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Journal of Integrative Neuroscience Jun 2024The understanding of neuropathic pain remains incomplete, highlighting the need for research on biomarkers for improved diagnosis and treatment. This review focuses on... (Meta-Analysis)
Meta-Analysis
BACKGROUND
The understanding of neuropathic pain remains incomplete, highlighting the need for research on biomarkers for improved diagnosis and treatment. This review focuses on identifying potential biomarkers in blood and cerebrospinal fluid for neuropathic pain in different neuropathies.
METHODS
Searches were performed in six databases: PubMed, Web of Science, Scopus, Cochrane Library, EMBASE, and CINAHL. Included were observational studies, namely cross-sectional, cohort, and case-control, that evaluated quantitative biomarkers in blood or cerebrospinal fluid. Data were qualitatively synthesized, and meta-analyses were conducted using R. The study is registered with PROSPERO under the ID CRD42022323769.
RESULTS
The literature search resulted in 16 studies for qualitative and 12 for quantitative analysis, covering patients over 18 years of age with painful neuropathies. A total of 1403 subjects were analyzed, identifying no significant differences in levels of C-Reactive Protein (CRP), Interleukin-6 (IL-6), and Tumor Necrosis Factor-alpha (TNF-alpha) between patients with and without pain. Despite the high inter-rater reliability and adequate bias assessment, the results suggest negligible differences in inflammatory biomarkers, with noted publication bias and heterogeneity among studies, indicating the need for further research.
CONCLUSIONS
Our review underscores the complex nature of neuropathic pain and the challenges in identifying biomarkers, with no significant differences found in CRP, IL-6, and TNF-alpha levels between patients with and without pain. Despite methodological robustness, the results are limited by publication bias and heterogeneity. This emphasizes the need for further research to discover definitive biomarkers for improved diagnosis and personalized treatment of neuropathic pain.
Topics: Humans; Neuralgia; Biomarkers; Inflammation Mediators
PubMed: 38940091
DOI: 10.31083/j.jin2306120 -
Parkinson's Disease 2024Fatigue is a common and debilitating symptom affecting a significant proportion of individuals with Parkinson's disease (PD), often overshadowing even motor symptoms in... (Review)
Review
Fatigue is a common and debilitating symptom affecting a significant proportion of individuals with Parkinson's disease (PD), often overshadowing even motor symptoms in its impact on quality of life. The accurate definition and assessment of mental fatigue in PD is crucial for both clinical management and research, yet it remains a challenge due to the subjective nature of the symptom and the heterogeneity of assessment scales. This systematic review examined the existing measures of self-reported mental fatigue in PD by searching through PubMed, Embase, and Scopus databases using specific keywords from 2001 to 2024. Out of the 4182 articles found, 40 met the inclusion criteria, and 14 different scales were identified to measure self-reported fatigue in PD patients. However, most of these scales lack a consistent definition of fatigue, indicating a need for validated combinations of unidimensional and multidimensional scales to accurately assess mental fatigue in PD. The review found that it is best to use Fatigue Severity Inventory (FSI) and Multidimensional Fatigue Inventory (MdFI) to screen for severity of PD mental fatigue and Neuro-QoL Item Bank v1.0 (Neuro-QoL) to evaluate its impact on patients' lives. Furthermore, multidimensional scales Parkinson's Disease Questionnaire (PDQ) and Functional Assessment of Chronic Illness Therapy-Fatigue Scale (FACIT-F) are frequently coupled with Fatigue Severity Scale (FSS), Parkinson's Fatigue Scale (PFS), and/or Modified Fatigue Impact Scale (MFIS) due to their short length and holistic coverage of variables in patients' quality of life. Combining fatigue scales can be used for screening and scoring methods. The review also recommends validating fatigue scales translation and combining them with biomarkers to improve the accuracy and effectiveness of fatigue assessment in clinical practice. Future research should analyze correlations between fatigue scales, expand language types, and explore the link between fatigue scales and the pathophysiological basis of PD. Our findings underscore the need for a standardized approach to the measurement of fatigue in PD and set the stage for future research to consolidate assessment tools that can reliably guide treatment strategies and improve patient outcomes.
PubMed: 38939533
DOI: 10.1155/2024/9614163 -
Gynecologic Oncology Reports Aug 2024Studies suggest a need for new diagnostic approaches for cervical cancer including microRNA technology. In this review, we assessed the diagnostic accuracy of microRNAs...
Studies suggest a need for new diagnostic approaches for cervical cancer including microRNA technology. In this review, we assessed the diagnostic accuracy of microRNAs in detecting cervical cancer and Cervical Intraepithelial Neoplasia (CIN). We performed a systematic review following the Preferred Reporting Items for Systematic Review and Meta-Analysis guideline for protocols (PRISMA-P). We searched for all articles in online databases and grey literature from 01st January 2012 to 16th August 2022. We used the quality assessment of diagnostic accuracy studies tool (QUADAS-2) to assess the risk of bias of included studies and then conducted a Random Effects Meta-analysis. We identified 297 articles and eventually extracted data from 24 studies. Serum/plasma concentration miR-205, miR-21, miR-192, and miR-9 showed highest diagnostic accuracy (AUC of 0.750, 0.689, 0.980, and 0.900, respectively) for detecting CIN from healthy controls. MicroRNA panels (miR-21, miR-125b and miR-370) and (miR-9, miR-10a, miR-20a and miR-196a and miR-16-2) had AUC values of 0.897 and 0.886 respectively for detecting CIN from healthy controls. For detection of cervical cancer from healthy controls, the most promising microRNAs were miR-21, miR-205, miR-192 and miR-9 (AUC values of 0.723, 0.960, 1.00, and 0.99 respectively). We report higher diagnostic accuracy of upregulated microRNAs, especially miR-205, miR-9, miR-192, and miR-21. This highlights their potential as stand-alone screening or diagnostic tests, either with others, in a new algorithm, or together with other biomarkers for purposes of detecting cervical lesions. Future studies could standardize quantification methods, and also study microRNAs in higher prevalence populations like in sub-Saharan Africa and South Asia. Our review protocol was registered in PROSPERO (CRD42022313275).
PubMed: 38939506
DOI: 10.1016/j.gore.2024.101424 -
JACC. Advances Feb 2024
PubMed: 38939393
DOI: 10.1016/j.jacadv.2023.100764 -
JACC. Advances Feb 2024Cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule (KIM)-1 are renal biomarkers increasingly appreciated for their role in the...
BACKGROUND
Cystatin C, neutrophil gelatinase-associated lipocalin (NGAL), and kidney injury molecule (KIM)-1 are renal biomarkers increasingly appreciated for their role in the risk stratification and prognostication of heart failure (HF) patients. However, very few have been adopted clinically, owing to the lack of consistency.
OBJECTIVES
The authors aimed to study the association between cystatin C, NGAL, and KIM-1 and outcomes, mortality, hospitalizations, and worsening renal function (WRF) in patients with acute and chronic HF.
METHODS
We included peer-reviewed English-language articles from PubMed and EMBASE published up to December 2021. We analyzed the above associations using random-effects meta-analysis. Publication bias was assessed using funnel plots.
RESULTS
Among 2,631 articles, 100 articles, including 45,428 patients, met the inclusion criteria. Top-tertile of serum cystatin C, when compared to the bottom-tertile, carried a higher pooled hazard ratio (pHR) for mortality (pHR: 1.59, 95% CI: 1.42-1.77) and for the composite outcome of mortality and HF hospitalizations (pHR: 1.49, 95% CI: 1.23-1.75). Top-tertile of serum NGAL had a higher hazard for mortality (pHR: 2.91, 95% CI: 1.49-5.67) and composite outcome (HR: 4.11, 95% CI: 2.69-6.30). Serum and urine NGAL were significantly associated with WRF, with pHRs of 2.40 (95% CI: 1.48-3.90) and 2.01 (95% CI: 1.21-3.35). Urine KIM-1 was significantly associated with WRF (pHR: 1.60, 95% CI: 1.24-2.07) but not with other outcomes. High heterogeneity was noted between studies without an obvious explanation based on meta-regression.
CONCLUSIONS
Serum cystatin C and serum NGAL are independent predictors of adverse outcomes in HF. Serum and urine NGAL are important predictors of WRF in HF.
PubMed: 38939376
DOI: 10.1016/j.jacadv.2023.100765 -
Journal of Extracellular Biology Nov 2023Parkinsonian disorders, including Parkinson's disease (PD), multiple system atrophy (MSA), dementia with Lewy body (DLB), corticobasal syndrome (CBS) and progressive... (Review)
Review
Parkinsonian disorders, including Parkinson's disease (PD), multiple system atrophy (MSA), dementia with Lewy body (DLB), corticobasal syndrome (CBS) and progressive supranuclear palsy (PSP) are often misdiagnosed due to overlapping symptoms and the absence of precise biomarkers. Furthermore, there are no current methods to ascertain the progression and conversion of prodromal conditions such as REM behaviour disorder (RBD). Extracellular vesicles (EVs), containing a mixture of biomolecules, have emerged as potential sources for parkinsonian diagnostics. However, inconsistencies in previous studies have left their diagnostic potential unclear. We conducted a meta-analysis, following PRISMA guidelines, to assess the diagnostic accuracy of general EVs isolated from various bodily fluids, including cerebrospinal fluid (CSF), plasma, serum, urine or saliva, in differentiating patients with parkinsonian disorders from healthy controls (HCs). The meta-analysis included 21 studies encompassing 1285 patients with PD, 24 with MSA, 105 with DLB, 99 with PSP, 101 with RBD and 783 HCs. Further analyses were conducted only for patients with PD versus HCs, given the limited number for other comparisons. Using bivariate and hierarchal receiver operating characteristics (HSROC) models, the meta-analysis revealed moderate diagnostic accuracy in distinguishing patients with PD from HCs, with substantial heterogeneity and publication bias. The trim-and-fill method revealed at least two missing studies with null or low diagnostic accuracy. CSF-EVs showed better overall diagnostic accuracy, while plasma-EVs had the lowest performance. General EVs demonstrated higher diagnostic accuracy compared to CNS-originating EVs, which are more time-consuming, labour- and cost-intensive to isolate. In conclusion, while holding promise, utilizing biomarkers in general EVs for PD diagnosis remains unfeasible due to existing challenges. The focus should shift toward harmonizing the field through standardization, collaboration, and rigorous validation. Current efforts by the International Society For Extracellular Vesicles (ISEV) aim to enhance the accuracy and reproducibility of EV-related research through rigor and standardization, aiming to bridge the gap between theory and practical clinical application.
PubMed: 38939363
DOI: 10.1002/jex2.121 -
Cureus May 2024Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune... (Review)
Review
Non-small cell lung carcinoma (NSCLC) is a prevalent and aggressive form of lung cancer, with a poor prognosis for metastatic disease. Immunotherapy, particularly immune checkpoint inhibitors (ICIs), has revolutionized the management of NSCLC, but response rates are highly variable. Identifying reliable predictive biomarkers is crucial to optimize patient selection and treatment outcomes. This systematic review aimed to evaluate the current state of artificial intelligence (AI) and machine learning (ML) applications in predicting the response to immunotherapy in NSCLC. A comprehensive literature search identified 19 studies that met the inclusion criteria. The studies employed diverse AI/ML techniques, including deep learning, artificial neural networks, support vector machines, and gradient boosting methods, applied to various data modalities such as medical imaging, genomic data, clinical variables, and immunohistochemical markers. Several studies demonstrated the ability of AI/ML models to accurately predict immunotherapy response, progression-free survival, and overall survival in NSCLC patients. However, challenges remain in data availability, quality, and interpretability of these models. Efforts have been made to develop interpretable AI/ML techniques, but further research is needed to improve transparency and explainability. Additionally, translating AI/ML models from research settings to clinical practice poses challenges related to regulatory approval, data privacy, and integration into existing healthcare systems. Nonetheless, the successful implementation of AI/ML models could enable personalized treatment strategies, improve treatment outcomes, and reduce unnecessary toxicities and healthcare costs associated with ineffective treatments.
PubMed: 38939246
DOI: 10.7759/cureus.61220 -
Expert Review of Gastroenterology &... Jun 2024Alcoholic liver disease (ALD) encompasses a spectrum of liver conditions, including liver steatosis, alcoholic hepatitis (AH), fibrosis, cirrhosis, and hepatocellular... (Review)
Review
INTRODUCTION
Alcoholic liver disease (ALD) encompasses a spectrum of liver conditions, including liver steatosis, alcoholic hepatitis (AH), fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). microRNAs (miRNAs) have garnered significant interest as potential biomarkers for ALD.
METHODS
We searched PubMed, Embase, Web of Science and Cochrane Central Register of Controlled Trials (CENTRAL) systemically from inception to June 2024. All extracted data was stratified according to the stages of ALD. The vote-counting strategy performed a meta-analysis on miRNA expression profiles.
RESULTS
We included 40 studies. In serum of individuals with alcohol-use vs. no alcohol-use, miRNA-122 and miRNA-155 were upregulated, and miRNA-146a was downregulated. In patients with ALD vs. healthy controls, miRNA-122 and miRNA-155 were also upregulated and miRNA-146a was downregulated. However, in patients with AH vs. healthy individuals, only the serum miRNA-122 level was upregulated. Due to insufficient data on diagnostic accuracy, we failed to conclude the ability of miRNAs to distinguish different stages of ALD-related liver fibrosis. The results for ALD-related HCC were also insufficient and controversial.
CONCLUSIONS
Circulating miRNA-122 was the most promising biomarker to manage individuals with ALD. More studies were needed for the diagnostic accuracy of miRNAs in ALD.
REGISTRATION
This protocol was registered on the International Prospective Register of Systematic Reviews (PROSPERO) (www.crd.york.ac.uk/prospero/) with registration number CRD42023391931.
PubMed: 38937981
DOI: 10.1080/17474124.2024.2374470 -
BMC Cancer Jun 2024Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Lung cancer (LC), characterized by high incidence and mortality rates, presents a significant challenge in oncology. Despite advancements in treatments, early detection remains crucial for improving patient outcomes. The accuracy of screening for LC by detecting volatile organic compounds (VOCs) in exhaled breath remains to be determined.
METHODS
Our systematic review, following PRISMA guidelines and analyzing data from 25 studies up to October 1, 2023, evaluates the effectiveness of different techniques in detecting VOCs. We registered the review protocol with PROSPERO and performed a systematic search in PubMed, EMBASE and Web of Science. Reviewers screened the studies' titles/abstracts and full texts, and used QUADAS-2 tool for quality assessment. Then performed meta-analysis by adopting a bivariate model for sensitivity and specificity.
RESULTS
This study explores the potential of VOCs in exhaled breath as biomarkers for LC screening, offering a non-invasive alternative to traditional methods. In all studies, exhaled VOCs discriminated LC from controls. The meta-analysis indicates an integrated sensitivity and specificity of 85% and 86%, respectively, with an AUC of 0.93 for VOC detection. We also conducted a systematic analysis of the source of the substance with the highest frequency of occurrence in the tested compounds. Despite the promising results, variability in study quality and methodological challenges highlight the need for further research.
CONCLUSION
This review emphasizes the potential of VOC analysis as a cost-effective, non-invasive screening tool for early LC detection, which could significantly improve patient management and survival rates.
Topics: Humans; Volatile Organic Compounds; Lung Neoplasms; Early Detection of Cancer; Breath Tests; Exhalation; Sensitivity and Specificity; Biomarkers, Tumor
PubMed: 38937687
DOI: 10.1186/s12885-024-12537-7 -
AJNR. American Journal of Neuroradiology Jun 2024Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma...
BACKGROUND
Visually Accessible Rembrandt (Repository for Molecular Brain Neoplasia Data) Images (VASARI) features, a vocabulary to establish reproducible terminology for glioma reporting, have been applied for a decade, but a systematic performance evaluation is lacking.
PURPOSE
Our aim was to conduct a systematic review and meta-analysis of the performance of the VASARI features set for glioma assessment.
DATA SOURCES
MEDLINE, Web of Science, EMBASE, and the Cochrane Library were systematically searched until September 26, 2023.
STUDY SELECTION
Original articles predicting diagnosis, progression, and survival in patients with glioma were included.
DATA ANALYSIS
The modified Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was applied to evaluate the risk-of-bias. The meta-analysis used a random effects model and forest plot visualizations, if ≥5 comparable studies with a low or medium risk of bias were provided.
DATA SYNTHESIS
Thirty-five studies (3304 patients) were included. Risk-of-bias scores were medium ( = 33) and low ( = 2). Recurring objectives were overall survival ( = 18) and isocitrate dehydrogenase mutation (; = 12) prediction. Progression-free survival was examined in 7 studies. In 4 studies (glioblastoma = 2, grade 2/3 glioma = 1, grade 3 glioma = 1), a significant association was found between progression-free survival and single VASARI features. The single features predicting overall survival with the highest pooled hazard ratios were multifocality (hazard ratio = 1.80; 95%-CI, 1.21-2.67; I = 53%), ependymal invasion (hazard ratio = 1.73; 95% CI, 1.45-2.05; I = 0%), and enhancing tumor crossing the midline (hazard ratio = 2.08; 95% CI, 1.35-3.18; I = 52%). mutation-predicting models combining VASARI features rendered a pooled area under the receiver operating characteristic curve of 0.82 (95% CI, 0.76-0.88) at considerable heterogeneity (I = 100%). Combined input models using VASARI plus clinical and/or radiomics features outperformed single data-type models in all relevant studies ( = 17).
LIMITATIONS
Studies were heterogeneously designed and often with a small sample size. Several studies used The Cancer Imaging Archive database, with likely overlapping cohorts. The meta-analysis for was limited due to a high study heterogeneity.
CONCLUSIONS
Some VASARI features perform well in predicting overall survival and mutation status, but combined models outperform single features. More studies with less heterogeneity are needed to increase the evidence level.
PubMed: 38937115
DOI: 10.3174/ajnr.A8274