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Chemosphere Jun 2024Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans,...
Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.
Topics: Wastewater; Machine Learning; Drug Resistance, Microbial; Anti-Bacterial Agents; Bacteria; Bayes Theorem; Neural Networks, Computer; Drug Resistance, Bacterial
PubMed: 38704045
DOI: 10.1016/j.chemosphere.2024.142223 -
Journal of Clinical Medicine Apr 2024: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial... (Review)
Review
: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. : Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. : The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. : The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.
PubMed: 38673581
DOI: 10.3390/jcm13082307 -
Journal of Endourology May 2024To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. A PROSPERO-registered (CRD473152) systematic search of Scopus,...
To perform a systematic review on artificial intelligence (AI) performances to detect urinary stones. A PROSPERO-registered (CRD473152) systematic search of Scopus, Web of Science, Embase, and PubMed databases was performed to identify original research articles pertaining to AI stone detection or measurement, using search terms ("automatic" OR "machine learning" OR "convolutional neural network" OR "artificial intelligence" OR "detection" AND "stone volume"). Risk-of-bias (RoB) assessment was performed according to the Cochrane RoB tool, the Joanna Briggs Institute Checklist for nonrandomized studies, and the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Twelve studies were selected for the final review, including three multicenter and nine single-center retrospective studies. Eleven studies completed at least 50% of the CLAIM checkpoints and only one presented a high RoB. All included studies aimed to detect kidney (5/12, 42%), ureter (2/12, 16%), or urinary (5/12, 42%) stones on noncontrast computed tomography (NCCT), but 42% intended to automate measurement. Stone distinction from vascular calcification interested two studies. All studies used AI machine learning network training and internal validation, but a single one provided an external validation. Trained networks achieved stone detection, with sensitivity, specificity, and accuracy rates ranging from 58.7% to 100%, 68.5% to 100%, and 63% to 99.95%, respectively. Detection Dice score ranged from 83% to 97%. A high correlation between manual and automated stone volume ( = 0.95) was noted. Differentiate distal ureteral stones and phleboliths seemed feasible. AI processes can achieve automated urinary stone detection from NCCT. Further studies should provide urinary stone detection coupled with phlebolith distinction and an external validation, and include anatomical abnormalities and urologic foreign bodies (ureteral stent and nephrostomy tubes) cases.
PubMed: 38666692
DOI: 10.1089/end.2023.0717 -
Journal of Affective Disorders Jul 2024Major depressive disorder (MDD) is a heterogeneous group of mood disorders. A prominent symptom domain is anhedonia narrowly defined as a loss of interest and ability to... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Major depressive disorder (MDD) is a heterogeneous group of mood disorders. A prominent symptom domain is anhedonia narrowly defined as a loss of interest and ability to experience pleasure. Anhedonia is associated with depressive symptom severity, MDD prognosis, and suicidality. We perform a systematic review and meta-analysis of extant literature investigating the effects of anhedonia on health-related quality of life (HRQoL) and functional outcomes in persons with MDD.
METHODS
A literature search was conducted on PubMed, OVID databases, and SCOPUS for published articles from inception to November 2023, reporting on anhedonia and patient-reported outcomes in persons with MDD. The reported correlation coefficients between anhedonia and self-reported measures of both HRQoL and functional outcomes were pooled using a random effects model.
RESULTS
We identified 20 studies that investigated anhedonia with HRQoL and/or functional outcomes in MDD. Anhedonia as measured by the Snaith-Hamilton Pleasure Scale (SHAPS) scores had a statistically significant correlation with patient-reported HRQoL (r = -0.41 [95 % CI = -0.60, -0.18]) and functional impairment (r = 0.39 [95 % CI = 0.22, 0.54]).
LIMITATIONS
These preliminary results primarily investigate correlations with consummatory anhedonia and do not distinguish differences in anticipatory anhedonia, reward valuation or reward learning; therefore, these results require replication.
CONCLUSIONS
Persons with MDD experiencing symptoms of anhedonia are more likely to have worse prognosis including physical, psychological, and social functioning deficits. Anhedonia serves as an important predictor and target for future therapeutic and preventative tools in persons with MDD.
Topics: Humans; Anhedonia; Depressive Disorder, Major; Quality of Life
PubMed: 38657767
DOI: 10.1016/j.jad.2024.04.086 -
International Journal of Geriatric... Apr 2024Stroke survivors are at high risk of coping with cognitive problems after stroke. In recent decades, the relationship between socioeconomic status (SES) and... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Stroke survivors are at high risk of coping with cognitive problems after stroke. In recent decades, the relationship between socioeconomic status (SES) and health-related outcomes has been a topic of considerable interest. Learning more about the potential impact of SES on poststroke cognitive dysfunction is of great importance.
OBJECTIVE
The purpose of this systematic review and meta-analysis was to summarize the association between SES and poststroke cognitive function by quantifying the effect sizes of the existing studies.
METHOD
We searched studies from PubMed, Ovid, Embase, Cochrane, Scopus, and PsychINFO up to January 30 2024 and the references of relevant reviews. Studies reporting the risk of poststroke cognitive dysfunction as assessed by categorized SES indicators were included. The Newcastle-Ottawa scale and the Agency for Healthcare Research and Quality were used to evaluate the study quality. Meta-analyses using fixed-effect models or random-effect models based on study heterogeneity were performed to estimate the influence of SES on cognitive function after stroke, followed by subgroup analyses stratified by study characteristics.
RESULTS
Thirty-four studies were eligible for this systematic review and meta-analysis. Of which, 19 studies reported poststroke cognitive impairment (PSCI) as the outcome, 13 reported poststroke dementia (PSD), one reported both PSCI and PSD, and one reported vascular cognitive impairment no dementia. The findings showed that individuals with lower SES levels had a higher risk of combined poststroke cognitive dysfunction (odds ratio (OR) = 1.91, 95% confidence interval (CI) = 1.59-2.29), PSCI (OR = 2.09, 95% CI = 1.57-2.78), and PSD (OR = 1.95, 95% CI = 1.48-2.57). Subgroup analyses stratified by SES indicators demonstrated the protective effects of education and occupation against the diagnoses of combined poststroke cognitive dysfunction, PSCI, and PSD.
CONCLUSIONS
Stroke survivors belonging to a low SES are at high risk of poststroke cognitive dysfunction. Our findings add evidence for public health strategies to reduce the risk of poststroke cognitive dysfunction by reducing SES inequalities.
Topics: Humans; Cognition; Cognitive Dysfunction; Stroke; Learning; Social Class
PubMed: 38563601
DOI: 10.1002/gps.6082 -
Frontiers in Digital Health 2024This umbrella review aims to ascertain the extent to which immersive Virtual Reality (VR) and Augmented Reality (AR) technologies improve specific competencies in...
OBJECTIVE
This umbrella review aims to ascertain the extent to which immersive Virtual Reality (VR) and Augmented Reality (AR) technologies improve specific competencies in healthcare professionals within medical education and training, in contrast to traditional educational methods or no intervention.
METHODS
Adhering to PRISMA guidelines and the PICOS approach, a systematic literature search was conducted across major databases to identify studies examining the use of VR and AR in medical education. Eligible studies were screened and categorized based on the PICOS criteria. Descriptive statistics and chi-square tests were employed to analyze the data, supplemented by the Fisher test for small sample sizes or specific conditions.
ANALYSIS
The analysis involved cross-tabulating the stages of work (Development and Testing, Results, Evaluated) and variables of interest (Performance, Engagement, Performance and Engagement, Effectiveness, no evaluated) against the types of technologies used. Chi-square tests assessed the associations between these categorical variables.
RESULTS
A total of 28 studies were included, with the majority reporting increased or positive effects from the use of immersive technologies. VR was the most frequently studied technology, particularly in the "Performance" and "Results" stages. The chi-square analysis, with a Pearson value close to significance ( = 0.052), suggested a non-significant trend toward the association of VR with improved outcomes.
CONCLUSIONS
The results indicate that VR is a prevalent tool in the research landscape of medical education technologies, with a positive trend toward enhancing educational outcomes. However, the statistical analysis did not reveal a significant association, suggesting the need for further research with larger sample sizes. This review underscores the potential of immersive technologies to enhance medical training yet calls for more rigorous studies to establish definitive evidence of their efficacy.
PubMed: 38550715
DOI: 10.3389/fdgth.2024.1365345 -
Brain Sciences Mar 2024Mild cognitive impairment (MCI) is a transitional or prodromal stage of dementia in which autonomies are largely preserved (autonomies are not particularly affected).... (Review)
Review
Mild cognitive impairment (MCI) is a transitional or prodromal stage of dementia in which autonomies are largely preserved (autonomies are not particularly affected). However, this condition may entail a depletion of decision-making (DM) abilities likely due to a gradual deterioration of the prefrontal cortex and subcortical brain areas underlying cognitive-emotional processing. Given the clinical implications of a decline in self-determination observed in some MCI sufferers, the present systematic review was aimed at investigating the literature addressing DM processes in patients with MCI, consistent with PRISMA guidelines. The six online databases inquired yielded 1689 research articles that were screened and then assessed based on eligibility and quality criteria. As a result, 41 studies were included and classified following the PICOS framework. Overall, patients with MCI who underwent neuropsychological assessment were found to be slightly or moderately impaired in DM abilities related to financial management, medical adherence, specific cognitive performances, risky conditions, and especially uncertain life circumstances. Comparative cross-sectional studies indicated not only mid-stage cognitive functioning in MCI but also borderline or deficit DM patterns evaluated through different tasks and procedures. Further research addressing MCI profiles suggested an association between explicit memory, executive functions, and DM performance. These findings highlight the diversity of MCI manifestations, in addition to the critical importance of DM features and correlates in patients' daily functioning. Due to a lack of consensus on both MCI and DM, this review paper sought to shed light on assessment and intervention strategies accounting for the interplay between emotion, motivation, and learning to foster DM in cognitively impaired individuals.
PubMed: 38539666
DOI: 10.3390/brainsci14030278 -
Multiple Sclerosis and Related Disorders May 2024Cognitive impairment is highly prevalent in multiple sclerosis (MS) with poorly understood underlying mechanisms. Lipids are considered to be associated with MS... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Cognitive impairment is highly prevalent in multiple sclerosis (MS) with poorly understood underlying mechanisms. Lipids are considered to be associated with MS progression through the inflammatory and oxidative stress pathways, brain atrophy, cellular signaling, and tissue physiology. In addition, serum lipids are proposed as a modifiable factor affecting the neuropsychiatric condition; therefore, this study aims to assess the association between serum lipid levels and cognitive outcomes in MS.
METHODS
This study was carried out following the PRISMA 2020 statement. A systematic search was conducted in PubMed, Scopus, Web of Science, and Embase in March 2023, and the Joanna Briggs Institute (JBI)'s critical appraisal tools were utilized for risk of bias (RoB) assessments in the included studies. The quantitative synthesis was performed with the comprehensive meta-analysis (CMA3) software.
RESULTS
Out of 508 screened records, 7 studies were eventually found to meet our inclusion criteria. In two studies, the course of MS in the sample of the study was only Relapsing-Remitting MS (RRMS), whereas the other five studies' sample was a combination of different phenotypes. Studies utilized different scales such as Minimal Assessment of Cognitive Function in MS (MACFIMS), Brief International Cognitive Assessment for MS (BICAMS), Montreal Cognitive Assessment (MoCA), Brief Repeatable Battery of Neuropsychological Tests (BRB-N) for cognitive evaluations. Dealing with possible confounders such as age, disease duration and level of disability was the most common possible source of bias in the included studies. One study revealed an inverse relationship between serum levels of apolipoproteins (including ApoA-I, ApoB, and ApoB/ApoA-I) and Symbol Digit Modalities Test (SDMT) scores. Also, a correlation between 24S-hydroxycholesterol (24OHC) serum concentrations and SDMT score was reported in one study. The association between serum total cholesterol (TC) and low-density lipoprotein cholesterol (LDL) and different aspects of cognitive function was reported in the studies; however, serum levels of high-density lipoprotein cholesterol (HDL) were not found to be associated. The quantitative synthesis revealed a significant correlation between TC and the MoCA scores (r =-0.238; 95 %CI: -0.366 to -0.100; p-value = 0.001); however, the correlation between TG levels and MoCA were not statistically significant (r:-0.070; 95 %CI: -0.209 to 0.072; p-value: 0.334). In addition, the mata-analyses were not associated with significant findings regarding the correlation between lipid profiles (including HDL, LDL, TG, and TC) and other cognitive assessment scales including SDMT, Brief Visuospatial Memory Test (BVMT), and California Verbal Learning Test (CVLT) (p-values>0.05).
DISCUSSION
Available evidence suggested a link between TC and LDL with cognitive outcomes of MS patients which was not evident in our quantitative synthesis. The limited number of studies, high RoB, different cognitive assessment scales and reporting methods, and the cross-sectional design of the included studies, were the main limitations that alleviate the clinical significance of the findings of this study and suggested further investigations on this topic.
FUNDING AND REGISTRATION
The research protocol was approved and supported by the Student Research Committee, Tabriz University of Medical Sciences (grant number: 71,909). This study is registered in the international prospective register of systematic reviews (PROSPERO ID: CRD42023441625).
Topics: Humans; Cognitive Dysfunction; Multiple Sclerosis; Lipids
PubMed: 38522226
DOI: 10.1016/j.msard.2024.105530 -
Therapeutic Advances in Ophthalmology 2024New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural... (Review)
Review
BACKGROUND
New developments in artificial intelligence, particularly with promising results in early detection and management of keratoconus, have favorably altered the natural history of the disease over the last few decades. Features of artificial intelligence in different machine such as anterior segment optical coherence tomography, and femtosecond laser technique have improved safety, precision, effectiveness, and predictability of treatment modalities of keratoconus (from contact lenses to keratoplasty techniques). These options ingrained in artificial intelligence are already underway and allow ophthalmologist to approach disease in the most non-invasive way.
OBJECTIVES
This study comprehensively describes all of the treatment modalities of keratoconus considering machine learning strategies.
DESIGN
A multidimensional comprehensive systematic narrative review.
DATA SOURCES AND METHODS
A comprehensive search was done in the five main electronic databases (PubMed, Scopus, Web of Science, Embase, and Cochrane), without language and time or type of study restrictions. Afterward, eligible articles were selected by screening the titles and abstracts based on main mesh keywords. For potentially eligible articles, the full text was also reviewed.
RESULTS
Artificial intelligence demonstrates promise in keratoconus diagnosis and clinical management, spanning early detection (especially in subclinical cases), preoperative screening, postoperative ectasia prediction after keratorefractive surgery, and guiding surgical decisions. The majority of studies employed a solitary machine learning algorithm, whereas minor studies assessed multiple algorithms that evaluated the association of various keratoconus staging and management strategies. Last but not least, AI has proven effective in guiding the implantation of intracorneal ring segments in keratoconus corneas and predicting surgical outcomes.
CONCLUSION
The efficient and widespread clinical translation of machine learning models in keratoconus management is a crucial goal of potential future approaches to better visual performance in keratoconus patients.
TRIAL REGISTRATION
The article has been registered through PROSPERO, an international database of prospectively registered systematic reviews, with the ID: CRD42022319338.
PubMed: 38516169
DOI: 10.1177/25158414241232258 -
Journal of the West African College of... 2024Over the last decade, YouTube has been extensively used as a learning tool for both physicians and patients, but the reliability of this information remains...
AIMS AND OBJECTIVES
Over the last decade, YouTube has been extensively used as a learning tool for both physicians and patients, but the reliability of this information remains questionable. The purpose of this study was to look for the reliability and quality of videos on tennis elbow arthroscopy on YouTube.
MATERIALS AND METHODS
We used three search terms on YouTube "tennis elbow arthroscopic surgery," "Arthroscopic ECRB release," and "Arthroscopic debridement for tennis elbow," and screened the first 50 videos according to popularity. The videos were included from 2009 to date. Only videos in the English language were included. Repeated videos and videos without sound were excluded. A total of 74 videos were selected for this study and reliability was checked with DISCERN and journal of the American medical association (JAMA) scores. The quality was assessed with the Global Quality Score Criteria (GQSC) score and TEARS (a novel score). Popularity was tested with the video power index (VPI). A pilot study was conducted using 20 videos to validate the TEARS score.
RESULTS
In the pilot study, TEARS showed results in accordance with other scores used. The average number of views was 41,644.97, and the average duration was 5.03 ± 3.39 years. The mean value of DISCERN and JAMA was found to be 21.47 ± 6.28 and 1.05 ± 0.92, respectively. GQSC, TEARS, and VPI were found to be 1.70 ± 0.82, 4.17 ± 2.62, and 769,936.9 ± 6,538,851.37.
CONCLUSION
Most of the videos were educational and physicians were targeted. The USA was the major contributor to such videos. The reliability and quality of these videos were found to be of poor quality. The video popularity was however found to be relatively high. The inter-observer reliability was good. Based on the findings, we conclude that the videos are not reliable and could not be used for learning.
PubMed: 38486639
DOI: 10.4103/jwas.jwas_18_23