Differentiating schizophrenic patients from healthy control; application of machine learning to resting state fmri

In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviors and other variables of interest from fMRI data. Most of these studies focus on fMRI low frequency oscillations. This study focuses on the amplitude of low-frequency fluctuations (ALFF) and fractional amplitude of low-frequency fluctuations (fALFF). A Voxel-wise analysis is performed on the whole brain for two groups of subjects. A machine learning algorithm is applied to two independent groups of subjects (a total of 160 healthy control and schizophrenic subjects) to classify Schizophrenia subjects from healthy control. Kendall tau rank correlation coefficient is also used to dominate most important voxels (features). This study is done on three datasets: a) fALFF b) mALFF dataset and c) combination of mALFF and fALFF. The results show that using the combination dataset improves the classification and demonstrates that machine learning algorithms can extract new information from a resting state image of schizophrenia which can help in diagnosing and treating schizophrenic patients in the future. Future studies can focus on testing these algorithms on different modalities and moreover on different physiological disorders

Investigation of NQO1 genetic polymorphism, NQO1 gene expression and PAH-DNA adducts in ESCC. A case-control study from Iran

We evaluated the effect of NQO1 genetic variation on PAH-DNA adducts in esophageal squamous cell carcinoma (ESCC) in northeast Iran. Golestan Province in northeast of Iran has one of the highest esophageal cancer incidences in the world. The study included 93 ESCC cases and 50 control individuals who were seen at the clinical cancer center in Golestan province. NQO1 C609T genotypes were determined by PCR-RFLP analysis. NQO1 gene expression in tissue samples was determined by quantitative real-time PCR. Immunohistochemical techniques were used to detect PAH-DNA adducts in ESCC and normal esophageal tissues. The distributions of NQO1 genetic polymorphism between cases and the control group were not significantly different. NQO1 gene expression was not higher in tumor tissues than in normal esophageal tissues adjacent to the ESCC; expression was higher in tumor tissues that had the NQO1 T allele. NQO1 gene expression was high in normal esophageal tissues. The level of PAH-DNA adducts was significantly higher in ESCC tissues of cases than in normal tissues adjacent to tumor tissues and in normal esophageal tissues of healthy controls. There were no significant differences between the adduct levels of normal esophageal tissues of patients and controls. There was also no significant relationship between cigarette smoking and PAH-DNA adducts. We concluded that PAHs are a risk factor for ESCC and that PAH-DNA adducts have potential as a biomarker for risk of ESCC

Methodological challenges in the evidence synthesis of health outcomes of digital health technologies [védés előtt]

Medical devices and pharmaceuticals are worlds apart, but healthcare would be impossible without them. Digital biomarkers are the subject of this thesis defined as objective, measurable, physiological, and behavioural parameters collected using wearable, portable, implantable, or digestible digital devices. Since the 1970s, systematic reviews and meta-analyses have dominated medical evidence synthesis. They provide medical decision-making evidence. To avoid biases and maintain methodological quality, the Cochrane Handbook recommends systematic reviews follow certain procedures during study stages. This thesis comprises six hypotheses related to digital biomarkers. The first hypothesis aimed to evaluate the suitability of using tools provided by the World Health Organization (WHO), including ICD-11 (International Classification of Diseases, 11th Revision), ICHI (International Classification of Health Interventions), and ICF (International Classification of Functioning, Disability and Health), for categorizing populations, interventions, outcomes, and behavioral/physiological data in studies involving digital biomarkers. The results indicated that these tools were not applicable for categorizing digital biomarker studies as a whole. However, further analysis revealed that these tools were suitable for categorizing digital biomarker studies involving non-general populations or populations with specific diseases. The second hypothesis focused on comparing the statistical power of direct and indirect digital biomarkers. The results indicated that there was no significant difference in power between these two types of digital biomarkers (p-value > 0.05). The next three hypotheses compared the characteristics of systematic reviews and meta-analyses of digital biomarker-based interventions with those of non-digital biomarkers or pharmaceuticals. The comparisons were made in terms of methodological quality, quality of evidence, and publication bias. Although all these hypotheses revealed non-significant differences between the two groups (p-values > 0.05), the results showed that both digital biomarkers and non-digital biomarkers or pharmaceuticals systematic reviews did not exhibit high methodological quality or quality of evidence. The Medical Device Regulation (MDR) has significantly improved European medical device regulatory standards, addressing the above concerns and improving clinical evidence. Despite MDR implementation delays, digital health technology evidence requirements are rising. Companies that achieve these higher clinical requirements will survive and obtain access to large interconnected markets, while those that fail may lose their market authorisation. Thus, medical technology enterprises may gain a competitive edge by strategically planning and executing extensive clinical investigations to provide high-quality clinical data. Developing these essential skills needs immediate attention and effort. Digital health investors should actively monitor industry players' evidence quality and clinical trial competence, since these characteristics may significantly increase company risk

The Features of Cardiovascular Papers and Impact on Citations

Introduction: The number of citations is a factor in evaluating the quality of scientific articles. The present study aims to examine the factors affecting the citation rate of cardiovascular articles. Methods: In this scientometrics study, the research population is all cardiovascular articles in 2014 in Web of Science (WoS), including a sample of 381 articles studied. Pearson correlation coefficient, Mann–Whitney, Kruskal–Wallis, and Bonferroni tests were used to examine the impact of article features on citations. Results: The results indicated that all quantitative variables (title length, number of authors, author's H-index, journal IF, number of pages, number of author's keywords, number of keywords-plus, number of references)had a significant relationship with the number of citations (P-value<0.001), except for the number of article keywords.  All of the qualitative variables (title length, number of authors, author's H-index, journal IF, number of pages, number of author's keywords, number of keywords-plus, number of references) also affect the number of citations (P-value<0.001). Open access articles, articles with the first author from Australia and North America, articles with international collaboration, and meta-analysis articles have received a more citation rate. Conclusion: Paying attention to the factors affecting the citation rate of cardiovascular articles can be of help to cardiovascular centers for policy-making and researchers in determining the research approach. In this way, they can improve the citation of their works

Visual Acuity and Prognosis among Hyperopic Patients Undergoing Photorefractive Keratectomy Using Allegretto EX500 Excimer Laser

Purpose: This study aims to investigate the visual acuity and prognosis after photorefractive keratectomy among hyperopic patients with and without astigmatism. Patients and Methods: In this interventional case series study, 74 eyes from 42 hyperopia patients with and without astigmatism who underwent photorefractive keratectomy using Allegretto EX500 excimer laser at Torfeh and Negah Eye Hospitals from 2014 to 2018 were enrolled. Pre-and post-surgical visual examination findings, including uncorrected distance visual acuity, corrected distance visual acuity, manifest refraction, cyclorefraction, and slit lamp examinations to measure ocular pressure and the presence or absence of haze, were recorded. Results: The mean age of participants was 34 ± 9 years, and 54.8 % were female. The preoperative mean uncorrected distance visual acuity was 0.55 ± 0.25 LogMAR, which significantly improved to 0.11 ± 0.14 at 6 months postoperatively (P < 0.0001). The predictive value for surgical outcomes at six months post-operation was 71.6 % within ±0.5 diopter, 89.2 % within ±1 diopter, and 97.3 % within ±2 diopters. No eye lost corrected distance visual acuity of two lines or more, and only 16.6 % (12 eyes) experienced a one-line reduction in corrected distance visual acuity. No other notable complications occurred. Conclusion: Photorefractive keratectomy using Allegretto EX500 excimer laser is an effective and safe method for correcting mild to moderate hyperopia with or without astigmatism

A voting-based machine learning approach for classifying biological and clinical datasets.

BACKGROUND: Different machine learning techniques have been proposed to classify a wide range of biological/clinical data. Given the practicability of these approaches accordingly, various software packages have been also designed and developed. However, the existing methods suffer from several limitations such as overfitting on a specific dataset, ignoring the feature selection concept in the preprocessing step, and losing their performance on large-size datasets. To tackle the mentioned restrictions, in this study, we introduced a machine learning framework consisting of two main steps. First, our previously suggested optimization algorithm (Trader) was extended to select a near-optimal subset of features/genes. Second, a voting-based framework was proposed to classify the biological/clinical data with high accuracy. To evaluate the efficiency of the proposed method, it was applied to 13 biological/clinical datasets, and the outcomes were comprehensively compared with the prior methods. RESULTS: The results demonstrated that the Trader algorithm could select a near-optimal subset of features with a significant level of p-value \u3c 0.01 relative to the compared algorithms. Additionally, on the large-sie datasets, the proposed machine learning framework improved prior studies by ~ 10% in terms of the mean values associated with fivefold cross-validation of accuracy, precision, recall, specificity, and F-measure. CONCLUSION: Based on the obtained results, it can be concluded that a proper configuration of efficient algorithms and methods can increase the prediction power of machine learning approaches and help researchers in designing practical diagnosis health care systems and offering effective treatment plans