Quantitative Analysis Research Articles

Proteomic characterization of MET-amplified esophageal adenocarcinomas reveals enrichment of alternative splicing- and androgen signaling-related proteins

BackgroundEsophageal adenocarcinomas (EACs) represent an evolving tumor entity with high mortality rates. MET amplification is a recurrent driver in EACs and is associated with decreased patient survival. However, the response to MET inhibitors is limited. Recent studies have identified several mechanisms that lead to resistance against MET inhibitors in different tumor entities. Nonetheless, a characterization of additional vulnerable targets beyond MET has not been conducted in MET-amplified EACs.MethodsIn this study, we determined the MET amplification status in a cohort of more than 900 EACs using fluorescence in situ hybridization (FISH) and compared the proteomes of MET-amplified (n = 20) versus non-amplified tumors (n = 39) by mass spectrometry.ResultsWe identified a phenotype, present in almost all MET-amplified tumors, which shows an enrichment of alternative RNA splicing, and androgen receptor signaling proteins, as well as decreased patient survival. Additionally, our analyses revealed a negative correlation between MET expression and patient survival in MET-amplified EACs, indicating biological heterogeneity with clinical relevance despite the presence of MET amplification as the predominant oncogenic driver. Furthermore, quantitative immunohistochemical analysis of the inflammatory tumor microenvironment showed that an increased percentage of M2 macrophages is associated with lower overall survival in MET-amplified EACs.ConclusionsOur results provide valuable insights into possible new therapeutic approaches for MET-amplified EACs for further research.

Reconceptualisation of sport and quality of life in young athletes following anterior cruciate ligament reconstruction: understanding the experiences behind the numbers through a prospective mixed-methods study

To explore the theory of response shift in young athletes undergoing anterior cruciate ligament (ACL) reconstruction (ACLR) by investigating athletes’ conceptualisation of quality of life (QOL) across time following ACL...

Comprehensive study of the microplastic footprint in the urban pond and river of Eastern India

Freshwater ecosystems, comprising lakes, ponds, rivers, and groundwater, provides essential resources that support life on Earth. Though, during last few decades, the increasing concentrations of microplastics (MPs) in freshwater ecosystems are becoming a serious environmental concern with far-reaching and unpredictable consequences for human health and aquatic life. This study aimed to quantify and assess the dynamic footprint of MPs in the urban river and pond with their potential risks. The quantitative analyses revealed the presence of MPs in surface water and sediment of study sites between the range of 59–100 particles/l and 167–193 particles/g, respectively. According to the size distribution, 56.98% of the MPs were between 300 and 1180 μm, and 43.02% were between 1180 and 5000 μm. Further polymer characterization confirmed the presence of nylon (36%) as the dominant type, followed by polyethylene (25%), polyethylene terephthalate (18%), polyvinyl chloride (9%), polyurethane (5%), polypropylene (5%), and polystyrene (2%) in the available MPs. The film (39.07%) shaped MPs were dominant throughout the samples, followed by fragments (22.2%), particles (12.63%), fibre (9.73%), pellets (9.3%), foam (1.7%), and others. Following the risk analyses, the polymer hazard index and polymer load index values were above the crisis level (V and IV level), and potential ecological risk index for river water and pond surface water were extremely danger and danger levels.

Synthesizing Genotoxicity Results in the MultiFlow Assay With Point-of-Departure Analysis and ToxPi Visualization Techniques.

In vitro genotoxicity has historically served a hazard identification role, with simple binary outcomes provided for each of several single endpoint assays. This will need to change, given: (i) efforts to curtail animal testing, (ii) the increased use of multiplexed invitro assays and the ongoing development of NAMS, and (iii) the desire to holistically consider quantitative results from multiple biomarkers/endpoints that take potency into consideration. To help facilitate more quantitative analyses of multiple biomarkers and/or assay streams, we explored the combined use of PROAST and Toxicological Prioritization Index (ToxPi) software. As a proofofconcept, this investigation employed the MultiFlow DNA damage assay, focusing on γH2AX and p53 biomarkers at two time points, whereby 10 genotoxicants were evaluated in the presence and absence of rat liver S9 metabolic activation. Whereas PROAST was used to calculate BMD point estimates and confidence intervals (CIs), ToxPi synthesized the BMD results into visual, quantitative summaries conveying genotoxicity and metabolic properties. Our analyses suggest that ToxPi's data synthesis and visualization modules provide useful insights into compound response, chemical grouping, and genotoxic mechanisms. By integrating multiple data sources, we find that ToxPi offers a powerful complementary approach to traditional BMD CI graphs, particularly for the simultaneous analysis of multiple biomarkers, enhancing chemical potency analysis of complex datasets.

Dynamics of Microsphere Inclusions within Biomimetic Membranes Reveal Membrane Heterogeneity.

Extracellular macromolecules and particles often bind and diffuse over cell membranes during transport processes like endocytosis, exocytosis, drug imbibition, etc. However, imaging the real-time dynamics of these phenomena at nanometer scale resolution is highly challenging. Here, we use a model system of polystyrene microspheres diffusing over the surface of colloidal membranes as 3 orders of magnitude scaled up analogue of this process. Colloidal membranes are freely fluctuating two-dimensional smectic A monolayers of one micrometer long rod-shaped viruses that bind to polystyrene microspheres through the generic mechanism of depletion attraction. We find that the microsphere causes local deformation in the membrane, behaving as an inclusion that diffuses freely in the bulk of the membrane but surprisingly gets radially trapped near the edge of the membrane. We identify the critical particle size and membrane composition required to observe trapping of particles at the membrane edge. A quantitative analysis of the motion of the particle in the bulk enabled us to determine the local membrane interfacial viscosity of the colloidal membranes. Overall, our study shows that the complex interplay of membrane fluctuations and particle characteristics can lead to a rich phenomenology even in highly simplified few component model systems.

Interferometric Analysis of Femtosecond Laser-Generated Plasma Expansion from Solid Targets

The paper presents the preliminary results of interferometric studies on plasma expansion induced by femtosecond laser pulses interacting with solid aluminum targets. These interactions offer great potential for the generation of energetic particle streams (electrons and ions) commonly known as laser ion (electron) acceleration, which are of particular interest in research on inertial confinement fusion, modeling of astrophysical phenomena, and other applications. Interferometry is an excellent tool for visualizing processes of plasma stream expansion and provides information about the spatial-temporal distributions of these streams with high spatial and temporal resolution. However, there is a limited body of research that focuses on interferometric measurements of plasma generated by femtosecond laser pulses on solid targets, making this study a significant contribution to the field. The presented research was conducted at the High Power Laser Laboratory, located in the Institute of Plasma Physics and Laser Microfusion, using a femtosecond laser system with an energy of approximately 280 mJ (after compression, on target) and a pulse duration (full-width at half-maximum) of ~260 fs. The studies involved flat aluminum solid targets and foils, illuminated by a laser pulse with different spot sizes depending on the target’s position relative to the focal point. A one-frame interferometer with a tunable delay line was used to capture images at various stages of plasma expansion. These studies clearly demonstrate the significant impact of spot size on the character of plasma expansion, with spherical expansion dominating for small spot sizes and axial expansion dominating for larger spot sizes. A substantial part of the paper is devoted to the description of the quantitative analysis of the obtained data.

Real-Time Mapping of Polymer Film Thickness Using Near-Infrared Hyperspectral Imaging.

A new method based on near-infrared (NIR) hyperspectral imaging (HSI) has been developed for online polymer film thickness mapping. Traditional online methods, including X-ray, capacitance, and physical gauging (micrometers), can only determine film thickness for a point with each measurement. The NIR-HIS method allows the determination of film thickness for a line based on each image, thus enabling true real-time two-dimensional (2D) mapping of film thickness as the film translates in front of the instrument. A Specim NIR camera, 1000-2500 nm, 384 (spatial) × 288 (spatial) pixels, was used in this study for various low-density polyethylene (LDPE), and high-density polyethylene (HDPE) films. Sample thickness between μm to mm can be mapped based on the myriad NIR absorbance bands with various molar absorptivity. The 2310 nm NIR peak was found to be the most effective feature for determining film thickness over the range of polyethylene film studied in this project: 10∼100 μm. A good correlation was found between the 2310 nm absorbance and the incumbent X-ray thickness scanner results. Interference fringes were found to be a potential source of error for quantitative analysis of thin films, and a classical least squares (CLS) analysis was found to be effective in removing fringes. This method was implemented to map out film thickness in real-time in an industrial blown film process.

Quantitative Analysis of Risk Coupling Effects in Highway Accidents: A Focus on Primary and Secondary Accidents

Quantitative Analysis of Risk Coupling Effects in Highway Accidents: A Focus on Primary and Secondary Accidents

Tumor Dose-Response Relationship of [131I]MIBG Therapy in Patients with Neural Crest Tumors by Means of [124I]MIBG PET.

[131I]Metaiodobenzylguanidine (MIBG) therapy in patients with neural crest tumors has demonstrated sustained control of catecholamine-associated hypertension and corresponding partial response. Details on how neural crest tumors respond to an absorbed dose delivered by [131I]MIBG-targeted therapies is insufficiently known. The primary aim of this retrospective study was to assess the tumor dose-response relationship by means of quantitative analysis of [124I]MIBG PET data. Methods: The tumor dose-response relationship was studied in patients with advanced malignant pheochromocytoma, neuroblastoma, or paraganglioma receiving [131I]MIBG treatment, as well as pretherapeutic and follow-up [124I]MIBG-based dosimetry. [124I]MIBG PET imaging was performed around 4, 24, 48, and 120 h after injection. Lesion uptake was projected to [131I]MIBG for every time point, and respective time-integrated activity coefficients (TIACs) for [131I]MIBG were calculated and used for tumor-absorbed dose estimation. Functional response was denoted for decrease of maximal lesion uptake or TIAC by at least 30% in the follow-up examination. In a consecutive analysis, the predictive value of a single tumor-uptake assessment from PET imaging at 24 h after administration was investigated with respect to receiving the derived target dose. Results: In total, 46 lesions from 9 patients were available for dose-response analysis. The mean ± SD tumor-absorbed dose coefficient was 13.4 ± 15.4 Gy/GBq (median, 7.2 Gy/GBq; range, 1.1-64.7 Gy/GBq). A high correlation (-0.60, P < 0.001) was found between uptake decrease and tumor dose. In addition, a very high correlation (0.91, P < 0.001) was found between uptake and TIAC decrease. The estimated targeted tumor dose was 200 Gy, that is, the dose at which the response rate exceeded the 90% threshold. A single 24-h uptake assessment showed predictive value with respect to receiving the target dose. Conclusion: This study demonstrated a clear correlation between tumor-absorbed dose and functional response in [131I]MIBG therapy and proposes a target dose for response at the tumor level.

Osteopontin as a prognostic biomarker in head and neck cancer- a systematic review and meta-analysis.

Osteopontin (OPN) is linked to cancer progression, metastasis, and treatment resistance in head and neck cancer (HNC). This meta-analysis evaluated OPN as a prognostic biomarker in HNC. A comprehensive search was conducted for studies published up to December 2023, including English papers on HNC analyzing OPN expression. Data extraction, quality assessment, and quantitative analysis were performed using fixed and random effect models with 95% CI. Heterogeneity and publication bias were assessed with I2 and Egger's regression test. Fifty-one studies were included. OPN expression was significantly elevated in tissue and plasma in HNC compared to control (SMD 0.98; 95% CI 0.47-1.49; I2 = 13%; p < 0.00). High plasma OPN predicted poor survival (HR: 2.00; I2 = 64%; P = 0.03), as did high tissue OPN (hazard ratio: 2.71, 95% confidence interval = 1.51-4.87; I2 = 49.4%, p > 0.05). Elevated plasma OPN correlated with smoking, poorly differentiated neoplasms, larger tumors, advanced stage, and lymph node metastasis. Positive tissue OPN was associated with nodal involvement, advanced stage, male gender, and smoking. OPN is a robust prognostic biomarker in HNC, indicating tumor aggressiveness and poor prognostic outcomes. Standardized measurement protocols and further validation in prospective studies are necessary.

Criteria and prognostic factors for survival and success rates for autotransplantation in the area of first and second molars.

Autotransplantation of immature third molars is an effective treatment option, offering high success rates, minimal complications, and notable improvements in occlusal function and dental aesthetics. This study aims to review and evaluate the available evidence specifically on the survival and success rates of immature autotransplanted third molars and to identify prognostic factors that influence these outcomes. A comprehensive search was conducted in the Elsevier Journals (ScienceDirect), MEDLINE (PubMed), and Dentistry & Oral Sciences Source (EBSCOhost) databases up to May 2024. Risk of bias within studies was asse ssed with the CASP guidelines. Nine studies met the inclusion criteria for qualitative and quantitative analysis. The pooled survival rate was consistently high across studies, estimated at 91.867% (95% CI: 85.058% to 96.741%), while the success rate, though slightly lower, remained substantial at 90.162% (95% CI: 79.813% to 97.067%). Key prognostic factors identified include the developmental stage of the donor teeth (Moorrees stages from R ¼ to Rc), showing enhanced success due to better potential for pulp revascularization and continued root growth. Additionally, innovative techniques, including advanced methods like computer-aided rapid prototyping (CARP) and platelet-rich plasma (PRP), were found to positively impact healing and integration. Fixation methods also played a role, as excessive rigid fixation was linked to higher rates of ankylosis and inflammatory root resorption. Future research should prioritize recipient site and donor tooth characteristics, standardized success criteria, extended follow-up periods, and refined treatment protocols to enhance predictability and long-term outcomes in teeth autotransplantation.

How does the exogenous contamination of drowning medium affect the diatom test in the diagnosis of drowning?

ABSTRACT Diatom tests have been widely used in the diagnosis of drowning. During a diatom test, exogenous diatom contamination is difficult to avoid, usually causing a shadow over the reliability of test results. This study was designed to analyse the effect of false positive results caused by exogenous diatom contamination on the diagnosis of a drowned cadaver. Samples from both drowned and non-drowned cadavers were obtained and divided into four groups: the control (CTRL) group, low concentration of diatoms solution (LCDS) group, medium concentration of diatoms solution (MCDS) group, and the high concentration of diatoms solution (HCDS) group, and then these samples analysed by the Microwave Digestion-Vacuum Filtration-Automated Scanning Electron Microscopy (MD-VF-Auto SEM) method. The results showed that exogenous diatom contamination could cause false positive results in lung, liver and kidney tissues from a non-drowned cadaver, and the concentration of diatoms in non-drowned cadavers increased with the increase of exogenous concentration of diatom solution. Exogenous diatom contamination caused the concentration of diatom changes in liver and kidney tissues, but not in lung tissue. Depending on quantitative analysis, exogenous diatom contamination can be identified easily, and the L/D ratio is a more reliable indicator in the diagnosis of drowning.

Direct comparison of single peak and gradient chromatographic methods for routine analysis of surfactants in biopharmaceuticals.

Direct comparison of single peak and gradient chromatographic methods for routine analysis of surfactants in biopharmaceuticals.

Nano-arrayed Cu2S@MoS2 heterojunction SERS sensor for highly sensitive and visual detection of polystyrene in environmental matrices.

Nano-arrayed Cu2S@MoS2 heterojunction SERS sensor for highly sensitive and visual detection of polystyrene in environmental matrices.

The Influence of Working Capital Management, Market Risk, and Investment Strategy on Profitability With Financial Distress As an Intervening Variable (Study on Companies With Special Notations Listed on The Indonesia Stock Exchange For The 2019-2023 Perio

This study aims to examine the influence of working capital management, market risk, and investment strategy on profitability, with financial distress as an intervening variable. The research is conducted on companies with special notations listed on the Indonesia Stock Exchange (IDX) during the period of 2019-2023. Quantitative Research Methods The research uses quantitative analysis with an associative quantitative approach, which is research that asks about the relationship between two or more variables. The analysis is carried out using a quantitative approach with Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM-PLS) through Smart PLS 4.0 software. The results show that working capital management and investment strategy have a positive and significant impact on profitability, while market risk and financial distress negatively affect profitability. Furthermore, financial distress plays a significant mediating role between the independent variables and profitability. The model demonstrates a strong predictive ability with an R² value of 0.65 for profitability and 0.57 for financial distress. These findings suggest that efficient working capital management and appropriate investment strategies contribute to higher profitability, while market risks and financial distress pose a threat to the financial stability of companies.

The effectiveness of the Sierra Leone health sector’s response to COVID-19: a quantitative analysis

BackgroundThe COVID-19 pandemic posed significant challenges to health systems globally, particularly in low-resource settings like Sierra Leone. Understanding the effectiveness of leadership, health workforce performance, community engagement, and service delivery during the pandemic is critical for strengthening future pandemic, preparedness and response.MethodsA cross-sectional study was conducted with 303 respondents, including stakeholders from the Ministry of Health, district health management teams, and community health workers. Data were collected using structured questionnaires and analyzed to assess perceptions of leadership, workforce performance, community participation, and disruptions to health services.ResultsLeadership and governance were rated as “effective” or “very effective” by 58% of respondents, with key challenges including inadequate communication, delays in resource mobilization, and limited transparency. The health workforce demonstrated strong commitment (62%), but gaps in infection prevention and control training (48%) and shortages of personal protective equipment (39%) were significant barriers. Community engagement was moderately effective, with 54% rating it as “effective” or “very effective.” However, low trust in the health sector and misinformation hindered compliance with preventive measures. Maternal and child health services were the most disrupted, but innovative approaches such as telemedicine and mobile health units were adopted to mitigate service interruptions.ConclusionSierra Leone’s COVID-19 response highlighted both achievements and challenges. While leadership structures, workforce dedication, and community health worker contributions were notable strengths, gaps in communication, resource availability, and community trust limited the overall effectiveness of the response. Strengthening communication channels, investing in workforce training and resources, and enhancing community engagement strategies are critical for improving preparedness and response in future health emergencies.

Leveraging Artificial Intelligence and Clinical Laboratory Evidence to Advance Mobile Health Applications in Ophthalmology: Taking the Ocular Surface Disease as a Case Study

ABSTRACTBackgroundThe advent of mobile health (mHealth) applications has fundamentally transformed the healthcare landscape, particularly within the field of ophthalmology, by providing unprecedented opportunities for remote diagnosis, monitoring, and treatment. Ocular surface diseases, including dry eye disease (DED), are the most common eye diseases that can be detected by mHealth applications. However, most remote artificial intelligence (AI) systems for ocular surface disease detection are predominantly based on self‐reported data collected through interviews, which lack the rigor of clinical evidence. These constraints underscore the need to develop robust, evidence‐based AI frameworks that incorporate objective health indicators to improve the reliability and clinical utility of remote health applications.MethodsTwo novel deep learning (DL) models, YoloTR and YoloMBTR, were developed to detect key ocular surface indicators (OSIs), including tear meniscus height (TMH), non‐invasive Keratograph break‐up time (NIKBUT), ocular redness, lipid layer, and trichiasis. Additionally, back propagation neural networks (BPNN) and universal network for image segmentation (U‐Net) were employed for image classification and segmentation of meibomian gland images to predict Demodex mite infections. These models were trained on a large dataset from high‐resolution devices, including Keratograph 5M and various mobile platforms (Huawei, Apple, and Xiaomi).ResultsThe proposed DL models of YoloMBTR and YoloTR outperformed baseline you only look once (YOLO) models (Yolov5n, Yolov6n, and Yolov8n) across multiple performance metrics, including test average precision (AP), validation AP, and overall accuracy. These two models also exhibit superior performance compared to machine plug‐in models in KG5M when benchmarked against the gold standard. Using Python's Matplotlib for visualization and SPSS for statistical analysis, this study introduces an innovative proof‐of‐concept framework leveraging quantitative AI analysis to address critical challenges in ophthalmology. By integrating advanced DL models, the framework offers a robust approach for detecting and quantifying OSIs with a high degree of precision. This methodological advancement bridges the gap between AI‐driven diagnostics and clinical ophthalmology by translating complex ocular data into actionable insights.ConclusionsIntegrating AI with clinical laboratory data holds significant potential for advancing mobile eye health (MeHealth), particularly in detecting OSIs. This study aims to explore this integration, focusing on improving diagnostic accuracy and accessibility. This study demonstrates the potential of AI‐driven tools in ophthalmic diagnostics, paving the way for reliable, evidence‐based solutions in remote patient monitoring and continuous care. The results contribute to the foundation of AI‐powered health systems that can extend beyond ophthalmology, improving healthcare accessibility and patient outcomes across various domains.

Best Vitelliform Macular Dystrophy Natural History Study Report 2: Fundus Autofluorescence and Optical Coherence Tomography.

Best Vitelliform Macular Dystrophy Natural History Study Report 2: Fundus Autofluorescence and Optical Coherence Tomography.

Quantitative RT-PCR Analysis of Plant Growth-Promoting Rhizobacteria-Induced Transcriptional Responses in Barley (Hordeum vulgare L.) under High Salt and Drought Stress Conditions

Quantitative RT-PCR Analysis of Plant Growth-Promoting Rhizobacteria-Induced Transcriptional Responses in Barley (<i>Hordeum vulgare</i> L.) under High Salt and Drought Stress Conditions

Two-photon polymerization of miniaturized 3D scaffolds optimized for studies on glioblastoma multiforme in spaceflight-like microgravity conditions

The obtainment of innovative models recalling complex tumour architectures and activitiesin vitrois a challenging drive in the understanding of pathology molecular bases, yet it is a crucial path to the identification of targets for advanced oncotherapy. Cell environment recapitulation by 3D scaffolding and gravitational unloading of cell cultures represent powerful means in tumour biomimicry processes, but their simultaneous adoption has consistently been explored only in the latest decade. Here, an unprecedented bioengineering approach capitalizing on spaceflight biology practice is proposed for modelling of glioblastoma multiforme, a highly aggressive neoplasm that affects the central nervous system and has poorly effective pharmacological and radiological countermeasures. Tumour modelling was pursued by the original implementation of two-photon polymerization in fast prototyping of 3D scaffolds on flexible substrates for U87-MG glioma cell culture, and by the exposure of cell-laden scaffolds to simulated microgravity (s-μg). Realistic spaceflight conditions were applied to collect preliminary information suitable for testing of U87-MG cell-laden scaffold in low Earth orbit. Responses of glioma cells anchored to 3D scaffolds were investigated by microscopy, quantitative reverse transcription-polymerase chain reaction and proteomic analyses, revealing synergic regulatory effects of cell scaffolding and s-μg on markers of tumour cell growth, metabolism and invasiveness.