Field Of Medical Research Research Articles

Research ethics implications in the development of personalised therapy for pediatric SIRS patients

Abstract Background Pediatric Systemic Inflammatory Response Syndrome (SIRS) presents diagnostic and therapeutic challenge that can be addressed through personalised therapy. Related ethical challenges are understudied. Our report aims at identification and analysis of research ethics implications within the bigger ERA PerMed funded project of development of tailored immunotherapy for paediatric SIRS patients (TIPS) coordinated by Prof. Catharina Schütz from Technic University Dresden. Methods A sample of 10 experts with different backgrounds was studied through semi-structured interviews. Systematic literature review grounded the interview questionnaire. The transcripts were subjected to thematic analysis by two independent researchers. Results Five classical research ethics issues were identified: respect for autonomy; informed consent; risk-benefits ratio; data protection; social justice. All experts favoured the empowerment of children (N = 10) and obtainment of informed consent rather than assent was seen as a common goal (N = 5). The emergency context of SIRS research impeded the informed consent. Despite favourable risk-benefits ratio, no direct benefits resulted to study participants. Data anonymisation though necessary (N = 10), hinders proper dealing with incidental findings. Social justice issues apparently accompanied research design in personalised medicine (N = 2) but should not extend to access to personalised therapy. Conclusions The classical framework of research ethics is challenged by the new fields of medical research. Traditional concepts, such as informed consent in minors, are subject to rethinking. Current ethical and legal guidelines fall short of proper practical instructions in the specific case of SIRS pediatric patients. Thus, ethical aspects of new technology development should always be studied in parallel to the biometrics within personalised medicine projects so as to the development of guidelines is more evidence-based than precautionary driven. Key messages • Current ethical and legal guidelines fall short of proper practical instructions in the specific case of SIRS pediatric patients. • Ethical aspects of new technology development should always be studied within personalised medicine projects so as to the development of guidelines is more evidence-based than precautionary driven.

A point cloud segmentation algorithm based on multi-feature training and weighted random forest

Abstract Point cloud segmentation is the process of dividing point cloud data into a series of coherent subsets according to its attributes. It has been widely used in target recognition, digital protection of cultural relics, medical research and other fields. To improve the classification accuracy of point cloud and achieve accurate segmentation of objects or scenes, a point cloud segmentation algorithm based on multi–features training and weighted random forest (RF) is proposed. Firstly, the feature vector composed of 3D coordinate value, RGB value, echo intensity, point cloud density, normal direction and average curvature is used to train the SVM classifier, and the ‘one–to–one’ strategy is adopted to achieve the initial multivariate rough segmentation of point cloud. Then, the maximum information coefficient and sample correlation coefficient (SCC) are used to evaluate the correlation of the decision tree, and the decision tree is weighted accordingly to build a weak correlation weighted RF, so as to achieve further accurate segmentation of the point cloud. The experiment verifies the effectiveness of the proposed algorithm by segmenting the outdoor scene point cloud data model. The results show that the segmentation algorithm based on multi–features training and weighted RF can achieve accurate point cloud segmentation, and is an effective point cloud segmentation method.

Contributions to a Theoretical Framework for Evaluating the Supply–Demand Matching of Medical Care Facilities in Mega-Cities: Incorporating Location, Scale, and Quality Factors

The rapid urbanization and population growth in mega-cities have led to a significant increase in the demand for medical services, highlighting the critical need for a more efficient alignment between the supply and demand of medical resources. Previous research often focuses on singular factors, such as accessibility or quantity, as the primary criteria for matching medical services, without comprehensively considering the location, scale, and quality factors of medical facilities. Addressing this gap, this study develops a theoretical framework that integrates these three critical factors to assess the supply–demand matching (SDM) of medical care facilities (MCFs) with population needs. This assessment is conducted using geospatial analysis techniques with ArcGIS and Python. The study includes an empirical analysis of 134 streets within the Chongqing municipality. The empirical results reveal significant disparities in the performance of integrated medical care facilities (MCFs), as well as variations across the dimensions of location, scale, and quality. Central districts like Yuzhong demonstrate high levels of accessibility, appropriate scale matching, and satisfactory service quality, whereas rapidly urbanizing peripheral districts such as Yubei suffer from significant mismatches in resource availability and service quality. The theoretical framework contributes to the field of medical care research, and the corresponding empirical findings provide valuable insights for urban planners and policymakers to optimize the allocation of medical resources, improve healthcare accessibility, and enhance service quality across different urban areas.

An optical fiber high sensitivity temperature sensor with MZI and FPI parallel connection

Accurate detection of temperature is of great significance in the field of medical research. The focal point of this article centers around an optical fiber sensor that integrates Fabry Perot interferometer (FPI) filled with polydimethylsiloxane (PDMS) and Mach Zehnder interferometer (MZI), leveraging the principle of the vernier effect. By introducing the vernier effect principle, the sensitivity is 31.09 nm/°C in the detection range of 36 ∼ 40°C, and the maximum temperature measurement error is about 0.55 %. In addition, good linear response and longtime stability make it suitable for application in various scenarios where accurate temperature measurement is required.

Integration of spectral computed tomography in arterial imaging.

Artery imaging, a crucial component in the diagnosis and management of vascular disease, assumes a significant role in the field of medical research. Utilizing advanced imaging modalities such as computed tomography (CT) and magnetic resonance angiography (MRA), artery imaging provides detailed insights into blood flow, detecting conditions such as atherosclerosis, aneurysms, and other vascular abnormalities with high precision. Spectral CT is an innovative imaging technique that goes beyond traditional CT scans, capturing data at multiple energy levels to provide detailed insights into the composition of tissues and materials within the scanned region. Notably, recent studies and clinical applications have underscored the diagnostic advantages offered by spectral CT in the detection and characterization of various arterial diseases. The integration of spectral CT into the realm of arterial imaging presents a promising avenue for improving the diagnosis and management of vascular disease. This comprehensive review aims to provide an extensive overview, delving into the potential benefits, optimization, challenges, and future of spectral CT in arterial imaging.

Future AI Will Most Likely Predict Antibody-Drug Conjugate Response in Oncology: A Review and Expert Opinion.

The medical research field has been tremendously galvanized to improve the prediction of therapy efficacy by the revolution in artificial intelligence (AI). An earnest desire to find better ways to predict the effectiveness of therapy with the use of AI has propelled the evolution of new models in which it can become more applicable in clinical settings such as breast cancer detection. However, in some instances, the U.S. Food and Drug Administration was obliged to back some previously approved inaccurate models for AI-based prognostic models because they eventually produce inaccurate prognoses for specific patients who might be at risk of heart failure. In light of instances in which the medical research community has often evolved some unrealistic expectations regarding the advances in AI and its potential use for medical purposes, implementing standard procedures for AI-based cancer models is critical. Specifically, models would have to meet some general parameters for standardization, transparency of their logistic modules, and avoidance of algorithm biases. In this review, we summarize the current knowledge about AI-based prognostic methods and describe how they may be used in the future for predicting antibody-drug conjugate efficacy in cancer patients. We also summarize the findings of recent late-phase clinical trials using these conjugates for cancer therapy.

Multi-Walled Carbon Nanotubes Accelerate Leukaemia Development in a Mouse Model.

Inflammation is associated with an increased risk of developing various cancers in both animals and humans, primarily solid tumors but also myeloproliferative neoplasms (MPNs), myelodysplastic syndromes (MDS), and acute myeloid leukemia (AML). Multi-walled carbon nanotubes (MWCNTs), a type of carbon nanotubes (CNTs) increasingly used in medical research and other fields, are leading to a rising human exposure. Our study demonstrated that exposing mice to MWCNTs accelerated the progression of spontaneous MOL4070LTR virus-induced leukemia. Additionally, similar exposures elevated pro-inflammatory cytokines such as interleukin (IL)-1β, IL-6, and tumor necrosis factor (TNF)-α and induced reactive oxygen species (ROS) in a murine macrophage cell line. These effects were significantly reduced in immunodeficient mice and when mice were treated with methoxypolyethylene glycol amine (PEG)-modified MWCNTs. These findings underscore the necessity of evaluating the safety of MWCNTs, particularly for those with hematologic cancers.

Data Standardization in the Medical Field Through FHIR and FAIR Implementation: A Systematic Review.

The authors investigate in this paper the current situation of the FHIR resources adoption in order to FAIRify data in the medical research field. By aligning with the FAIR data principles, data becomes easier to share and reuse. This review aims to analyze how integrating the FHIR resources improved the findability, accessibility, interoperability, and reusability of datasets. By searching for the state-of-art situation in this field, we want to emphasize the significant role that FAIR data occupies in the medical research community, by also providing directions for further development and improved interoperability.

Evaluating Literature Reviews Conducted by Humans Versus ChatGPT: Comparative Study.

With the rapid evolution of artificial intelligence (AI), particularly large language models (LLMs) such as ChatGPT-4 (OpenAI), there is an increasing interest in their potential to assist in scholarly tasks, including conducting literature reviews. However, the efficacy of AI-generated reviews compared with traditional human-led approaches remains underexplored. This study aims to compare the quality of literature reviews conducted by the ChatGPT-4 model with those conducted by human researchers, focusing on the relational dynamics between physicians and patients. We included 2 literature reviews in the study on the same topic, namely, exploring factors affecting relational dynamics between physicians and patients in medicolegal contexts. One review used GPT-4, last updated in September 2021, and the other was conducted by human researchers. The human review involved a comprehensive literature search using medical subject headings and keywords in Ovid MEDLINE, followed by a thematic analysis of the literature to synthesize information from selected articles. The AI-generated review used a new prompt engineering approach, using iterative and sequential prompts to generate results. Comparative analysis was based on qualitative measures such as accuracy, response time, consistency, breadth and depth of knowledge, contextual understanding, and transparency. GPT-4 produced an extensive list of relational factors rapidly. The AI model demonstrated an impressive breadth of knowledge but exhibited limitations in in-depth and contextual understanding, occasionally producing irrelevant or incorrect information. In comparison, human researchers provided a more nuanced and contextually relevant review. The comparative analysis assessed the reviews based on criteria including accuracy, response time, consistency, breadth and depth of knowledge, contextual understanding, and transparency. While GPT-4 showed advantages in response time and breadth of knowledge, human-led reviews excelled in accuracy, depth of knowledge, and contextual understanding. The study suggests that GPT-4, with structured prompt engineering, can be a valuable tool for conducting preliminary literature reviews by providing a broad overview of topics quickly. However, its limitations necessitate careful expert evaluation and refinement, making it an assistant rather than a substitute for human expertise in comprehensive literature reviews. Moreover, this research highlights the potential and limitations of using AI tools like GPT-4 in academic research, particularly in the fields of health services and medical research. It underscores the necessity of combining AI's rapid information retrieval capabilities with human expertise for more accurate and contextually rich scholarly outputs.

The Use of Photodynamic Therapy in the Treatment of Endometrial Cancer-A Review of the Literature.

Endometrial cancer is the most common malignant tumor of the female reproductive system. It develops in the mucous membrane lining the inside of the uterine body-the endometrium, through the abnormal and continuous growth of cancer cells originating from the uterine mucosa. In recent years, there has been a significant increase in the number of cases in European countries. Photodynamic therapy (PDT) is an innovative and dynamically developing medical procedure, useful in the treatment of cancer and non-cancer tissue conditions. The PDT reaction involves the activation of a photosensitizing substance with visible light, which in turn leads to the formation of free oxygen radicals, which contribute to the destruction of the cell. PDT is minimally invasive, has few side effects, and preserves organ anatomy and function. Both diagnostics and photodynamic therapy as modern methods of treatment are becoming more and more popular in many research units around the world. They are most often practiced and tested in in vitro experimental conditions. In clinical practice, the use of PDT is rare. Comprehensive cooperation between scientists contributes to taking steps towards obtaining new, synthetic photosensitizers, directing their physicochemical properties, and showing the impact on a given organism. This review examines the evidence for the potential and usefulness of PDT in the treatment of endometrial cancer. This review highlights that PDT is gaining popularity and is becoming a promising field of medical research.

Misconducts in research and methods to uphold research integrity.

Research misconduct refers to deliberate or accidental manipulation or misrepresentation of research data, findings, or processes. It can take many forms, such as fabricating data, plagiarism, or failing to disclose conflicts of interest. Data falsification is a serious problem in the field of medical research, as it can lead to the promotion of false or misleading information. Researchers might engage in p-hacking - the practice of using someone else's research results or ideas without giving them proper attribution. Conflict of interest (COI) occurs when an individual's personal, financial, or professional interests could potentially influence their judgment or actions in relation to their research. Nondisclosure of COI can be considered research misconduct and can damage the reputation of the authors and institutions. Hypothesis after results are known can lead to the promotion of false or misleading information. Cherry-picking data is the practice of focusing attention on certain data points or results that support a particular hypothesis, while ignoring or downplaying results that do not. Researchers should be transparent about their methods and report their findings honestly and accurately. Research institutions should have clear and stringent policies in place to address scientific misconduct. This knowledge must become widespread, so that researchers and readers understand what approaches to statistical analysis and reporting amount to scientific misconduct. It is imperative that readers and researchers alike are aware of the methods of statistical analysis and reporting that constitute scientific misconduct.

Navigating Bias When Using AI in Oncology

OPTIMISING patient care and survival with AI is a rapidly advancing field of medical research. During the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting, a symposium titled “Using ‘Artificial’ Intelligence to Achieve ‘Real’ Improvements in Cancer Care” explored the use of innovative AI algorithms in oncology. Experts presented breakthroughs in diagnostic and predictive AI, whilst increasing awareness of inherent bias in AI models that can perpetuate into clinical outcomes.

CRISPR Based Gene Knock Out Study in Western Clawed Frog

The advancement of biotechnology has contributed a revolutionary change in field of gene editing for the medical research and industrial biotechnology. There are different types of technology developed in recent era but the contributions of CRISPR/Cas 9(Clustered regularly interspaced short palindromic repeats/CRISPR-associated nuclease 9) has the major role for the advancement of gene editing[1]. Anciently it was known as acquired immunity that protects the bacteria from plasmid infection. It switches the bacterial immune system to eliminate the odd genetic material[2]. The technology has enormous potential to edit the genomes very accurate and efficient manner. It also provides various applications in the field of medical research. There are several elements involved in the CRISPR process involved in the process of gene editing. The developments of the process increased its potential for various model organisms. The major two components of the process are generally the sgRNA(Synthetic Single guide RNA) and the Cas 9[3]. The sgRNA is associated with genome specific side targeting and the Cas 9 has the ability to form the side specific DNA cleavage. There are several advantages of this technology over the disease like cystic fibrosis, sickle cell anemia, hemophilia and single nucleotide based genetic disorders[4]

Salivary Diagnostics in Oral and Systemic Diseases - A Review

Salivary diagnostics, an emerging field in medical research, leverages the analysis of saliva for non- invasive detection, monitoring, and management of various diseases. This abstract explores the potential of saliva as a diagnostic fluid, highlighting its advantages such as ease of collection, cost-effectiveness, and patient acceptability compared to traditional blood or tissue sampling. Key biomarkers identified in saliva include proteins, nucleic acids, and metabolites that reflect systemic health conditions, oral diseases, infectious agents, and even systemic diseases like cancer and diabetes. Advances in technology, such as biosensors and genomic analysis, enhance the sensitivity and specificity of salivary diagnostics, paving the way for personalized medicine and early intervention strategies. Challenges including standardization, variability in saliva composition, and regulatory approval are also discussed, underscoring the need for further research and clinical validation. Salivary diagnostics holds promise as a valuable tool in future healthcare, potentially transforming how diseases are diagnosed and managed globally.

The Impact of the Dermal Matrix in Tissue Reconstruction: A Bibliometric Perspective in Plastic Surgery.

In the vast field of medical scientific research, few topics have managed to attract as much attention and mobilise academic resources as the use of dermal matrices in the reconstruction of soft tissue defects. In this study, we used bibliographic metrics such as co-authorship, keyword co-occurrence, and citations per document to analyse the relationship between the use of dermal matrices to reconstruct soft tissue defects caused by burns, tumours, and trauma. In addition, keyword analysis has highlighted the crucial role of technology in recent studies and the innovation brought about by the use of dermal matrices in the reconstruction of soft tissue defects. Keywords used in recent studies have revealed the critical role of technology in the development of the field. We extracted a set of 1329 research papers from the Web of Science Core Collection database between 2010 and 2024 that met our criteria. Through keyword analysis, we identified technology as a significant factor in recent studies. Our results showed that there is very little collaboration between authors on the topic and that most of them are from Asia. A significant number of articles on this topic come from the USA, China, Japan, Germany, the UK, and France. We discovered the top ten most cited sources analysing the use of dermal matrices in the reconstruction of soft tissue defects. Finally, we think that this study will be beneficial for our further research.

Roles of Hydrogen Sulfide (H2S) as a Potential Therapeutic Agent in Cardiovascular Diseases: A Narrative Review.

Cardiovascular disease (CVD) stands as one of the leading causes of morbidity and mortality worldwide, and the continued search for novel therapeutics is vital for addressing this global health challenge. Over the past decade, hydrogen sulfide (H₂S) has garnered significant attention in the field of medical research, as it has been proven to be a cardioprotective gaseous signaling molecule. It joins nitric oxide and carbon monoxide as endogenously produced gasotransmitters. As for its mechanism, H₂S functions through the posttranslational addition of a sulfur group to cysteine residues on target proteins in a process called sulfhydration. As a result, the observed physiological effects of H₂S can include vasodilation, anti-apoptosis, anti-inflammation, antioxidant effects, and regulation of ion channels. Various studies have observed the cardioprotective benefits of H₂S in diseases such as myocardial infarction, ischemia-reperfusion injury, cardiac remodeling, heart failure, arrhythmia, and atherosclerosis. In this review, we discuss the mechanisms and therapeutic potential of H₂S in various CVDs.

Potential Roles of Large Language Models in the Production of Systematic Reviews and Meta-Analyses.

Large language models (LLMs) such as ChatGPT have become widely applied in the field of medical research. In the process of conducting systematic reviews, similar tools can be used to expedite various steps, including defining clinical questions, performing the literature search, document screening, information extraction, and language refinement, thereby conserving resources and enhancing efficiency. However, when using LLMs, attention should be paid to transparent reporting, distinguishing between genuine and false content, and avoiding academic misconduct. In this viewpoint, we highlight the potential roles of LLMs in the creation of systematic reviews and meta-analyses, elucidating their advantages, limitations, and future research directions, aiming to provide insights and guidance for authors planning systematic reviews and meta-analyses.

Synthesis, Anticancer Activity, and Molecular Docking of New 1,2,3-Triazole Linked Tetrahydrocurcumin Derivatives.

Cancer is one of the deadliest diseases to humanity. There is significant progress in treating this disease, but developing some drugs that can fight this disease remains a challenge in the field of medical research. Thirteen new 1,2,3-triazole linked tetrahydrocurcumin derivatives were synthesized by click reaction, including a 1,3-dipolar cycloaddition reaction of tetrahydrocurcumin baring mono-alkyne with azides in good yields, and their in vitro anticancer activity against four cancer cell lines, including human cervical carcinoma (HeLa), human lung adenocarcinoma (A549), human hepatoma carcinoma (HepG2), and human colon carcinoma (HCT-116) were investigated using MTT(3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyltetraz-olium bromide) assay. The newly synthesized compounds had their structures identified using NMR HRMS and IR techniques. Some of prepared compounds, including compounds 4g and 4k, showed potent cytotoxic activity against four cancer cell lines compared to the positive control of cisplatin and tetrahydrocurcumin. Compound 4g exhibited anticancer activity with a IC50 value of 1.09 ± 0.17 μM against human colon carcinoma HCT-116 and 45.16 ± 0.92 μM against A549 cell lines compared to the positive controls of tetrahydrocurcumin and cisplatin. Moreover, further biological examination in HCT-116 cells showed that compound 4g can arrest the cell cycle at the G1 phase. A docking study revealed that the potential mechanism by which 4g exerts its anti-colon cancer effect may be through inhabiting the binding of APC-Asef. Compound 4g can be used as a promising lead for further exploration of potential anticancer agents.

Establishment and Application of a Novel Genetic Detection Panel for SNPs in Mongolian Gerbils.

The Mongolian gerbil is a distinctive experimental animal in China, as its genetic qualities possess significant value in the field of medical biology research. Here, we aimed to establish an economical and efficient panel for genetic quality detection in Mongolian gerbils using single-nucleotide polymorphism (SNP) markers. To search for SNPs, we conducted whole-genome sequencing (WGS) in 40 Mongolian gerbils from outbred populations. Reliable screening criteria were established to preliminarily select SNPs with a wide genome distribution and high levels of polymorphism. Subsequently, a multiple-target regional capture detection system based on second-generation sequencing was developed for SNP genotyping. Based on the results of WGS, 219 SNPs were preliminarily selected, and they were established and optimized in a multiple-amplification system that included 206 SNP loci by genotyping three outbred populations. PopGen.32 analysis revealed that the average effective allele number, Shannon index, observed heterozygosity, expected heterozygosity, average heterozygosity, polymorphism information content, and other population genetic parameters of the Capital Medical University (CMU) gerbils were the highest, followed by those of Zhejiang gerbils and Dalian gerbils. Through scientific screening and optimization, we successfully established a novel, robust, and cost-effective genetic detection system for Mongolian gerbils by utilizing SNP markers for the first time.

New diagnostic technologies in laboratory medicine: potential benefits and challenges.

Laboratory tests play a central role in medicine, as they help to make diagnoses, assess prognosis and risk of disease, and monitor therapies, thus contributing to 70% of all medical decisions. This cross‑sectional function offers great potential for technologic and organizational innovation to influence health care as a whole. In recent years, a variety of technologies have emerged and entered the field of medical research, or even medical care. A new generation of biosensors enables laboratory tests to be carried out at the point of care and allows for faster medical decisions. Modern devices allow for patient‑centric blood sampling, which eliminates the need for painful blood draws, patient traveling, and limits the workload of health care professionals. Analytical techniques, such as metabolomics, lipidomics, or proteomics can identify biomarkers extremely sensitively, even down to individual cells. Pharmacogenomics allows for determination of genetic polymorphisms that predict a response to chemotherapeutic agents. Machine‑learning approaches can handle large amounts of multilayered data for diagnostic applications. However, this enormous diagnostic potential is far from being utilized and only very few applications have been implemented in clinical practice. Why is this the case? In this article, we describe the key technologic fields, discuss their medical potential, and list obstacles to their implementation. In addition, we present a methodologic framework to support researchers, clinicians, and authorities in development and implementation of novel diagnostic approaches.