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Journal of Forensic and Legal Medicine Jul 2023One of the most important parameters in the identification process in forensic Medicine and Dentistry is the determination of sex through the skull, based on... (Review)
Review
One of the most important parameters in the identification process in forensic Medicine and Dentistry is the determination of sex through the skull, based on morphological and metric dimorphism. Photogrammetry is an affordable option that allows the reconstruction of position, orientation, shape, and size, allowing the performance of quantitative and qualitative analyzes to identify the sex of the individual. However, there are few systematic reviews in the literature validating whether photogrammetry is a reliable methodology for sexual identification using human skulls. Therefore, the objective of the current systematic review was to validate whether photogrammetry of dry skulls is reliable as a method for calculating sex in human identification. This revision follows the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) and was recorded in the Prospective International Systematic Reviews Registry (PROSPERO) (CRD420223 Systematic Registry) (CRD420223). The inclusion criteria for selecting the studies were based on the PICO question: "Is test photogrammetry reliable as a method for estimating sex in human identification?". A literature search for studies was performed in the databases MEDLINE Scopus, Web of Science, LILACS, and the Cochrane Library. The Kappa agreement presented an approval level of (k = 0.93). This systematic review analyzed 11 ex-vivo studies published between 2001 and 2021. The risk of bias was considered low in 8 of the studies, and high in 3 studies. Based on this systematic review, it can be concluded that the photogrammetry method is viable and reliable in identifying sexual dimorphism.
Topics: Humans; Prospective Studies; Skull; Head; Sex Characteristics; Photogrammetry
PubMed: 37307776
DOI: 10.1016/j.jflm.2023.102546 -
Artificial Intelligence in Medicine Aug 2023Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact... (Review)
Review
BACKGROUND
Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.
OBJECTIVE
This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.
METHODS
We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.
RESULTS
Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.
CONCLUSION
This systematic review highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.
Topics: Humans; Amyotrophic Lateral Sclerosis; Artificial Intelligence; Brain; Cluster Analysis; Databases, Factual
PubMed: 37316101
DOI: 10.1016/j.artmed.2023.102588 -
EBioMedicine Jul 2023Various studies have reported cell-free RNAs (cfRNAs) as noninvasive biomarkers for detecting hepatocellular carcinoma (HCC). However, they have not been independently...
BACKGROUND
Various studies have reported cell-free RNAs (cfRNAs) as noninvasive biomarkers for detecting hepatocellular carcinoma (HCC). However, they have not been independently validated, and some results are contradictory. We provided a comprehensive evaluation of various types of cfRNA biomarkers and a full mining of the biomarker potential of new features of cfRNA.
METHODS
We first systematically reviewed reported cfRNA biomarkers and calculated dysregulated post-transcriptional events and cfRNA fragments. In 3 independent multicentre cohorts, we further selected 6 cfRNAs using RT-qPCR, built a panel called HCCMDP with AFP using machine learning, and internally and externally validated HCCMDP's performance.
FINDINGS
We identified 23 cfRNA biomarker candidates from a systematic review and analysis of 5 cfRNA-seq datasets. Notably, we defined the cfRNA domain to describe cfRNA fragments systematically. In the verification cohort (n = 183), cfRNA fragments were more likely to be verified, while circRNA and chimeric RNA candidates were neither abundant nor stable as qPCR-based biomarkers. In the algorithm development cohort (n = 287), we build and test the panel HCCMDP with 6 cfRNA markers and AFP. In the independent validation cohort (n = 171), HCCMDP can distinguish HCC patients from control groups (all: AUC = 0.925; CHB: AUC = 0.909; LC: AUC = 0.916), and performs well in distinguishing early-stage HCC patients (all: AUC = 0.936; CHB: AUC = 0.917; LC: AUC = 0.928).
INTERPRETATION
This study comprehensively evaluated full-spectrum cfRNA biomarker types for HCC detection, highlighted the cfRNA fragment as a promising biomarker type in HCC detection, and provided a panel HCCMDP.
FUNDING
National Natural Science Foundation of China, and The National Key Basic Research Program (973 program).
Topics: Humans; Carcinoma, Hepatocellular; Liver Neoplasms; alpha-Fetoproteins; Cell-Free Nucleic Acids; Biomarkers, Tumor; ROC Curve; MicroRNAs
PubMed: 37315449
DOI: 10.1016/j.ebiom.2023.104645 -
Bioengineering (Basel, Switzerland) Aug 2023Biomechanical studies play an important role in understanding the pathophysiology of sleep disorders and providing insights to maintain sleep health. Computational... (Review)
Review
Biomechanical studies play an important role in understanding the pathophysiology of sleep disorders and providing insights to maintain sleep health. Computational methods facilitate a versatile platform to analyze various biomechanical factors in silico, which would otherwise be difficult through in vivo experiments. The objective of this review is to examine and map the applications of computational biomechanics to sleep-related research topics, including sleep medicine and sleep ergonomics. A systematic search was conducted on PubMed, Scopus, and Web of Science. Research gaps were identified through data synthesis on variants, outcomes, and highlighted features, as well as evidence maps on basic modeling considerations and modeling components of the eligible studies. Twenty-seven studies ( = 27) were categorized into sleep ergonomics ( = 2 on pillow; = 3 on mattress), sleep-related breathing disorders ( = 19 on obstructive sleep apnea), and sleep-related movement disorders ( = 3 on sleep bruxism). The effects of pillow height and mattress stiffness on spinal curvature were explored. Stress on the temporomandibular joint, and therefore its disorder, was the primary focus of investigations on sleep bruxism. Using finite element morphometry and fluid-structure interaction, studies on obstructive sleep apnea investigated the effects of anatomical variations, muscle activation of the tongue and soft palate, and gravitational direction on the collapse and blockade of the upper airway, in addition to the airflow pressure distribution. Model validation has been one of the greatest hurdles, while single-subject design and surrogate techniques have led to concerns about external validity. Future research might endeavor to reconstruct patient-specific models with patient-specific loading profiles in a larger cohort. Studies on sleep ergonomics research may pave the way for determining ideal spine curvature, in addition to simulating side-lying sleep postures. Sleep bruxism studies may analyze the accumulated dental damage and wear. Research on OSA treatments using computational approaches warrants further investigation.
PubMed: 37627802
DOI: 10.3390/bioengineering10080917 -
Journal of Translational Medicine Oct 2023Animal models are widely used to study pathological processes and drug (side) effects in a controlled environment. There is a wide variety of methods available for... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Animal models are widely used to study pathological processes and drug (side) effects in a controlled environment. There is a wide variety of methods available for establishing animal models depending on the research question. Commonly used methods in tumor research include xenografting cells (established/commercially available or primary patient-derived) or whole tumor pieces either orthotopically or heterotopically and the more recent genetically engineered models-each type with their own advantages and disadvantages. The current systematic review aimed to investigate the meningioma model types used, perform a meta-analysis on tumor take rate (TTR), and perform critical appraisal of the included studies. The study also aimed to assess reproducibility, reliability, means of validation and verification of models, alongside pros and cons and uses of the model types.
METHODS
We searched Medline, Embase, and Web of Science for all in vivo meningioma models. The primary outcome was tumor take rate. Meta-analysis was performed on tumor take rate followed by subgroup analyses on the number of cells and duration of incubation. The validity of the tumor models was assessed qualitatively. We performed critical appraisal of the methodological quality and quality of reporting for all included studies.
RESULTS
We included 114 unique records (78 using established cell line models (ECLM), 21 using primary patient-derived tumor models (PTM), 10 using genetically engineered models (GEM), and 11 using uncategorized models). TTRs for ECLM were 94% (95% CI 92-96) for orthotopic and 95% (93-96) for heterotopic. PTM showed lower TTRs [orthotopic 53% (33-72) and heterotopic 82% (73-89)] and finally GEM revealed a TTR of 34% (26-43).
CONCLUSION
This systematic review shows high consistent TTRs in established cell line models and varying TTRs in primary patient-derived models and genetically engineered models. However, we identified several issues regarding the quality of reporting and the methodological approach that reduce the validity, transparency, and reproducibility of studies and suggest a high risk of publication bias. Finally, each tumor model type has specific roles in research based on their advantages (and disadvantages).
SYSTEMATIC REVIEW REGISTRATION
PROSPERO-ID CRD42022308833.
Topics: Animals; Humans; Meningeal Neoplasms; Meningioma; Reproducibility of Results; Disease Models, Animal
PubMed: 37898750
DOI: 10.1186/s12967-023-04620-7 -
Heliyon Nov 2023This systematic review and meta-analysis aimed to systematically evaluate the prediction models for the risk of post-thrombotic syndrome (PTS) in deep vein thrombosis...
OBJECTIVE
This systematic review and meta-analysis aimed to systematically evaluate the prediction models for the risk of post-thrombotic syndrome (PTS) in deep vein thrombosis (DVT) patients.
METHODS
This systematic review and meta-analysis was guided by the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). A systematic search on the following electronic database: PubMed/MEDLINE, EMBASE, and Cochrane Library, and Chinese databases such as WANFANG and CNKI was conducted to look for relevant articles based on the research question. The risk of bias for each studies included was carried out based on Prediction Model Risk of Bias Assessment Tool (PROBAST).
RESULTS
We identified 10 studies that developed a total of 13 clinical prediction models for PTS risk in DVT patients, 3 models were externally validated, 2 models were temporally validated. The top 5 predictors were: BMI (N = 9), Varicose vein (N = 6), Baseline Villalta Score (N = 6), Iliofemoral thrombosis (N = 5), and Age (N = 4). The high risk of bias was from the analysis domain, which the number of participants and selection of predictors often did not meet the requirements of PROBAST. A random-effects meta-analysis of C-statistics was conducted, the pooled discrimination was C-statistic 0.75, 95%CI (0.69, 0.81).
CONCLUSION
Among the 13 PTS risk prediction models reported in this study, no prediction model has been applied to clinical practice due to the lack of external validation. In the development of prediction models, most models were not standardized in data analysis. It is recommended that future studies on the design and implementation of prediction models refer to Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) and PROBAST.
PubMed: 38045217
DOI: 10.1016/j.heliyon.2023.e22226 -
ESC Heart Failure Aug 2023The aim of the meta-analysis was to generate a more comprehensive understanding of the HFA-PEFF score in the diagnosis of heart failure with preserved ejection fraction... (Meta-Analysis)
Meta-Analysis Review
The aim of the meta-analysis was to generate a more comprehensive understanding of the HFA-PEFF score in the diagnosis of heart failure with preserved ejection fraction (HFpEF) and to pose clues in the field of scientific and clinical practice. Electronic databases of PubMed, Web of Science, Cochrane Library, and Embase were systematically searched. Studies investigating the use of the HFA-PEFF score to diagnose HFpEF were included. Pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative Likelihood Ratio (NLR), diagnostic odds ratio (DOR), area under the curve of summary receiver operating characteristic, and superiority index were calculated. Five studies with 1521 participants were included in this meta-analysis. In the pooled analysis of the 'Rule-out' approach, the pooled sensitivity, specificity, PLR, NLR, and DOR were 0.98 (0.94, 1.00), 0.33 (0.08, 0.73), 1.5 (0.8, 2.5), 0.05 (0.02, 0.17), and 28 (6, 127). In the pooled analysis of the 'Rule-in' approach, the pooled sensitivity and specificity, PLR, NLR, and DOR were 0.69 (0.62, 0.75), 0.87 (0.64, 0.96), 5.5 (1.8, 16.9), 0.35 (0.30, 0.41), and 16 (5, 50). This meta-analysis indicates that the HFA-PEFF algorithm showed acceptable specificity and sensitivity for the diagnosis and exclusion of HFpEF. More relevant studies on the diagnostic validity of the HFA-PEFF score are needed in the future.
Topics: Humans; Heart Failure; Stroke Volume; Sensitivity and Specificity; ROC Curve; Algorithms
PubMed: 37292053
DOI: 10.1002/ehf2.14421 -
Digestive and Liver Disease : Official... Oct 2023Several ursodeoxycholic acid (UDCA) treatment response definitions have been introduced in primary biliary cholangitis (PBC). However, the lack of a gold standard... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Several ursodeoxycholic acid (UDCA) treatment response definitions have been introduced in primary biliary cholangitis (PBC). However, the lack of a gold standard results in heterogeneity in second-line treatment research and clinical practice.
AIMS
This study aimed to explore which UDCA treatment response endpoint serves as the most accurate predictive model of long-term outcome.
METHODS
A systematic review and meta-analysis of UDCA treatment response endpoints (and corresponding validations) were performed.
RESULTS
Sixteen individual UDCA treatment response endpoints and 96 external validations were found. Barcelona, Paris-1, Paris-2, Rotterdam, Toronto and GLOBE and UK-PBC Risk Scores are currently most robustly validated in external populations. The results show that the continuous models (GLOBE and UK-PBC Risk Scores) serve as the most accurate predictive models. Besides standard UDCA treatment response endpoints, the alkaline phosphatase and total bilirubin normalization has been suggested as a new therapeutic target.
CONCLUSIONS
The GLOBE and UK-PBC Risk Scores are the most suitable for the real-world allocation of second-line therapies (obeticholic acid and fibrates). However, in the wake of the recent findings, alkaline phosphatase and total bilirubin normalization should be the primary outcome in trial research in PBC.
Topics: Humans; Ursodeoxycholic Acid; Liver Cirrhosis, Biliary; Cholagogues and Choleretics; Alkaline Phosphatase; Treatment Outcome; Bilirubin; Cholangitis
PubMed: 36593158
DOI: 10.1016/j.dld.2022.12.010 -
International Journal of Nursing Studies Sep 2023Perineal lacerations could lead to substantial morbidities for women. A reliable prediction model for perineal lacerations has the potential to guide the prevention....
BACKGROUND
Perineal lacerations could lead to substantial morbidities for women. A reliable prediction model for perineal lacerations has the potential to guide the prevention. Although several prediction models have been developed to estimate the risk of perineal lacerations, especially third- and fourth-degree perineal lacerations, the evidence about the model quality and clinical applicability is scarce.
OBJECTIVES
To systematically review and critically appraise the existing prediction models for perineal lacerations.
METHODS
Seven databases (PubMed, Embase, The Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, SinoMed, China National Knowledge Infrastructure, and Wanfang Data) were systematically searched from inception to July 2022. Studies that developed prediction models for perineal lacerations or performed external validation of existing models were considered eligible to include in the systematic review. Two reviewers independently conducted data extraction according to the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies. The risk of bias and the applicability of the included models were assessed with the Prediction Model Risk of Bias Assessment Tool. A narrative synthesis was performed to summarize the characteristics, risk of bias, and performance of existing models.
RESULTS
Of 4345 retrieved studies, 14 studies with 22 prediction models for perineal lacerations were included. The included models mainly aimed to estimate the risk of third- and fourth-degree perineal lacerations. The top five predictors used were operative vaginal birth (72.7 %), parity/previous vaginal birth (63.6 %), race/ethnicity (59.1 %), maternal age (50.0 %), and episiotomy (40.1 %). Internal and external validation was performed in 12 (54.5 %) and seven (31.8 %) models, respectively. 13 studies (92.9 %) assessed model discrimination, with the c-index ranging from 0.636 to 0.830. Seven studies (50.0 %) evaluated the model calibration using the Hosmer-Lemeshow test, Brier score, or calibration curve. The results indicated that most of the models had fairly good calibration. All the included models were at higher risk of bias mainly due to unclear or inappropriate methods for handling missing data and continuous predictors, external validation, and model performance evaluation. Six models (27.3 %) showed low concerns about applicability.
CONCLUSIONS
The existing models for perineal lacerations were poorly validated and evaluated, among which only two have the potential for clinical use: one for women undergoing vaginal birth after cesarean delivery, and the other one for all women undergoing vaginal birth. Future studies should focus on robust external validation of existing models and the development of novel models for second-degree perineal laceration.
PROSPERO REGISTRATION NUMBER
CRD42022349786.
TWEETABLE ABSTRACT
The existing models for perineal lacerations during childbirth need external validation and updating. Tools are needed for second-degree perineal laceration.
Topics: Female; Humans; Pregnancy; Delivery, Obstetric; Episiotomy; Lacerations; Parity; Perineum; Risk Factors
PubMed: 37423201
DOI: 10.1016/j.ijnurstu.2023.104546 -
Frontiers in Immunology 2023To identify the risk factors associated with prognosis in patients with hepatocellular carcinoma (HCC) treated with immune checkpoint inhibitors (ICI) via meta-analysis.... (Meta-Analysis)
Meta-Analysis
Development and validation of prognostic risk prediction models for hepatocellular carcinoma patients treated with immune checkpoint inhibitors based on a systematic review and meta-analysis of 47 cohorts.
OBJECTIVE
To identify the risk factors associated with prognosis in patients with hepatocellular carcinoma (HCC) treated with immune checkpoint inhibitors (ICI) via meta-analysis. And to construct prediction models to aid in the prediction and improvement of prognosis.
METHODS
We searched PubMed, Embase, Web of Science and Cochrane Library for relevant studies from inception to March 29, 2023. After completing literature screening and data extraction, we performed meta-analysis, sensitivity analysis, and subgroup analysis to identify risk factors associated with OS and PFS. Using the pooled hazard ratio value for each risk factor, we constructed prediction models, which were then validated using datasets from 19 centers in Japan and two centers in China, comprising a total of 204 patients.
RESULTS
A total of 47 studies, involving a total of 7649 ICI-treated HCC patients, were included in the meta-analysis. After analyzing 18 risk factors, we identified AFP, ALBI, NLR, ECOG performance status, Child-Pugh stage, BCLC stage, tumor number, vascular invasion and combination therapy as predictors for OS prediction model, while AFP, ALBI, NLR, ECOG performance status, Child-Pugh stage, BCLC stage, tumor number and vascular invasion were selected as predictors for PFS model. To validate the models, we scored two independent cohorts of patients using both prediction models. Our models demonstrated good performance in these cohorts. In addition, in the pooled cohort of 204 patients, Our models also showed good performance with area under the curve (AUC) values of 0.712, 0.753, and 0.822 for the OS prediction model at 1-year, 2-year, and 3-year follow-up points, respectively, and AUC values of 0.575, 0.749 and 0.691 for the PFS prediction model Additionally, the calibration curve, decision curve analysis, and Kaplan-Meier curves in the pooled cohort all supported the validity of both models.
CONCLUSION
Based on the meta-analysis, we successfully constructed the OS and PFS prediction models for ICI-treated HCC patients. We also validated the models externally and observed good discrimination and calibration. The model's selected indicators are easily obtainable, making them suitable for further application in clinical practice.
Topics: Humans; Carcinoma, Hepatocellular; Prognosis; Immune Checkpoint Inhibitors; Liver Neoplasms; alpha-Fetoproteins
PubMed: 37520554
DOI: 10.3389/fimmu.2023.1215745