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Journal of Affective Disorders Dec 2021Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD) assessment. The development of digital phenotyping promises to improve BD management. We... (Review)
Review
BACKGROUND
Bias-prone psychiatric interviews remain the mainstay of bipolar disorder (BD) assessment. The development of digital phenotyping promises to improve BD management. We present a systematic review of the evidence about the use of portable digital devices for the identification of BD, BD types and BD mood states and for symptom assessment.
METHODS
We searched Web of Knowledge, Scopus ®, IEEE Xplore, and ACM Digital Library databases (until 5/1/2021) for articles evaluating the use of portable/wearable digital devices, such as smartphone apps, wearable sensors, audio and/or visual recordings, and multimodal tools. The protocol is registered in PROSPERO (CRD42020200086).
RESULTS
We included 62 studies (2325 BD; 724 healthy controls, HC): 27 using smartphone apps, either for recording self-assessments (n = 10) or for passively gathering metadata (n = 7) or both (n = 10); 15 using wearable sensors for physiological parameters; 17 analysing audio and/or video recordings; 3 using multiple technologies. Two thirds of the included studies applied artificial intelligence (AI)-based approaches. They achieved fair to excellent classification performances.
LIMITATIONS
The included studies had small sample sizes and marked heterogeneity. Evidence of overfitting emerged, limiting generalizability. The absence of clear guidelines about reporting classification performances, with no shared standard metrics, makes results hardly interpretable and comparable.
CONCLUSIONS
New technologies offer a noteworthy opportunity to BD digital phenotyping with objectivity and high granularity. AI-based models could deliver important support in clinical decision-making. Further research and cooperation between different stakeholders are needed for addressing methodological, ethical and socio-economic considerations.
Topics: Artificial Intelligence; Bipolar Disorder; Humans; Self-Assessment; Wearable Electronic Devices
PubMed: 34488086
DOI: 10.1016/j.jad.2021.08.052 -
PloS One 2023Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and...
Several studies applying Machine Learning to deception detection have been published in the last decade. A rich and complex set of settings, approaches, theories, and results is now available. Therefore, one may find it difficult to identify trends, successful paths, gaps, and opportunities for contribution. The present literature review aims to provide the state of research regarding deception detection with Machine Learning. We followed the PRISMA protocol and retrieved 648 articles from ACM Digital Library, IEEE Xplore, Scopus, and Web of Science. 540 of them were screened (108 were duplicates). A final corpus of 81 documents has been summarized as mind maps. Metadata was extracted and has been encoded as Python dictionaries to support a statistical analysis scripted in Python programming language, and available as a collection of Jupyter Lab Notebooks in a GitHub repository. All are available as Jupyter Lab Notebooks. Neural Networks, Support Vector Machines, Random Forest, Decision Tree and K-nearest Neighbor are the five most explored techniques. The studies report a detection performance ranging from 51% to 100%, with 19 works reaching accuracy rate above 0.9. Monomodal, Bimodal, and Multimodal approaches were exploited and achieved various accuracy levels for detection. Bimodal and Multimodal approaches have become a trend over Monomodal ones, although there are high-performance examples of the latter. Studies that exploit language and linguistic features, 75% are dedicated to English. The findings include observations of the following: language and culture, emotional features, psychological traits, cognitive load, facial cues, complexity, performance, and Machine Learning topics. We also present a dataset benchmark. Main conclusions are that labeled datasets from real-life data are scarce. Also, there is still room for new approaches for deception detection with Machine Learning, especially if focused on languages and cultures other than English-based. Further research would greatly contribute by providing new labeled and multimodal datasets for deception detection, both for English and other languages.
Topics: Neural Networks, Computer; Research Design; Publications; Machine Learning; Deception
PubMed: 36757928
DOI: 10.1371/journal.pone.0281323 -
Malaria Journal Jan 2021Malaria and HIV are two important public health issues. However, evidence on HIV-Plasmodium vivax co-infection (HIV/PvCo) is scarce, with most of the available...
BACKGROUND
Malaria and HIV are two important public health issues. However, evidence on HIV-Plasmodium vivax co-infection (HIV/PvCo) is scarce, with most of the available information related to Plasmodium falciparum on the African continent. It is unclear whether HIV can change the clinical course of vivax malaria and increase the risk of complications. In this study, a systematic review of HIV/PvCo studies was performed, and recent cases from the Brazilian Amazon were included.
METHODS
Medical records from a tertiary care centre in the Western Brazilian Amazon (2009-2018) were reviewed to identify HIV/PvCo hospitalized patients. Demographic, clinical and laboratory characteristics and outcomes are reported. Also, a systematic review of published studies on HIV/PvCo was conducted. Metadata, number of HIV/PvCo cases, demographic, clinical, and outcome data were extracted.
RESULTS
A total of 1,048 vivax malaria patients were hospitalized in the 10-year period; 21 (2.0%) were HIV/PvCo cases, of which 9 (42.9%) had AIDS-defining illnesses. This was the first malaria episode in 11 (52.4%) patients. Seven (33.3%) patients were unaware of their HIV status and were diagnosed on hospitalization. Severe malaria was diagnosed in 5 (23.8%) patients. One patient died. The systematic review search provided 17 articles (12 cross-sectional or longitudinal studies and 5 case report studies). A higher prevalence of studies involved cases in African and Asian countries (35.3 and 29.4%, respectively), and the prevalence of reported co-infections ranged from 0.1 to 60%.
CONCLUSION
Reports of HIV/PvCo are scarce in the literature, with only a few studies describing clinical and laboratory outcomes. Systematic screening for both co-infections is not routinely performed, and therefore the real prevalence of HIV/PvCo is unknown. This study showed a low prevalence of HIV/PvCo despite the high prevalence of malaria and HIV locally. Even though relatively small, this is the largest case series to describe HIV/PvCo.
Topics: Adolescent; Adult; Aged; Brazil; Child; Coinfection; Female; HIV Infections; HIV-1; Humans; Incidence; Malaria, Vivax; Male; Middle Aged; Plasmodium vivax; Prevalence; Young Adult
PubMed: 33407474
DOI: 10.1186/s12936-020-03518-9 -
PloS One 2018Network meta-analysis (NMA) is a new tool developed to overcome some limitations of pairwise meta-analyses. NMAs provide evidence on more than two comparators... (Review)
Review
BACKGROUND
Network meta-analysis (NMA) is a new tool developed to overcome some limitations of pairwise meta-analyses. NMAs provide evidence on more than two comparators simultaneously. This study aimed to map the characteristics of the published NMAs on drug therapy comparisons.
METHODS
A systematic review of NMAs comparing pharmacological interventions was performed. Searches in Medline (PubMed) and Scopus along with manual searches were conducted. The main characteristics of NMAs were systematically collected: publication metadata, criteria for drug inclusion, statistical methods used, and elements reported. A methodological quality score with 25 key elements was created and applied to the included NMAs. To identify potential trends, the median of the publication year distribution was used as a cut-off.
RESULTS
The study identified 365 NMAs published from 2003 to 2016 in more than 30 countries. Randomised controlled trials were the primary source of data, with only 5% including observational studies, and 230 NMAs used a placebo as a comparator. Less than 15% of NMAs were registered in PROSPERO or a similar system. One third of studies followed PRISMA and less than 9% Cochrane recommendations. Around 30% presented full-search strategies of the systematic review, and 146 NMAs stated the selection criteria for drug inclusion. Over 75% of NMAs presented network plots, but only half described their geometry. Statistical parameters (model fit, inconsistency, convergence) were properly reported by one third of NMAs. Although 216 studies exhibited supplemental material, no data set of primary studies was available. The methodological quality score (mean 13·9; SD 3·8) presented a slightly positive trend over the years.
CONCLUSION
The map of the published NMAs emphasises the potential of this tool to gather evidence in healthcare, but it also identified some weaknesses, especially in the report, which limits its transparency and reproducibility.
Topics: Algorithms; Bayes Theorem; Decision Making; Drug Therapy; Humans; Internet; MEDLINE; Meta-Analysis as Topic; Network Meta-Analysis; Pharmaceutical Preparations; Reproducibility of Results; Research Design; Software
PubMed: 29709028
DOI: 10.1371/journal.pone.0196644 -
MBio Dec 2021High-throughput 16S rRNA sequencing has allowed the characterization of helminth-uninfected (HU) and helminth-infected (HI) gut microbiomes, revealing distinct profiles.... (Meta-Analysis)
Meta-Analysis
High-throughput 16S rRNA sequencing has allowed the characterization of helminth-uninfected (HU) and helminth-infected (HI) gut microbiomes, revealing distinct profiles. However, there have been no qualitative or quantitative syntheses of these studies, which show marked variation in participant age, diet, pathogen of interest, and study location. A predefined minimally biased search strategy identified 23 studies in humans. For each of these studies, we qualitatively addressed the effects of helminth infection on within-individual (alpha) and between-individual (beta) fecal microbiome diversity, infection-associated microbial taxa, the effect of helminth clearance on microbiome composition, microbiome composition as a predictor of infection status or treatment outcome, and treatment-specific effects on the fecal microbiome. Concomitantly, we performed a meta-analysis on a subset of 7 of these studies containing raw, paired-end 16S reads and individual-level metadata, comprising 424 pretreatment or untreated HI individuals and 497 HU controls. After reducing the batch effect and adjusting for age, our data demonstrated that intestinal helminth parasites can alter the host gut microbiome by increasing alpha diversity and promoting taxonomic reassortment and gradient collapse. Most strongly influencing the microbiome composition were the helminths found in the large intestine, Enterobius vermicularis and Trichuris trichiura, suggesting that this influence appears to be specific to soil-transmitted helminths (STH) species and host anatomical niche. In summary, using a large and diverse sample set captured in the meta-analysis, we were able to evaluate the influence of individual helminth species as well as species-species interactions, each of which explained a significant portion of the variation in the microbiome. The gut microbiome has established importance in regulating many aspects of human health, including nutrition and immunity. While many internal and environmental factors are known to influence the microbiome, less is known about the effects of intestinal helminth parasites (worms), which together affect one-sixth of the world's population. Through a comprehensive qualitative systematic review and quantitative meta-analysis of existing literature, we provide strong evidence that helminth infection dynamically shifts the intestinal microbiome structure. Moreover, we demonstrated that such influence seems to be specific to helminth species and host anatomical niche. Our findings suggest that the gut microbiome may underlie some of the pathology associated with intestinal worm infection and support future work to understand the precise nature of the helminth-microbiome relationship.
Topics: Adolescent; Adult; Aged; Animals; Bacteria; Child; Child, Preschool; Dysbiosis; Feces; Female; Gastrointestinal Microbiome; Helminthiasis; Helminths; Humans; Infant; Male; Middle Aged; Phylogeny; Young Adult
PubMed: 34933444
DOI: 10.1128/mBio.02890-21 -
Frontiers in Cellular Neuroscience 2023We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous...
BACKGROUND
We performed a systematic review that identified at least 9,000 scientific papers on PubMed that include immunofluorescent images of cells from the central nervous system (CNS). These CNS papers contain tens of thousands of immunofluorescent neural images supporting the findings of over 50,000 associated researchers. While many existing reviews discuss different aspects of immunofluorescent microscopy, such as image acquisition and staining protocols, few papers discuss immunofluorescent imaging from an image-processing perspective. We analyzed the literature to determine the image processing methods that were commonly published alongside the associated CNS cell, microscopy technique, and animal model, and highlight gaps in image processing documentation and reporting in the CNS research field.
METHODS
We completed a comprehensive search of PubMed publications using Medical Subject Headings (MeSH) terms and other general search terms for CNS cells and common fluorescent microscopy techniques. Publications were found on PubMed using a combination of column description terms and row description terms. We manually tagged the comma-separated values file (CSV) metadata of each publication with the following categories: animal or cell model, quantified features, threshold techniques, segmentation techniques, and image processing software.
RESULTS
Of the almost 9,000 immunofluorescent imaging papers identified in our search, only 856 explicitly include image processing information. Moreover, hundreds of the 856 papers are missing thresholding, segmentation, and morphological feature details necessary for explainable, unbiased, and reproducible results. In our assessment of the literature, we visualized current image processing practices, compiled the image processing options from the top twelve software programs, and designed a road map to enhance image processing. We determined that thresholding and segmentation methods were often left out of publications and underreported or underutilized for quantifying CNS cell research.
DISCUSSION
Less than 10% of papers with immunofluorescent images include image processing in their methods. A few authors are implementing advanced methods in image analysis to quantify over 40 different CNS cell features, which can provide quantitative insights in CNS cell features that will advance CNS research. However, our review puts forward that image analysis methods will remain limited in rigor and reproducibility without more rigorous and detailed reporting of image processing methods.
CONCLUSION
Image processing is a critical part of CNS research that must be improved to increase scientific insight, explainability, reproducibility, and rigor.
PubMed: 37545881
DOI: 10.3389/fncel.2023.1188858 -
Photodiagnosis and Photodynamic Therapy Jun 2022Oral potentially malignant disorders (OPMD) represent a group of lesions with increased risk for malignant transformation. The management of such injuries is based on... (Meta-Analysis)
Meta-Analysis Review
Oral potentially malignant disorders (OPMD) represent a group of lesions with increased risk for malignant transformation. The management of such injuries is based on surgical treatment or detailed follow-up throughout the patient's lifetime. This systematic review and meta-analysis investigated and critically evaluated the use of autofluorescence and fluorescent probes as potential techniques for the early detection of OPMD. A comprehensive search was performed on Pubmed, Scopus, Web of Science and LIVIVO databases. The gray literature was also consulted and included Google Scholar, Proquest and Open gray databases. 2715 articles were retrieved, and after the different stages of critical evaluation, were reduced to 25 articles that fully met the inclusion criteria. VELscope® was the most used equipment for autofluorescence, while aminolevulinic acid (5-ALA) was the main representative of the probes. The meta-analysis performed included 10 articles that used VELscope® as a method to detect oral disorders. A 95% confidence interval (CI) with a p value significance <0.05 was considered as a criterion for the statistical analysis. The combined sensitivity was 74% (CI95 60-76%, p = 0.0001) and the specificity was 57% (CI95 52-60%, p = 0.0000). The inclusion of these adjunct methods in clinical practice is very promising, since they are able to help both the clinician and the specialist in the early detection of potentially malignant oral disorders, favoring a better prognosis. However, it is still necessary to carry out further studies, with the aim of establishing a protocol for use and qualification of results.
Topics: Data Analysis; Early Detection of Cancer; Fluorescent Dyes; Humans; Mouth Diseases; Mouth Neoplasms; Photochemotherapy; Precancerous Conditions
PubMed: 35192945
DOI: 10.1016/j.pdpdt.2022.102764 -
Cardiology 2022Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global...
BACKGROUND
Transparent and robust real-world evidence sources are increasingly important for global health, including cardiovascular (CV) diseases. We aimed to identify global real-world data (RWD) sources for heart failure (HF), acute coronary syndrome (ACS), and atrial fibrillation (AF).
METHODS
We conducted a systematic review of publications with RWD pertaining to HF, ACS, and AF (2010-2018), generating a list of unique data sources. Metadata were extracted based on the source type (e.g., electronic health records, genomics, and clinical data), study design, population size, clinical characteristics, follow-up duration, outcomes, and assessment of data availability for future studies and linkage.
RESULTS
Overall, 11,889 publications were retrieved for HF, 10,729 for ACS, and 6,262 for AF. From these, 322 (HF), 287 (ACS), and 220 (AF) data sources were selected for detailed review. The majority of data sources had near complete data on demographic variables (HF: 94%, ACS: 99%, and AF: 100%) and considerable data on comorbidities (HF: 77%, ACS: 93%, and AF: 97%). The least reported data categories were drug codes (HF, ACS, and AF: 10%) and caregiver involvement (HF: 6%, ACS: 1%, and AF: 1%). Only a minority of data sources provided information on access to data for other researchers (11%) or whether data could be linked to other data sources to maximize clinical impact (20%). The list and metadata for the RWD sources are publicly available at www.escardio.org/bigdata.
CONCLUSIONS
This review has created a comprehensive resource of CV data sources, providing new avenues to improve future real-world research and to achieve better patient outcomes.
Topics: Acute Coronary Syndrome; Atrial Fibrillation; Comorbidity; Heart Failure; Humans; Information Storage and Retrieval
PubMed: 34781301
DOI: 10.1159/000520674 -
Frontiers in Oncology 2022Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define...
Machine learning and semantic analysis are computer-based methods to evaluate complex relationships and predict future perspectives. We used these technologies to define recent, current and future topics in pancreatic cancer research. Publications indexed under the Medical Subject Headings (MeSH) term 'Pancreatic Neoplasms' from January 1996 to October 2021 were downloaded from PubMed. Using the statistical computing language R and the interpreted, high-level, general-purpose programming language Python, we extracted publication dates, geographic information, and abstracts from each publication's metadata for bibliometric analyses. The generative statistical algorithm "latent Dirichlet allocation" (LDA) was applied to identify specific research topics and trends. The unsupervised "Louvain algorithm" was used to establish a network to identify relationships between single topics. A total of 60,296 publications were identified and analyzed. The publications were derived from 133 countries, mostly from the Northern Hemisphere. For the term "pancreatic cancer research", 12,058 MeSH terms appeared 1,395,060 times. Among them, we identified the four main topics "Clinical Manifestation and Diagnosis", "Review and Management", "Treatment Studies", and "Basic Research". The number of publications has increased rapidly during the past 25 years. Based on the number of publications, the algorithm predicted that "Immunotherapy", Prognostic research", "Protein expression", "Case reports", "Gemcitabine and mechanism", "Clinical study of gemcitabine", "Operation and postoperation", "Chemotherapy and resection", and "Review and management" as current research topics. To our knowledge, this is the first study on this subject of pancreatic cancer research, which has become possible due to the improvement of algorithms and hardware.
PubMed: 35419289
DOI: 10.3389/fonc.2022.832385 -
BMC Geriatrics Nov 2023Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering...
Exploration of key drug target proteins highlighting their related regulatory molecules, functional pathways and drug candidates associated with delirium: evidence from meta-data analyses.
BACKGROUND
Delirium is a prevalent neuropsychiatric medical phenomenon that causes serious emergency outcomes, including mortality and morbidity. It also increases the suffering and the economic burden for families and carers. Unfortunately, the pathophysiology of delirium is still unknown, which is a major obstacle to therapeutic development. The modern network-based system biology and multi-omics analysis approach has been widely used to recover the key drug target biomolecules and signaling pathways associated with disease pathophysiology. This study aimed to identify the major drug target hub-proteins associated with delirium, their regulatory molecules with functional pathways, and repurposable drug candidates for delirium treatment.
METHODS
We used a comprehensive proteomic seed dataset derived from a systematic literature review and the Comparative Toxicogenomics Database (CTD). An integrated multi-omics network-based bioinformatics approach was utilized in this study. The STRING database was used to construct the protein-protein interaction (PPI) network. The gene set enrichment and signaling pathways analysis, the regulatory transcription factors and microRNAs were conducted using delirium-associated genes. Finally, hub-proteins associated repurposable drugs were retrieved from CMap database.
RESULTS
We have distinguished 11 drug targeted hub-proteins (MAPK1, MAPK3, TP53, JUN, STAT3, SRC, RELA, AKT1, MAPK14, HSP90AA1 and DLG4), 5 transcription factors (FOXC1, GATA2, YY1, TFAP2A and SREBF1) and 6 microRNA (miR-375, miR-17-5, miR-17-5p, miR-106a-5p, miR-125b-5p, and miR-125a-5p) associated with delirium. The functional enrichment and pathway analysis revealed the cytokines, inflammation, postoperative pain, oxidative stress-associated pathways, developmental biology, shigellosis and cellular senescence which are closely connected with delirium development and the hallmarks of aging. The hub-proteins associated computationally identified repurposable drugs were retrieved from database. The predicted drug molecules including aspirin, irbesartan, ephedrine-(racemic), nedocromil, and guanidine were characterized as anti-inflammatory, stimulating the central nervous system, neuroprotective medication based on the existing literatures. The drug molecules may play an important role for therapeutic development against delirium if they are investigated more extensively through clinical trials and various wet lab experiments.
CONCLUSION
This study could possibly help future research on investigating the delirium-associated therapeutic target biomarker hub-proteins and repurposed drug compounds. These results will also aid understanding of the molecular mechanisms that underlie the pathophysiology of delirium onset and molecular function.
Topics: Humans; Gene Regulatory Networks; Proteomics; MicroRNAs; Transcription Factors; Delirium
PubMed: 37993790
DOI: 10.1186/s12877-023-04457-1