MedNexus
2022年 · 第08卷第03期
MedNexus
自从冠状病毒疾病2019（新冠肺炎）爆发成为疫情以来，医务人员和研究人员一直在病原体、预防和治疗方面致力于管理这种疾病。即便如此，病毒仍在继续肆虐人们的生活。最近的证据表明，严重急性呼吸综合征冠状病毒2型患者表现出不同的症状，从无症状或轻度感染到致命疾病。此外，这种病毒不仅引起急性炎症反应，还可能在急性感染阶段后引起一系列持续症状。国家健康和护理卓越研究所将这种现象定义为新冠后遗症。
Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid-19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X-ray) and Covid-19 (real-time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid-19 can be detected instantly from chest X-rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid-19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community-acquired pneumonia, viral pneumonia, and Covid-19 from images of chest X-rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one-stop solution to beginners and current researchers interested in this field.
Most of the medical and nonmedical research labs, all around the world, are racing against time to produce an effective vaccine or an antiviral medicine for coronavirus disease 2019 (COVID-19). Conventional medicines and novel nano-materials including chemical and herbal-based compounds are all into positive trials toward coronaviruses and other pandemic infections. Among them, natural immune boosters have attracted physicians because of their longevity and reliability for fewer side effects. This is a review article with a detailed picture of an unexplored antiviral source with maximum potency in curing viral infections. Cyanobacteriae have been known for centuries and are rich in secondary metabolites of proteins, biopeptides, and polysaccharides for prominent antiviral action against chest infections. But detailed exploratory research is required to purify, scale-up, and commercialize the pharmacologically active agents from these drug reserves.
The complement system plays a key role in the pathogenesis of autoimmune diseases, which usually injures the kidney. More and more studies have shown the pathogenic role and indicated that abnormal activation of the complement system was highly involved in the outbreak of autoimmune diseases. This review mainly introduced recent studies of complement system activation contributing to the pathogenesis of autoimmune diseases, including systemic lupus erythematosus, antiphospholipid syndrome, antineutrophil cytoplasmic antibody-associated vasculitides, and so on. Understanding the pathogenic roles of complement activation in various autoimmune diseases will identify potential novel therapeutic targets on complement systems.
Grayscale image attributes of computed tomography (CT) of pulmonary scans contain valuable information relating to patients with respiratory ailments. These attributes are used to evaluate the severity of lung conditions of patients confirmed to be with and without COVID-19.
Five hundred thirteen CT images relating to 57 patients (49 with COVID-19; 8 free of COVID-19) were collected at Namazi Medical Centre (Shiraz, Iran) in 2020 and 2021. Five visual scores (VS: 0, 1, 2, 3, or 4) are clinically assigned to these images with the score increasing with the severity of COVID-19-related lung conditions. Eleven deep learning and machine learning techniques (DL/ML) are used to distinguish the VS class based on 12 grayscale image attributes.
The convolutional neural network achieves 96.49% VS accuracy (18 errors from 513 images) successfully distinguishing VS Classes 0 and 1, outperforming clinicians’ visual inspections. An algorithmic score (AS), involving just five grayscale image attributes, is developed independently of clinicians’ assessments (99.81% AS accuracy; 1 error from 513 images).
Grayscale CT image attributes can be successfully used to distinguish the severity of COVID-19 lung damage. The AS technique developed provides a suitable basis for an automated system using ML/DL methods and 12 image attributes.
Schizophrenia (SCZ) is a complex psychiatric disorder associated with widespread alterations in the subcortical brain structure. Hemispheric asymmetries are a fundamental organizational principle of the human brain and relate to human psychological and behavioral characteristics. We aimed to explore the state of thalamic lateralization of SCZ.
We used voxel-based morphometry (VBM) analysis, whole-brain analysis of low-frequency fluctuations (ALFF), fractional amplitude of low-frequency fluctuations (fALFF), and resting-state seed-based functional connectivity (FC) analysis to investigate brain structural and functional deficits in SCZ. Also, we applied Pearson's correlation analysis to validate the correlation between Positive and Negative Symptom Scale (PANSS) scores and them.
Compared with healthy controls, SCZ showed increased gray matter volume (GMV) of the left thalamus (t = 2.214, p = 0.029), which positively correlated with general psychosis (r = 0.423, p = 0.010). SCZ also showed increased ALFF in the putamen, the caudate nucleus, the thalamus, fALFF in the nucleus accumbens (NAc), and the caudate nucleus, and decreased fALFF in the precuneus. The left thalamus showed significantly weaker resting-state FC with the amygdala and insula in SCZ. PANSS negative symptom scores were negatively correlated with the resting-state FC between the thalamus and the insula (r = -0.414, p = 0.025).
Collectively, these results suggest the possibility of aberrant laterality in the left thalamus and its FC with other related brain regions involved in the limbic system.
The genetic central dogma (GCD) has been demonstrated its essential function in many biological processes and diseases. However, its roles in the process of osteogenic differentiation of mesenchymal stem cells (MSCs) remain unclear.
In this project, we analyzed an online database of osteogenic differentiation of MSCs after 14 days and 28 days by osteoinductive medium (GSE83770). The differentially expressed genes were screened by GEO2R, with further conducting of KEGG pathways using DAVID. In addition, protein-protein interactions of the enriched pathways were performed using STRING with marked hub genes measured by the CytoHubba. Hub genes were verified by quantitative reverse-transcription polymerase chain reaction.
Results showed that six pathways related to GCD, including DNA replication, Aminoacyl-tRNA biosynthesis, Mismatch repair, Ribosome, Spliceosome, and RNA degradation pathways enriched in the early stage (14 days vs. undifferentiated MSCs) of osteogenesis. The Lysosome pathway was highly enriched in the late stage (28 vs. 14 days) of osteogenesis, and Ribosome pathway plays a key role throughout the entire process (28 days vs. undifferentiated MSCs) of osteogenesis.
Both DNA replication and protein translation were functionally worked in the early stage of osteogenesis, whereas the Lysosome pathway was the only GCD-related one in the late stage of osteogenesis. The GCD-related Ribosome pathway occupied the entire process of osteogenesis.
The diagnosis of epilepsy in a child often and understandably causes psychological adjustment difficulties in the parents. To help parents of children with epilepsy cope with stress, it is important to understand how parents cope with the sickness of their child. The objective of this study was to assess factors related to the state of anxiety and depression among parents of children with epilepsy.
The present study was a cross-sectional study, and the data were collected through an anonymous, Internet-based survey platform between October 2018 and October 2019 from 250 participants aged 22-65 years. Participants were invited to fill questionnaires include socioeconomic questionnaire, anxiety, depression, and coping strategies scale.
Among the parents of children with epilepsy, 48.8% (122/250) had depressive symptoms (Patient Health Questionnaire-9 [PHQ-9] score >4) and 46.4% (116/250) had anxiety symptoms (7-item Generalized Anxiety Disorder [GAD-7] score >5). Depression among parents of children with epilepsy was significantly associated with comorbidity (odds ratio [OR] = 0.392, 95% CI = 0.182-0.846), a poor parental relationship (OR = 0.283, 95% CI = 0.130-0.614), positive coping (OR = 0.947, 95% CI = 0.903-0.992), and negative coping (OR = 1.287, 95% CI = 1.179-1.405). Anxiety among parents of children with epilepsy was significantly associated with a poor parental relationship (OR = 0.416, 95% CI = 0.207-0.835) and negative coping (OR = 1.155, 95% CI = 1.087-1.228).
The present study indicates the importance of couple support and providing effective coping to make parents of children with epilepsy more resilient in the presence of negative life events, especially for parents of children with comorbidity with cognitive deficiency.
在2012年的一项研究中，偶尔和少量累积使用大麻与对肺功能的不良影响无关。
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