Significance Of Medical Research Research Articles

Brain tumor segmentation in MRI with multi-modality spatial information enhancement and boundary shape correction

Brain tumor segmentation is currently of a priori guiding significance in medical research and clinical diagnosis. Brain tumor segmentation techniques can accurately partition different tumor areas on multi-modality images captured by magnetic resonance imaging (MRI). Due to the unpredictable pathological process of brain tumor generation and growth, brain tumor images often show irregular shapes and uneven internal gray levels. Existing neural network-based segmentation methods with an encoding/decoding structure can perform image segmentation to some extent. However, they ignore issues such as differences in multi-modality information, loss of spatial information, and under-utilization of boundary information, thereby limiting the further improvement of segmentation accuracy. This paper proposes a multimodal spatial information enhancement and boundary shape correction method consisting of a modality information extraction (MIE) module, a spatial information enhancement (SIE) module, and a boundary shape correction (BSC) module. The above three modules act on the input, backbone, and loss functions of deep convolutional networks (DCNN), respectively, and compose an end-to-end 3D brain tumor segmentation model. The three proposed modules can solve the low utilization rate of effective modality information, the insufficient spatial information acquisition ability, and the improper segmentation of key boundary positions can be solved. The proposed method was validated on BraTS2017, 2018, and 2019 datasets. Comparative experimental results confirmed the effectiveness and superiority of the proposed method over state-of-the-art segmentation methods.

A comprehensive review of the literature on CD10: its function, clinical application, and prospects.

CD10, a zinc-dependent metalloprotease found on the cell surface, plays a pivotal role in an array of physiological and pathological processes including cardiovascular regulation, immune function, fetal development, pain response, oncogenesis, and aging. Recognized as a biomarker for hematopoietic and tissue stem cells, CD10 has garnered attention for its prognostic potential in the progression of leukemia and various solid tumors. Recent studies underscore its regulatory significance and therapeutic promise in combating Alzheimer's disease (AD), and it is noted for its protective role in preventing heart failure (HF), obesity, and type-2 diabetes. Furthermore, CD10/substance P interaction has also been shown to contribute to the pain signaling regulation and immunomodulation in diseases such as complex regional pain syndrome (CRPS) and osteoarthritis (OA). The emergence of COVID-19 has sparked interest in CD10's involvement in the disease's pathogenesis. Given its association with multiple disease states, CD10 is a prime therapeutic target; inhibitors targeting CD10 are now being advanced as therapeutic agents. This review compiles recent and earlier literature on CD10, elucidating its physicochemical attributes, tissue-specific expression, and molecular functions. Furthermore, it details the association of CD10 with various diseases and the clinical advancements of its inhibitors, providing a comprehensive overview of its growing significance in medical research.

Deep learning-based respiratory muscle segmentation as a potential imaging biomarker for respiratory function assessment.

Respiratory diseases significantly affect respiratory function, making them a considerable contributor to global mortality. The respiratory muscles play an important role in disease prognosis; as such, quantitative analysis of the respiratory muscles is crucial to assess the status of the respiratory system and the quality of life in patients. In this study, we aimed to develop an automated approach for the segmentation and classification of three types of respiratory muscles from computed tomography (CT) images using artificial intelligence. With a dataset of approximately 600,000 thoracic CT images from 3,200 individuals, we trained the model using the Attention U-Net architecture, optimized for detailed and focused segmentation. Subsequently, we calculated the volumes and densities from the muscle masks segmented by our model and performed correlation analysis with pulmonary function test (PFT) parameters. The segmentation models for muscle tissue and respiratory muscles obtained dice scores of 0.9823 and 0.9688, respectively. The classification model, achieving a generalized dice score of 0.9900, also demonstrated high accuracy in classifying thoracic region muscle types, as evidenced by its F1 scores: 0.9793 for the pectoralis muscle, 0.9975 for the erector spinae muscle, and 0.9839 for the intercostal muscle. In the correlation analysis, the volume of the respiratory muscles showed a strong correlation with PFT parameters, suggesting that respiratory muscle volume may serve as a potential novel biomarker for respiratory function. Although muscle density showed a weaker correlation with the PFT parameters, it has a potential significance in medical research.

Amplified Drug Delivery System with a Pair of Master Keys Triggering Precise Drug Release for Chemo-Photothermal Therapy.

A versatile drug delivery system (DDS) enabling highly effective and targeting oncotherapy has always been of great significance in medical research. In the development of a stimuli-responsive DDS, compared with a single-factor stimulation DDS, a multifactor activation DDS has higher therapeutic specificity between diseased and normal tissue, but there are challenges in drug-release efficiency and united targeting cancer therapy. Herein, a novel dual-microRNA (dual-miRNA)-mediated 1:N-amplified DDS is fabricated. The gold nanocage (AuNC) was synthesized and used as a carrier. A DNA bridge motif as a nanolock (DNA bridge nanolock) was designed and modified on the surface of AuNCs, which could seal the holes of AuNCs. Using the dual-miRNAs as a pair of master keys, through DNA strand migration and DNAzyme self-assembly, a cell endogenous substance Mg2+-dependent DNAzyme cyclic shear reaction could perform the function of the master keys to open multiple locks for the enhanced release of doxorubicin from the AuNCs. In addition, under near-infrared irradiation, via absorption of light and heat release, the AuNC is activated to perform the function of photothermal therapy. Thereby, the system achieves precise chemo-photothermal therapy. Using the in vitro and in vivo anti-tumor analysis, the DDS could be proved to present a novel design of enhanced and targeted drug-release system for highly effective cancer therapy.

High-Precise Bipolar Disorder Detection by Using Radial Basis Functions Based Neural Network

Presently, several million people suffer from major depressive and bipolar disorders. Thus, the modelling, characterization, classification, diagnosis, and analysis of such mental disorders bears great significance in medical research. Electroencephalogram records provide important information to improve clinical diagnosis and are very useful in the scientific community. In this work, electroencephalogram records and patient data from the Hospital Virgen de la Luz in Cuenca (Spain) were processed for a correct classification of bipolar disorders. This work implemented an innovative radial basis function-based neural network employing a fuzzy means algorithm. The results show that the proposed method is an effective approach for discrimination of two kinds of classes, i.e., bipolar disorder patients and healthy persons. The proposed algorithm achieved the best performance compared with other machine learning techniques such as Bayesian linear discriminant analysis, Gaussian naive Bayes, decision trees, K-nearest neighbour, or support vector machine, showing a very high accuracy close to 97%. Therefore, the neural network technique presented could be used as a new tool for the diagnosis of bipolar disorder, considering the possibility of integrating this method into medical software.

Apparatus and methods for mouse behavior recognition on foot contact features

Behavior recognition of model animals such as mice, rats, and monkeys, is of great significance in medical research and vital in the development in medicines or drugs. Studies of the animal’s responses to specific stimulations consisted of visual imaging methods that was easily affected by cage lighting conditions and shooting angle. In this study, we designed a behavior classification system that focused on object-floor contact features extracted from touch images of the frustrated total internal reflection injected with infrared lights. A mouse behavioral dataset (IMBD) was then established, and a support vector machine was applied to conduct this classification. To improve its performance, an improved particle swarm optimization was proposed for the optimization of parameters. Based on IMBD and the support vector machine, we tested different feature extraction methods and classifiers. The system performance of the current study showed more accuracy and efficacy compared with performance of other prevalent particle swarm optimizations. Our study resulted in a recognition rate of up to 94.37% for individual behavior. The average rate of behavioral recognition for all tested mice reached 83.09%. The results suggested that foot contact features of mice were more effective than regular video features in behavior recognition.

Production of 52Fe from symmetric complete fusion-evaporation reactions

A promising method for the production and extraction of 52Fe utilizing near-symmetric, heavy-ion fusion-evaporation reactions is presented. A 28Si beam was used to bombard a 27Al target, producing 52Fe through the reaction: 27Al (28Si, p2n)52Fe. After irradiation, the aluminum target was dissolved, and the 52Fe was separated from the aluminum matrix and other reaction products through the use of a cationic-exchange column. It was possible to recover the 52Fe (t1/2 = 8.3 h) with high specific activity, higher than can be achieved with the high energy proton spallation reactions currently used to produce this isotope, which has significance in medical research. The production yields for this reaction were measured and compared to PACE4 calculations. These results demonstrate the potential for dedicated accelerator production of 52Fe using heavy-ion beams, which will result in higher specific activities with robust radiochemical purification techniques.

Analysis of Bayesian posterior significance and effect size indices for the two-sample t-test to support reproducible medical research

BackgroundThe replication crisis hit the medical sciences about a decade ago, but today still most of the flaws inherent in null hypothesis significance testing (NHST) have not been solved. While the drawbacks of p-values have been detailed in endless venues, for clinical research, only a few attractive alternatives have been proposed to replace p-values and NHST. Bayesian methods are one of them, and they are gaining increasing attention in medical research, as some of their advantages include the description of model parameters in terms of probability, as well as the incorporation of prior information in contrast to the frequentist framework. While Bayesian methods are not the only remedy to the situation, there is an increasing agreement that they are an essential way to avoid common misconceptions and false interpretation of study results. The requirements necessary for applying Bayesian statistics have transitioned from detailed programming knowledge into simple point-and-click programs like JASP. Still, the multitude of Bayesian significance and effect measures which contrast the gold standard of significance in medical research, the p-value, causes a lack of agreement on which measure to report.MethodsTherefore, in this paper, we conduct an extensive simulation study to compare common Bayesian significance and effect measures which can be obtained from a posterior distribution. In it, we analyse the behaviour of these measures for one of the most important statistical procedures in medical research and in particular clinical trials, the two-sample Student’s (and Welch’s) t-test.ResultsThe results show that some measures cannot state evidence for both the null and the alternative. While the different indices behave similarly regarding increasing sample size and noise, the prior modelling influences the obtained results and extreme priors allow for cherry-picking similar to p-hacking in the frequentist paradigm. The indices behave quite differently regarding their ability to control the type I error rates and regarding their ability to detect an existing effect.ConclusionBased on the results, two of the commonly used indices can be recommended for more widespread use in clinical and biomedical research, as they improve the type I error control compared to the classic two-sample t-test and enjoy multiple other desirable properties.

Glia-neuronal transitions in development: significance in medical research and treatment of pathologies

The questions of how autonomic neurons and their fibers select the target organs and whether parasympathetic innervation affects the development and growth of diverse organs are still open. We hypothesize that glia-neuronal transitions in development is crucial for correct development and homeostasis of innervated tissues in mice. The result of this preliminary experiment using genetic tracing showed that the mice mutants with lack autonomic ganglia indeed have a problem with development of target organs and support idea that autonomic nervous system directly involved in innervated organs morphogenesis.

Exploiting Distributed, Heterogeneous and Sensitive Data Stocks while Maintaining the Owner's Data Sovereignty.

To achieve statistical significance in medical research, biological or data samples from several bio- or databanks often need to be complemented by those of other institutions. For that purpose, IT-based search services have been established to locate datasets matching a given set of criteria in databases distributed across several institutions. However, previous approaches require data owners to disclose information about their samples, raising a barrier for their participation in the network. To devise a method to search distributed databases for datasets matching a given set of criteria while fully maintaining their owner's data sovereignty. As a modification to traditional federated search services, we propose the decentral search, which allows the data owner a high degree of control. Relevant data are loaded into local bridgeheads, each under their owner's sovereignty. Researchers can formulate criteria sets along with a project proposal using a central search broker, which then notifies the bridgeheads. The criteria are, however, treated as an inquiry rather than a query: Instead of responding with results, bridgeheads notify their owner and wait for his/her decision regarding whether and what to answer based on the criteria set, the matching datasets and the specific project proposal. Without the owner's explicit consent, no data leaves his/her institution. The decentral search has been deployed in one of the six German Centers for Health Research, comprised of eleven university hospitals. In the process, compliance with German data protection regulations has been confirmed. The decentral search also marks the centerpiece of an open source registry software toolbox aiming to build a national registry of rare diseases in Germany. While the sacrifice of real-time answers impairs some use-cases, it leads to several beneficial side effects: improved data protection due to data parsimony, tolerance for incomplete data schema mappings and flexibility with regard to patient consent. Most importantly, as no datasets ever leave their institution, owners can reject projects without facing potential peer pressure. By its lower barrier for participation, a decentral search service is likely to attract a larger number of partners and to bring a researcher into contact with the right potential partners.

PROCEEDINGS OF THE AUSTRALIAN SOCIETY FOR MEDICAL RESEARCH

PROCEEDINGS OF THE AUSTRALIAN SOCIETY FOR MEDICAL RESEARCH