Detection of brain regions responsible for chronic pain in osteoarthritis: an fMRI-based neuroimaging study using deep learning

Introduction

Chronic pain is a multifaceted condition that has yet to be fully comprehended. It is frequently linked with a range of disorders, particularly osteoarthritis (OA), which arises from the progressive deterioration of the protective cartilage that cushions the bone endings over time.

Methods

In this paper, we examine the impact of chronic pain on the brain using advanced deep learning (DL) algorithms that leverage resting-state functional magnetic resonance imaging (fMRI) data from both OA pain patients and healthy controls. Our study encompasses fMRI data from 51 pain patients and 20 healthy subjects. To differentiate chronic pain-affected OA patients from healthy controls, we introduce a DL-based computer-aided diagnosis framework that incorporates Multi-Layer Perceptron and Convolutional Neural Networks (CNN), separately.

Results

Among the examined algorithms, we discovered that CNN outperformed the others and achieved a notable accuracy rate of nearly 85%. In addition, our investigation scrutinized the brain regions affected by chronic pain and successfully identified several regions that have not been mentioned in previous literature, including the occipital lobe, the superior frontal gyrus, the cuneus, the middle occipital gyrus, and the culmen.

Discussion

This pioneering study explores the applicability of DL algorithms in pinpointing the differentiating brain regions in OA patients who experience chronic pain. The outcomes of our research could make a significant contribution to medical research on OA pain patients and facilitate fMRI-based pain recognition, ultimately leading to enhanced clinical intervention for chronic pain patients.

Development of a cognitive-based smartphone application for Malaysian Parkinson's disease patients: Exploring the possibility?

The COVID-19 pandemic has accelerated the digital health system. Healthcare organizations want to give medical treatment to individuals who live a great distance away. As a result, they are emphasizing the creation of bespoke telemedicine apps. The number of individuals using telemedicine apps is increasing significantly. Increasing technology gives patients healthcare resources. This has been made feasible via a new telemedicine system and by developing a telemedicine app. Patients can use several technologies to communicate with healthcare professionals. For comfort and privacy, you can employ live visual media. The creation of telemedicine apps is the most attractive and practical investment. With the growing availability and usage of technology in PD, the focus of these technologies is gradually turning toward the disease's vast spectrum of Non-Motor Symptoms (NMS). The nature of NMS makes them difficult to objectively measure, further development and building on experience gained in other conditions may still result in NMS capture that is feasible. Although it is impossible to offer recommendations for the use of digital technology outcomes for NMS in clinical practise based on currently available data, evidence for these devices is evolving, and such guidance may become accessible in the not-too-distant future. To our knowledge, this is the first telemedicine method of its sort to address cognition as one of the NMS in Malay PD patients. The project will be done on two consecutive phases (1 year each); Phase1 aims to develop the Dementia Coach Mobile App, and Phase2 aims to validation of this app by using PD patients sample from SASMEC. Therefore, we hypothesize that developing a friendly mobile app to assess dementia for PD patients is highly beneficial and could be used for diagnosis of NMS in PD patients.

Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis

Introduction:

Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images.

Aims:

This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to leverage its capabilities.

Background:

Ultrasound imaging plays a vital role in healthcare for timely diagnosis and treatment. Increasing interest in automated medical image analysis for precise diagnosis has expanded the demand for a large number of ultrasound images. Generative adversarial networks have been proven beneficial for increasing the size of data by generating synthetic images.

Objective:

Our main purpose in generating synthetic ultrasound images is to produce a sufficient amount of ultrasound images with varying representations of a disease.

Methods:

DCGAN has been used to generate synthetic ultrasound images. It is trained on two ultrasound image datasets, namely, the common carotid artery dataset and nerve dataset, which are publicly available on Signal Processing Lab and Kaggle, respectively.

Results:

Results show that good quality synthetic ultrasound images are generated within 100 epochs of training of DCGAN. The quality of synthetic ultrasound images is evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). We have also presented some visual representations of the slices of generated images for qualitative comparison.

Conclusion:

Our empirical analysis reveals that synthetic ultrasound image generation using DCGAN is an efficient approach.

Other:

In future work, we plan to compare the quality of images generated through other adversarial methods such as conditional GAN, progressive GAN.

An era of brain-computer interface: BCI migration into space

Brain-computer interface's (BCI) potential applications increased tremendously over the past decade. The rising of this new technology is providing promising solutions in the field of aerospace and space exploration.  As astronauts face diverse challenges in long-duration spaceflight, BCI can help astronauts deal with complicated tasks with a minimal mental workload. It may provide intelligent communication systems, maximize safety and security, facilitate space discovery missions, and enhance astronauts' overall health and wellbeing. In new ventures such as SpaceX, Starlink, and Neuralink, pioneers adopt futuristic strategies that use BCI as their main anchor. Such efforts are valuable in neuroscience as they will reveal information that will allow neuroscientists to deeper understand the brain's mechanisms.

Role of neurotransmitters in schizophrenia: a comprehensive study

Schizophrenia is a psychological disorder, way tougher to diagnose than other psychological disorders, as it shares similar symptoms with psychosis. As we know, schizophrenia occurs due to chemical imbalance in the brain; identifying the role of neurotransmitters in schizophrenia poses a vital area to study. Neurotransmitters being the sole carrier of different brain activities, researchers have already initiated studies to investigate their role and effect in disorder. Firstly, this paper performs a critical review of the literature that dealt with different neurotransmitters in schizophrenia. Secondly, we identify the most important neurotransmitters and broadly elaborate on their functional roles and effects on the disorder. Finally, we have successfully identified various gaps and unexplored research questions to investigate these neurochemicals' role. Studies show that neurotransmitters like dopamine, glutamate, GABA, serotonin, and oxytocin are majorly responsible for schizophrenia, among which dopamine contributes the most. To the best of our knowledge, this paper encapsulates all the neurotransmitters, enzymes, and chemicals for the first time and explores their related literature. This study also identifies the most responsible chemicals involved in schizophrenia and unfolds the research community's unsolved problem.

Analysis of endotracheal intubation-related judicial precedents in South Korea

Background

Medical malpractice during endotracheal intubation can result in catastrophic complications. However, there are no reports on these severe complications in South Korea. We aimed to investigate the severe complications associated with endotracheal intubation occurring in South Korea, via medicolegal analysis.

Methods

We retrospectively analyzed the closed judicial precedents regarding complications related to endotracheal intubation lodged between January 1994 and June 2020, using the database of the Supreme Court of Korea. We collected clinical and judicial characteristics from the judgments and analyzed the medical malpractices related to endotracheal intubation.

Results

Of 220 potential cases, 63 were included in the final analysis. The most common event location was the operating room (n = 20, 31.7%). All but 3 cases were associated with significant permanent or more severe injury, including 31 deaths. The most common problems were failed or delayed intubation (n = 56, 88.9%). Supraglottic airway device was used in 5.2% (n = 3) cases of delayed or failed intubation. Fifty-one (81%) cases were ruled in favor of the plaintiff in the claims for damages, with a median payment of Korean Won 133,897,845 (38,000,000, 308,538,274). The most common malpractice recognized by the court was that of not attempting an alternative airway technique (n = 32, 50.8%), followed by violation of the duty of explanation (n = 10, 15.9%).

Conclusions

Our results could increase physicians’ awareness of the major complications related to endotracheal intubation and help ensure patient safety.

Tris(pentafluorophenyl)borane catalyzed C-C and C-heteroatom bond formation

A series of boron based Lewis acids have been reported to date, but among them, tris(pentafluorophenyl)borane (BCF) has gained the most significant attention in the synthetic chemistry community. The viability of BCF as a potential Lewis acid catalyst has been vastly explored in organic and materials chemistry due to its thermal stability and commercial availability. Most explorations of BCF chemistry in organic synthesis has occurred in the last two decades and many new catalytic reactivities are currently under investigation. This review mainly focuses on recent reports from 2018 onwards and provides a concise knowledge to the readers about the role of BCF in metal-free catalysis. The review has mainly been categorized by different types of organic transformation mediated through BCF catalysis for the C-C and C-heteroatom bond formation.

Integrated neural technologies: Solutions beyond tomorrow

Neuroscience is an exciting area in which, at a fast rate, revolutionary advances materialise.  Neurotechnology is interesting and contentious at the same time, as one of its aims is to "wire" human brains directly into computers. Neurotechnology is defined as the assembly of methods and instruments which allow a direct connection to the nervous system of technical components. These instruments are electrodes, machines or smart prostheses. They are designed to record and/or "translate" impulses from the brain into control instructions, or to modify brain function through the application of electrical or optical stimulation. The emergence of neuro-technologies is interdisciplinary. It supports the amalgamation of neurobiology with atomic, nano- and micro-sciences, as a fascinating path for significant development in the neuroscience domain. It poses a scientific foundation for potential therapeutic strategies.

Big Data and Cloud Computing

With evolving technology, a huge data is being generated from everywhere in various forms. The driving factors for the evolution of data, such as retail, media, banking, healthcare, and education, leads to a very large and complex collection of data popularly coined as big data. Handling, management, and analysis of big data seem to be a complicated process. Utilising cloud environment for analysing big data is a recent research trend. Big data analytics can provide cost-effective ways to analyse information quickly and helps in decision making, improvement in services or products. This paper aims to critically review the literature to find current issues and research gaps. This study illustrates the existing solutions and methods provided for big data and its rise in cloud computing technology. Furthermore, this paper throws light on the open research challenges in this domain, stating the scope of future work.

Mean deviation based identification of activated voxels from time-series fMRI data of schizophrenia patients

Background: Schizophrenia is a serious mental illness affecting different regions of the brain, which causes symptoms such as hallucinations and delusions. Functional magnetic resonance imaging (fMRI) is the most popular technique to study the functional activation patterns of the brain. The fMRI data is four-dimensional, composed of 3D brain images over time. Each voxel of the 3D brain volume is associated with a time series of signal intensity values. This study aimed to identify the distinct voxels from time-series fMRI data that show high functional activation during a task.

Methods: In this study, a novel mean-deviation based approach was applied to time-series fMRI data of 34 schizophrenia patients and 34 healthy subjects. The statistical measures such as mean and median were used to find the functional changes in each voxel over time. The voxels that show significant changes for each subject were selected and thus used as the feature set during the classification of schizophrenia patients and healthy controls.

Results: The proposed approach identifies a set of relevant voxels that are used to distinguish between healthy and schizophrenia subjects with high classification accuracy. The study shows functional changes in brain regions such as superior frontal gyrus, cuneus, medial frontal gyrus, middle occipital gyrus, and superior temporal gyrus.

Conclusions: This work describes a simple yet novel feature selection algorithm for time-series fMRI data to identify the activated brain voxels that are generally affected in schizophrenia. The brain regions identified in this study may further help clinicians to understand the illness for better medical intervention. It may be possible to explore the approach to fMRI data of other psychological disorders.

Identification of brain regions associated with working memory deficit in schizophrenia

Background: Schizophrenia, a severe psychological disorder, shows symptoms such as hallucinations and delusions. In addition, patients with schizophrenia often exhibit a deficit in working memory which adversely impacts the attentiveness and the behavioral characteristics of a person. Although several clinical efforts have already been made to study working memory deficit in schizophrenia, in this paper, we investigate the applicability of a machine learning approach for identification of the brain regions that get affected by schizophrenia leading to the dysfunction of the working memory.

Methods: We propose a novel scheme for identification of the affected brain regions from functional magnetic resonance imaging data by deploying group independent component analysis in conjunction with feature extraction based on statistical measures, followed by sequential forward feature selection. The features that show highest accuracy during the classification between healthy and schizophrenia subjects are selected.

Results: This study reveals several brain regions like cerebellum, inferior temporal gyrus, superior temporal gyrus, superior frontal gyrus, insula, and amygdala that have been reported in the existing literature, thus validating the proposed approach. We are also able to identify some functional changes in the brain regions, such as Heschl gyrus and the vermian area, which have not been reported in the literature involving working memory studies amongst schizophrenia patients.

Conclusions: As our study confirms the results obtained in earlier studies, in addition to pointing out some brain regions not reported in earlier studies, the findings are likely to serve as a cue for clinical investigation, leading to better medical intervention.

p-Benzoquinone-induced aggregation and perturbation of structure and chaperone function of α-crystallin is a causative factor of cigarette smoke-related cataractogenesis

Cigarette smoking is a significant risk factor for cataract. However, the mechanism by which cigarette smoke (CS) causes cataract remains poorly understood. We had earlier shown that in CS-exposed guinea pig, p-benzoquinone (p-BQ) derived from CS in the lungs is carried by the circulatory system to distant organs and induces various smoke-related pathogeneses. Here, we observed that CS exposure caused accumulation of the p-BQ-protein adduct in the eye lens of guinea pigs. We also observed accumulation of the p-BQ-protein adduct in resected lens from human smokers with cataract. No such accumulation was observed in the lens of never smokers. p-BQ is a strong arylating agent that forms Michael adducts with serum albumin and haemoglobin resulting in alterations of structure and function. A major protein in the mammalian eye lens is αA-crystallin, which is a potent molecular chaperone. αA-crystallin plays a key role in maintaining the integrity and transparency of the lens. SDS-PAGE indicated that p-BQ induced aggregation of αA-crystallin. Various biophysical techniques including UV-vis spectroscopy, fluorescence spectroscopy, FT-IR, bis-ANS titration suggested a perturbation of structure and chaperone function of αA-crystallin upon p-BQ modification. Our results indicate that p-BQ is a causative agent involved in the modification of αA-crystallin and pathogenesis of CS-induced cataract. Our findings would educate public about the impacts of smoking on eye health and help to discourage them from smoking. The study might also help scientists to develop new drugs for the intervention of CS-induced cataract at an early stage.