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European Journal of Clinical... Jul 2023Galectins are β-galactoside-binding proteins. Galectin-4 has shown an effect on cancer progression/metastasis, especially in cancers of the digestive system. This can... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Galectins are β-galactoside-binding proteins. Galectin-4 has shown an effect on cancer progression/metastasis, especially in cancers of the digestive system. This can be attributed to altered glycosylation pattern of cell membrane molecules, which is a characteristic attribute of oncogenesis. The aim of this paper is to systematically review galectin-4 in different cancers and its role in disease progression.
METHODS
The study was designed on the basis of Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. PubMed, Scopus, Web of Science, and Science Direct were used to search relevant literature with keywords "galectin-4 AND cancer", "galectin-4", "LGALS4", and "LGALS4 AND cancer". Inclusion criteria for study selection were availability of full-text articles, articles in English language and articles relevant to current topic, that is, galectin-4 and cancer. Exclusion criteria were studies that investigated other disease conditions, interventions unrelated to cancer or galectin-4 and bias outcome.
RESULTS
A total of 73 articles were retrieved after removing duplication from databases, out of which 40 studies were included in the review that followed the inclusion criteria, including low to moderate bias. These included 23 studies in digestive system, 5 in reproductive system, 4 in respiratory system, and 2 in brain and urothelial cancers.
CONCLUSIONS
A differential expression of galectin-4 was observed in different cancer stages/ and types. Furthermore, galectin-4 was found to modulate disease progression. A meta-analysis and comprehensive mechanistic studies, pertaining to different aspects of galectin-4 biology, could give statistically driven correlations, elucidating multifaceted role of galectin-4 in cancer.
Topics: Humans; Galectin 4; Neoplasms; Galectins; Bias; Disease Progression
PubMed: 36932875
DOI: 10.1111/eci.13987 -
Respiratory Medicine Oct 2022There are conflicting reports on the results of several of the latest clinical trials related to the use of baricitinib in the management of COVID-19 patients. The aim... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
There are conflicting reports on the results of several of the latest clinical trials related to the use of baricitinib in the management of COVID-19 patients. The aim of the current systematic review and meta-analysis was to evaluate the efficacy of baricitinib in COVID-19 patients.
METHODS
Databases like ScienceDirect, PubMed/Medline, Publons, Google Scholar and other sources like ClinicalTrials.gov, Cochrane, medRxiv, Research Square and reference lists were thoroughly searched.
RESULTS
Fifteen (15) articles which met the inclusion criteria were qualitatively and quantitatively analysed. Based on Cochrane and Newcastle-Ottawa Scale (NOS) risk of bias (RoB) analyses, 14/15 articles are grouped as high-quality. Meta-analyses revealed that randomised control trials (RCTs) and non-randomised control trials (nRCTs) statistically significantly reduced the mortality rate in COVID-19 patients, with a risk ratio (RR) in the fixed-effect model was RR = 0.64 [95% CI: 0.51 to 0.79; p < 0.0001] and RR = 0.58 [95% CI: 0.45 to 0.73; p < 0.00001], respectively, with insignificant heterogeneity and no publication bias found. For block/reduce disease progression (BDP), baricitinib did not statistically significantly reduce disease progression for RCTs. The RR in the random effect model was RR = 0.80 [95% CI: 0.58 to 1.10: p = 0.17], with significant heterogeneity, where I was 60%. On the other hand, baricitinib statistically significantly reduced disease progression in nRCTs, as the RR of the fixed effect model was RR = 0.54 [95% CI: 0.37 to 0.78; p = 0.001] with insignificant heterogeneity.
CONCLUSION
The current meta-analyses revealed that baricitinib statistically significantly reduced mortality rate and disease progression in COVID-19 patients.
PROSPERO REGISTRATION NUMBER
CRD42021281556.
Topics: Azetidines; Disease Progression; Humans; Purines; Pyrazoles; SARS-CoV-2; Sulfonamides; COVID-19 Drug Treatment
PubMed: 36150282
DOI: 10.1016/j.rmed.2022.106986 -
Neurology Sep 2021
Topics: Aging; Alzheimer Disease; Disease Progression; Humans; Research Personnel
PubMed: 34266916
DOI: 10.1212/WNL.0000000000012500 -
Biostatistics (Oxford, England) Dec 2023Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest...
Current diagnosis of neurological disorders often relies on late-stage clinical symptoms, which poses barriers to developing effective interventions at the premanifest stage. Recent research suggests that biomarkers and subtle changes in clinical markers may occur in a time-ordered fashion and can be used as indicators of early disease. In this article, we tackle the challenges to leverage multidomain markers to learn early disease progression of neurological disorders. We propose to integrate heterogeneous types of measures from multiple domains (e.g., discrete clinical symptoms, ordinal cognitive markers, continuous neuroimaging, and blood biomarkers) using a hierarchical Multilayer Exponential Family Factor (MEFF) model, where the observations follow exponential family distributions with lower-dimensional latent factors. The latent factors are decomposed into shared factors across multiple domains and domain-specific factors, where the shared factors provide robust information to perform extensive phenotyping and partition patients into clinically meaningful and biologically homogeneous subgroups. Domain-specific factors capture remaining unique variations for each domain. The MEFF model also captures nonlinear trajectory of disease progression and orders critical events of neurodegeneration measured by each marker. To overcome computational challenges, we fit our model by approximate inference techniques for large-scale data. We apply the developed method to Parkinson's Progression Markers Initiative data to integrate biological, clinical, and cognitive markers arising from heterogeneous distributions. The model learns lower-dimensional representations of Parkinson's disease (PD) and the temporal ordering of the neurodegeneration of PD.
Topics: Humans; Disease Progression; Parkinson Disease; Biomarkers; Neuroimaging
PubMed: 36124992
DOI: 10.1093/biostatistics/kxac042 -
IEEE Transactions on Bio-medical... Aug 2021Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across...
Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.
Topics: Disease Progression; Forecasting; Humans; Neural Networks, Computer; Phenotype
PubMed: 33259292
DOI: 10.1109/TBME.2020.3041815 -
Annals of Neurology Sep 2020
Topics: Disease Progression; Humans; Multiple Sclerosis; Multiple Sclerosis, Chronic Progressive
PubMed: 32628321
DOI: 10.1002/ana.25802 -
Emerging Infectious Diseases Jul 2021We investigated outcomes for patients born after 1983 and hospitalized with initial acute rheumatic fever (ARF) in New Zealand during 1989-2012. We linked ARF...
We investigated outcomes for patients born after 1983 and hospitalized with initial acute rheumatic fever (ARF) in New Zealand during 1989-2012. We linked ARF progression outcome data (recurrent hospitalization for ARF, hospitalization for rheumatic heart disease [RHD], and death from circulatory causes) for 1989-2015. Retrospective analysis identified initial RHD patients <40 years of age who were hospitalized during 2010-2015 and previously hospitalized for ARF. Most (86.4%) of the 2,182 initial ARF patients did not experience disease progression by the end of 2015. Progression probability after 26.8 years of theoretical follow-up was 24.0%; probability of death, 1.0%. Progression was more rapid and ≈2 times more likely for indigenous Māori or Pacific Islander patients. Of 435 initial RHD patients, 82.2% had not been previously hospitalized for ARF. This young cohort demonstrated low mortality rates but considerable illness, especially among underserved populations. A national patient register could help monitor, prevent, and reduce ARF progression.
Topics: Disease Progression; Humans; New Zealand; Retrospective Studies; Rheumatic Fever; Rheumatic Heart Disease
PubMed: 34153221
DOI: 10.3201/eid2707.203045 -
Cancer Cell Dec 2020Using targeted single-cell DNA sequencing approaches, two articles in Nature and Nature Communications have now firmly established that acute myeloid leukemia is a...
Using targeted single-cell DNA sequencing approaches, two articles in Nature and Nature Communications have now firmly established that acute myeloid leukemia is a highly dynamic oligoclonal disease. Clonal evolution during disease progression and therapy occurs in both linear and branched trajectories, with a clear order of mutational events.
Topics: Clonal Evolution; Disease Progression; Humans; Leukemia, Myeloid, Acute; Mutation; Sequence Analysis, DNA
PubMed: 33321087
DOI: 10.1016/j.ccell.2020.11.011 -
American Journal of Respiratory Cell... Apr 2022
Topics: Chemokine CCL17; Chemokine CCL22; Disease Progression; Humans; Ligands; Pulmonary Disease, Chronic Obstructive
PubMed: 35133243
DOI: 10.1165/rcmb.2021-0518ED -
Psycho-oncology Jun 2022Among patients living with advanced, life-limiting illness, reconciling the prospect of disease progression with future goals and expectations is a key psychological...
OBJECTIVE
Among patients living with advanced, life-limiting illness, reconciling the prospect of disease progression with future goals and expectations is a key psychological task, integral to treatment decision-making and emotional well-being. To date, this psychological process remains poorly understood with no available measurement tools. The present paper develops and validates a measurement model for operationalizing this psychological process.
METHODS
In Phase 1, concept elicitation interviews were conducted among Stage IV lung, gastrointestinal, and gynecologic cancer patients, their caregivers, and experts (N = 19), to further develop our conceptual framework centered on assimilation and accommodation coping. In Phase 2, draft self-report items of common assimilation and accommodation coping strategies were evaluated via patient cognitive interviews (N = 11).
RESULTS
Phase 1 interviews identified several coping strategies, some of which aimed to reduce the perceived likelihood of disease progression (assimilation), and others aimed to integrate the likelihood into new goals and expectations (accommodation). The coping strategies appeared to manifest in patients' daily lives, and integrally related to their emotional well-being and how they think about treatments. Phase 2 cognitive interviews identified items to remove and modify, resulting in a 31-item measure assessing 10 assimilation and accommodation coping strategies.
CONCLUSIONS
The present work derived a content-valid measure of the psychological process by which patients reconcile the prospect of disease progression with their goals and expectations. Further psychometric validation and use of the scale could identify intervention targets for enhancing patient decision-making and well-being.
Topics: Disease Progression; Female; Goals; Humans; Motivation; Neoplasms; Surveys and Questionnaires
PubMed: 34984756
DOI: 10.1002/pon.5878