves 100% signal recognition accuracy when combined with covariance matrix feature analysis and Convolutional Neural Network classification algorithms. This research demonstrates the potential of wearable hydrogel-based electronic devices for monitoring human motion, harvesting biomechanical energy and human-computer interaction.

Образные единицы в коммуникативных практиках медиатизированного политического дискурса

The article is dedicated to the communicative features of mediatized political discourse, which remain insufficiently explored. Using the example of the use of figurative means of language in the genres of media-political communications, it is demonstrated that the mediatized form of communication possesses not only the basic functions of professional discourse but also additional functions: amplifying or softening content, saving linguistic means, emotional and attractive functions. Each of these functions plays a significant role in shaping audience perception and influencing public opinion on political issues. With the help of contextual and semantic analysis, as well as the corpus method, the following results were obtained: in the studied context, figurative units are important linguistic means used for the effective conveyance of specific political views and emotions. These units include figurative metaphors and comparisons, phraseology, proverb, precedent phenomena and colloquial expressions. The novelty of the research lies in the analysis of communicative features and functions of figurative units in various genres of mediatized political discourse: political talk shows, political interviews, and internet texts of politicians. The research results can be applied in the practice of media communication, communicative linguistics, and discourse theory.

Systematic analysis of ocular features and responses of cultured spotted wolffish (Anarhichas minor).

A better understanding of unique anatomical and functional features of the visual systems of teleost fish could provide key knowledge on how these systems influence the health and survival of these animals in both wild and culture environments. We took a systematic approach to assess some of the visual systems of spotted wolffish (Anarhichas minor), a species of increasing importance in North Atlantic aquaculture initiatives. The lumpfish (Cyclopterus lumpus) was included in these studies in a comparative manner to provide reference. Histology, light and electron microscopy were used to study the spatial distribution and occurrence of cone photoreceptor cells and the nature of the retinal tissues, while immunohistochemistry was used to explore the expression patterns of two photoreceptor markers, XAP-1 and XAP-2, in both species. A marine bacterial infection paradigm in lumpfish was used to assess how host-pathogen responses might impact the expression of these photoreceptor markers in these animals. We define a basic photoreceptor mosaic and present an ultrastructural to macroscopic geographical configuration of the retinal pigment tissues in both animals. Photoreceptor markers XAP-1 and XAP-2 have novel distribution patterns in spotted wolffish and lumpfish retinas, and exogenous pathogenic influences can affect the normal expression pattern of XAP-1 in lumpfish. Live tank-side ophthalmoscopy and spectral domain optical coherence tomography (SD-OCT) revealed that normal cultured spotted wolffish display novel variations in the shape of the retinal tissue. These two complementary imaging findings suggest that spotted wolffish harbour unique ocular features not yet described in marine teleosts and that visual function might involve specific retinal tissue shape dynamics in these animals. Finally, extensive endogenous biofluorescence is present in the retinal tissues of both animals, which raises questions about how these animals might use retinal tissue in novel ways for visual perception and/or communication. This work advances fundamental knowledge on the visual systems of two economically important but now threatened North Atlantic teleosts and provides a basic foundation for further research on the visual systems of these animals in health versus disease settings. This work could also be useful for understanding and optimizing the health and welfare of lumpfish and spotted wolffish in aquaculture towards a one health or integrative perspective.

Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.

Genetic and clinical profile of 15 Chinese families with GDAP1-related Charcot-Marie-Tooth disease and identification of H256R as a frequent mutation.

Mutations in ganglioside-induced differentiation-associated protein 1 (GDAP1) cause axonal or demyelinating Charcot-Marie-Tooth disease (CMT) with autosomal dominant or recessive inheritance. In this study, we aim to report the genotypic and phenotypic features of GDAP1-related CMT in a Chinese cohort. Clinical, neurophysiological, genetic data, and available muscle/brain imaging information of 28 CMT patients with GDAP1 variants were retrospectively collected. We identified 16 GDAP1 pathogenic variants, among which two novel variants c.980dup(p.L328FfsX25) and c.480+4T>G were first reported. Most patients (16/28) presented with AR or AD CMT2K phenotype. Clinical characteristics in our cohort demonstrated that the AR patients presented earlier onset, more severe phenotype compared with the AD patients. Considerable intra-familial phenotypic variability was observed among three AD families. Muscle atrophy and fatty infiltration in the lower extremity were detected by Muscle magnetic resonance imaging (MRI) scans in four patients. MRI showed two AR patients showed more severe muscle involvement of the posterior compartment than those of the anterolateral compartment in the calf. One patient carrying Q38*/H256R variants accompanied with mild periventricular leukoaraiosis. In this study, we conducted an analysis of clinical features of the GDAP1-related CMT patients, expanded the mutation spectrum in GDAP1 by reporting two novel variants, and presented the prevalent occurrence of the H256R mutation in China. The screening of GDAP1 should be particularly emphasized in Chinese patients with CMT2, given the incomplete penetrance and pathogenic inheritance patterns involving dominant and recessive modes.

Drug Recommendation System Based on Sentiment Analysis of Drug Reviews using Machine Learning

People are self-medicating more during the COVID-19 pandemic because they can't get to good medical tools. This makes their health situations worse. This study suggests a drug suggestion system that uses machine learning and emotion analysis of patient reviews to make the job of healthcare workers easier. We use different vectorization methods, like Bag of Words (BoW), TF-IDF, Word2Vec, and Manual Feature Analysis, to guess how people feel about certain diseases and suggest the best drugs for them. We use classification methods, such as LinearSVC, to rate emotions based on their accuracy, F1-score, precision, and AUC score. The results show that LinearSVC with TF-IDF vectorization works well, as it achieved 93% accuracy, which was better than other models. By making drug suggestions, this system aims to make it easier for people to get medical care when they need it most, especially during emergencies.

Chemoreactomic analysis of acyzole in comparison with zinc derivatives of nonsteroidal anti-inflammatory drugs

Background. Nonsteroidal anti-inflammatory drugs (NSAIDs) are characterized by ulcerogenic effects and used for effective and safe pharmacotherapy of inflammation and pain. The zinc-containing drug acyzole is a promising anti-inflammatory drug that potentially does not have this drawback. Objective: to conduct chemoreactomic modeling of the pharmacological effects of acyzole and zinc derivatives of well-known NSAIDs (diclofenac, nimesulide, ketorolac).Material and methods. The analysis of the pharmacological capabilities of NSAIDs was based on a chemoinformatic approach, i.e. comparing the chemical structure of the studied molecules with the structures of millions of other molecules with established molecular pharmacological properties. The analysis procedure was based on the latest machine learning technologies developed in the theory of topological and metric analysis of feature descriptions.Results. It was shown that acyzole might have an anti-inflammatory effect due to its impact on the activity of cytokines and, partly, on the metabolism of prostaglandins and leuktrienes. The central effects of acyzole are comparable to those of zinc-NSAIDs. The analgesic effect of acyzole may be associated with kinin receptors inhibition, and weak antihistamine and antinociceptin effects. Acizol may also exhibit a gastroprotective effect. It was established that acyzole, to a lesser extent than the reference molecules, negatively affected the metabolism of vitamins and microelements.Conclusion. Chemoreactomic profiling of acyzole indicates prospects for its use as an anti-inflammatory drug.

Analysis of clinical features and prognostic factors in Takayasu arteritis involving pulmonary hypertension: A retrospective study.

Multiple takayasu arteritis (TA) is a chronic nonspecific large to medium vasculitis disease that mainly accumulates the aorta and its branches. Pulmonary vascular disease is often seen as stenosis and occlusion, and patients may show no moderate to severe pulmonary hypertension (PH). This study aims to summarize the clinical characteristics and analysis of prognostic factors in patients with PH caused by TA. Patients diagnosed with aortitis involving the pulmonary artery by pulmonary arteriography or pulmonary artery and total aortic computed tomography arteriography (CTA). All patients underwent detailed clinical assessment, laboratory data collection, and analysis of imaging data. Patients were followed up and factors affecting the prognosis of the pulmonary arteries were analyzed. Most of the patients' complaints were chest tightness, shortness of breath, decreased activity tolerance, hemoptysis and chest pain. 56.90% of the patients were in at the time of admission. Echocardiographic estimation of pulmonary artery systolic pressure was 90.39 ± 22.87 mm Hg. In terms of laboratory tests, 39.66%% of the patients had elevated C-reactive protein and erythrocyte sedimentation rate, and amino-terminal natriuretic peptide precursor on admission. In terms of imaging, all patients had pulmonary artery involvement, which was combined with aortic involvement in 31.03%. Nuclide lung perfusion/ventilation imaging of the patients revealed multiple perfusion defects/absences in the segmental and subsegmental distribution of the lungs. Univariate Cox regression model analysis suggested that patients' WHO functional class at admission, age ≧ 51 years at the time of consultation, and amino-terminal natriuretic peptide precursor ≧ 3500 pg/mL were factors affecting the prognosis. Further multifactorial Cox regression model analysis suggested amino-terminal natriuretic peptide precursor ≧ 3500 pg/mL was an independent predictor of poor prognosis with a hazard ratio (HR) value of 5.248. Electrocardiogram and echocardiogram may suggest an increased right heart load; some patients have elevated serum inflammatory indexes. Characteristic imaging manifestations include widening of the main pulmonary artery, multiple pulmonary segmental and subsegmental stenoses.

Integrated End-to-End automatic speech recognition for languages for agglutinative languages

The relevance of the problem of automatic speech recognition lies in the lack of research for low-resource languages, stemming from limited training data and the necessity for new technologies to enhance efficiency and performance. The purpose of this work was to study the main aspects of integrated end-to-end speech recognition and the use of modern technologies in the natural processing of agglutinative languages, including Kazakh. In this article, the study of language models was carried out using comparative, graphic, statistical and analytical-synthetic methods, which were used in combination. This paper addresses automatic speech recognition (ASR) in agglutinative languages, particularly Kazakh, through a unified neural network model that integrates both acoustic and language modeling. Employing advanced techniques like connectionist temporal classification and attention mechanisms, the study focuses on effective speech-to-text transcription for languages with complex morphologies. Transfer learning from high-resource languages helps mitigate data scarcity in languages such as Kazakh, Kyrgyz, Uzbek, Turkish, and Azerbaijani. The research assesses model performance, underscores ASR challenges, and proposes advancements for these languages. It includes a comparative analysis of phonetic and word-formation features in agglutinative Turkic languages, using statistical data. The findings aid further research in linguistics and technology for enhancing speech recognition and synthesis, contributing to voice identification and automation processes.

Infrared spectral features of the charge carriers in nickel salen polymeric complexes

A series of the polymeric complexes (PCs) of nickel(II) with N2O2 Schiff base ligands have been studied by in situ Fourier Transform Infrared (FTIR) spectroelectrochemistry. The molecular structure of the metal-salen complexes as starting materials for the polymer synthesis was modulated by various substituents in the phenolate moieties and diimine bridge. Two groups of the infrared-active vibration (IRAV) bands are obtained in the spectra depending on the oxidation level of the polymers. The assignment of the main IRAV bands was carried out and the electrochemically generated charge carriers were determined. Analysis of the spectroscopic features observed in the mid and near IR region provides information on the charge distribution within the polymer film.

Prediction and Feature Analysis of Entrapped Slag Defect on Casting Slab Based on Optimized XGBoost Model

Prediction and Feature Analysis of Entrapped Slag Defect on Casting Slab Based on Optimized XGBoost Model

Abstract PO1-07-02: Sensitive tumor detection, accurate quantification, and ER subtyping using low-pass methylome of liquid biopsy samples from patients (pts) with metastatic breast cancer (mBC)

Abstract Background The presence and amount of blood circulating tumor DNA (Tumor Content, TC) is emerging as a clinically relevant factor in many cancers, including BC. In the metastatic setting, a commonly used tumor tissue agnostic approach involves analyzing Copy Number Alterations (CNA) from low-pass whole-genome sequencing (lpWGS) by ichorCNA. However, with a lower limit of detection of 3%, it is unsuitable for low TC applications, such as monitoring disease in response to treatment. Here we report on the use of low-pass whole genome bisulfite sequencing (lpWGBS) coupled with a novel tool, METER (METhylation analyzER), to overcome current limitations of lpWGS, enabling sensitive TC detection, accurate quantification, and ER subtyping from mBC liquid biopsies. Methods METER is a computational tool to analyse TC exploiting BC-specific Differentially Methylated Sites (DMS) and Regions (DMR) in lpWGBS data. To define BC DMSs and DMRs, Rocker-meth was applied to the WGBS profiles of 30 BC tissue (BASIS dataset) and 23 healthy donor cell free DNA (cfDNA) (Fox-Fisher 2021). METER consists of three modules: 1) METER-quant, a DMS-based quantifier of TC; 2) METER-detect, a DMR-based z-score method to classify samples as TC+ (METER+) or TC- (METER-); 3) METER-subtype, to infer ER status via Robust Partial Correlation applied to ER+ vs. ER- BC DMR. We generated lpWGBS data (coverage 0.5-1X) of 135 cfDNA samples from 58 pts (45 ER+, 13 ER-, 55 (95%) up to 3 previous lines of treatment; before starting treatment (T0, n=58), after the first cycle (T1, n=25), and at progression (T2, n=22)) and 30 from healthy individuals as controls. ichorCNA using sensitive parameters (allowing normal fraction up to 0.99) was used to estimate reference TC. A leave-one-out strategy applied to the control samples was used to estimate the false discovery rate (FDR) of METER-detect classification. METER+/- pts were tested for association with OS and PFS using the log-rank test and compared with ichorCNA detection (TC by ichorCNA above or below 3%, ichorCNA+/-). Results TC of 105 cfDNA BC samples by METER-quant was concordant with estimates from the state-of-the-art tool ichorCNA (R >0.90, p< 1e-10). In 22 pts with complete longitudinal data, METER-quant median TC of 0.05 at T0 (IQR 0.02-0.12), 0.02 at T1 (0.01-0.07), and 0.06 at T2 (0.03-0.11) were obtained, with significant difference observed for T0 vs T1 and T2 vs T1 (paired Wilcoxon p< 0.05). Across all time points, METER-detect classified 43% of the n=42 ichorCNA- samples as METER+. The reliability and prognostic performance of METER and ichorCNA detection classification were then compared using clinical outcome data. Based on T0 data, METER+ pts had significantly worse OS than METER- (HR=4.2 CI=2.0-8.9, p< 0.001). This effect was stronger than using ichorCNA (HR=2.3 CI=1.3-4.0, p=0.006). Worse PFS at T0 was observed for METER+ compared with METER- pts (HR=3.7 CI=1.8-7.9, p< 0.001), while no significant association was observed for ichorCNA+ pts (HR=1.4 CI=0.8-2.5, p=0.2). Of note, ichorCNA-/METER+ pts (N=11) at T0 had worse PFS compared with ichorCNA-/METER- (N=11) (HR=3.7 CI 1.4-9.7, p=0.005). Considering the 73 METER+ and 53 samples with METER-quant >5%, METER-subtype showed an accuracy of 0.84 (CI=0.73-0.92) and 0.94 (CI=0.83-0.99) respectively in classifying ER status based on IHC. Discussion lpWGBS enables tumor tissue agnostic analysis and concurrent investigation of complementary molecular features, including CNA and DNA methylation patterns. In a small and heterogeneous cohort, METER showed comparable performance to state-of-the-art tools in terms of TC quantification (ichorCNA) and ER subtyping (IHC), while offering enhanced sensitivity and ensuring a low FDR. METER showed promising prognostic stratification capabilities, particularly for patients with low TC. Validation in an extended prospective cohort is currently ongoing. Citation Format: Marta Paoli, Agostina Nardone, Francesca Galardi, Chiara Biagioni, Dario Romagnoli, Francesca De Luca, Gian Marco Franceschini, Ilenia Migliaccio, Marta Pestrin, Giuseppina Sanna, Emanuela Risi, Luca Livraghi, Erica Moretti, Laura Biganzoli, Luca Malorni, Francesca Demichelis, Matteo Benelli. Sensitive tumor detection, accurate quantification, and ER subtyping using low-pass methylome of liquid biopsy samples from patients (pts) with metastatic breast cancer (mBC) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-02.

Increased co-expression of 4-1BB with PD-1 on CD8+ tumor-infiltrating lymphocytes is associated with improved prognosis and immunotherapy response in cervical cancer

BackgroundThe combination of agonistic antibodies with immune checkpoint inhibitors presents a promising avenue for cancer immunotherapy. Our objective is to explore the co-expression of 4-1BB, ICOS, CD28, with PD-1 on CD8+ T cells in the peripheral blood and tumor tissue of cervical cancer(CC) patients, with a specific focus on the association between the co-expression levels of 4-1BB with PD-1 and clinical features, prognosis as well as immunotherapy response. The goal is to offer valuable insights into cervical cancer immunotherapy.MethodsIn this study, 50 treatment-naive patients diagnosed with CC were enrolled. Flow cytometry was used to detect PD-1/4-1BB, PD-1/ICOS and PD-1/CD28 co-expression on CD8+ T cells. Subsequent analysis aimed to investigate the differential co-expression between peripheral blood and cancer tissue, and also the correlation between co-expression and clinical features in these patients. Gene Expression Omnibus (GEO) datasets, The Cancer Genome Atlas (TCGA) cohort, The IMvigor210 cohort, The BMS038cohort and Immunophenoscores were utilized to investigate the correlation between PD-1/4-1BB and the immune microenvironment, prognosis, immunotherapy, and drug sensitivity in cervical cancer.ResultsThe co-expression levels of PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 on CD8+ tumor-infiltrating lymphocytes (TILs) were significantly higher in cervical cancer patients compared to those in peripheral blood. Clinical feature analysis reveals that on CD8+ TILs, the co-expression of PD-1/4-1BB is more closely correlated with clinical characteristics compared to PD-1/ICOS, PD-1/CD28, PD-1, and 4-1BB. Pseudo-time analysis and cell communication profiling reveal close associations between the subgroups harboring 4-1BB and PD-1. The prognosis, tumor mutation burden, immune landscape, and immunotherapy response exhibit statistically significant variations between the high and low co-expression groups of PD-1/4-1BB. The high co-expression group of PD-1/4-1BB is more likely to benefit from immunotherapy.ConclusionPD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 exhibit elevated co-expression on CD8+TILs of cervical cancer, while demonstrating lower expression in circulating T cells. The co-expression patterns of PD-1/4-1BB significantly contributed to the prediction of immune cell infiltration characteristics, prognosis, and tailored immunotherapy tactics. PD-1/4-1BB exhibits potential as a target for combination immunotherapy in cervical cancer.