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Reproductive Biology and Endocrinology... Jul 2023Ovarian hyperstimulation syndrome (OHSS) is a potentially life-threating iatrogenic complication of the early luteal phase and/or early pregnancy after in vitro... (Meta-Analysis)
Meta-Analysis Review
Ovarian hyperstimulation syndrome (OHSS) is a potentially life-threating iatrogenic complication of the early luteal phase and/or early pregnancy after in vitro fertilization (IVF) treatment. The aim of the current study was to identify the most effective methods for preventing of and reducing the incidence and severity of OHSS in IVF patients. A systematic review of systematic reviews of randomized controlled trials (RCTs) with meta-analysis was used to assess each potential intervention (PROSPERO website, CRD 268626) and only studies with the highest quality were included in the qualitative analysis. Primary outcomes included prevention and reduction of OHSS incidence and severity. Secondary outcomes were maternal death, incidence of hospital admission, days of hospitalization, and reproductive outcomes, such as incidence of live-births, clinical pregnancies, pregnancy rate, ongoing pregnancy, miscarriages, and oocytes retrieved. A total of specific interventions related to OHSS were analyzed in 28 systematic reviews of RCTs with meta-analyses. The quality assessment of the included studies was high, moderate, and low for 23, 2, and 3 studies, respectively. The certainty of evidence (CoE) for interventions was reported for 37 specific situations/populations and resulted high, moderate, and low-to-very low for one, 5, and 26 cases, respectively, while it was not reported in 5 cases. Considering the effective interventions without deleterious reproductive effects, GnRH-ant co-treatment (36 RCTs; OR 0.61, 95% C 0.51 to 0.72, n = 7,944; I = 31%) and GnRH agonist triggering (8 RCTs; OR 0.15, 95% CI 0.05 to 0.47, n = 989; I = 42%) emerged as the most effective interventions for preventing OHSS with a moderate CoE, even though elective embryo cryopreservation exhibited a low CoE. Furthermore, the use of mild ovarian stimulation (9 RCTs; RR 0.26, CI 0.14 to 0.49, n = 1,925; I = 0%), and dopaminergic agonists (10 RCTs; OR 0.32, 95% CI 0.23 to 0.44, n = 1,202; I = 13%) coadministration proved effective and safe with a moderate CoE. In conclusion, the current study demonstrates that only a few interventions currently can be considered effective to reduce the incidence of OHSS and its severity with high/moderate CoE despite the numerous published studies on the topic. Further well-designed RCTs are needed, particularly for GnRH-a down-regulated IVF cycles.
Topics: Female; Humans; Pregnancy; Fertilization in Vitro; Gonadotropin-Releasing Hormone; Incidence; Ovarian Hyperstimulation Syndrome; Systematic Reviews as Topic
PubMed: 37480081
DOI: 10.1186/s12958-023-01113-6 -
Cureus Sep 2023Due to the increased burden of chronic medical conditions in recent years, artificial intelligence (AI) is suggested in the medical field to optimize health care.... (Review)
Review
Due to the increased burden of chronic medical conditions in recent years, artificial intelligence (AI) is suggested in the medical field to optimize health care. Physicians could implement these automated problem-solving tools for their benefit, reducing their workload, assisting in diagnostics, and supporting clinical decision-making. These tools are being considered for future medical assistance in real life. A literature review was performed to assess the impact of AI on the patient population with chronic medical conditions, using standardized guidelines. A MeSH strategy was created, and the database was searched for appropriate studies using specific inclusion and exclusion criteria. The online database yielded 93 results from various databases, of which 10 moderate to high-quality studies were selected to be included in our systematic review after removing the duplicates, screening titles, and articles. Of the 10 studies, nine recommended using AI after considering the potential limitations such as privacy protection, medicolegal implications, and psychosocial aspects. Due to its non-fatigable nature, AI was found to be of immense help in image recognition. It was also found to be valuable in various disciplines related to administration, physician burden, and patient adherence. The newer technologies of Chatbots and eHealth applications are of great help when used safely and effectively after proper patient education. After a careful review conducted by our team members, it is safe to conclude that implementing AI in daily clinical practice could potentiate the cognitive ability of physicians and decrease the workload through various automated technologies such as image recognition, speech recognition, and voice recognition due to its unmatchable speed and non-fatigable nature when compared to clinicians. Despite its vast benefits to the medical field, a few limitations could hinder its effective implementation into real-life practice, which requires enormous research and strict regulations to support its role as a physician's aid. However, AI should only be used as a medical support system, in order to improve the primary outcomes such as reducing waiting time, healthcare costs, and workload. AI should not be meant to replace physicians.
PubMed: 37900468
DOI: 10.7759/cureus.46066 -
International Journal of Molecular... Oct 2023Amyotrophic lateral sclerosis is a devastating neurodegenerative disease characterized by the gradual loss of motor neurons in the brain and spinal cord, leading to... (Review)
Review
Amyotrophic lateral sclerosis is a devastating neurodegenerative disease characterized by the gradual loss of motor neurons in the brain and spinal cord, leading to progressive motor function decline. Unfortunately, there is no effective treatment, and its increasing prevalence is linked to an aging population, improved diagnostics, heightened awareness, and changing lifestyles. In the gastrointestinal system, the gut microbiota plays a vital role in producing metabolites, neurotransmitters, and immune molecules. Short-chain fatty acids, of interest for their potential health benefits, are influenced by a fiber- and plant-based diet, promoting a diverse and balanced gut microbiome. These fatty acids impact the body by binding to receptors on enteroendocrine cells, influencing hormones like glucagon-like peptide-1 and peptide YY, which regulate appetite and insulin sensitivity. Furthermore, these fatty acids impact the blood-brain barrier, neurotransmitter levels, and neurotrophic factors, and directly stimulate vagal afferent nerves, affecting gut-brain communication. The vagus nerve is a crucial link between the gut and the brain, transmitting signals related to appetite, inflammation, and various processes. Dysregulation of this pathway can contribute to conditions like obesity and irritable bowel syndrome. Emerging evidence suggests the complex interplay among these fatty acids, the gut microbiota, and environmental factors influences neurodegenerative processes via interconnected pathways, including immune function, anti-inflammation, gut barrier, and energy metabolism. Embracing a balanced, fiber-rich diet may foster a diverse gut microbiome, potentially impacting neurodegenerative disease risk. Comprehensive understanding requires further research into interventions targeting the gut microbiome and fatty acid production and their potential therapeutic role in neurodegeneration.
Topics: Humans; Aged; Gastrointestinal Microbiome; Amyotrophic Lateral Sclerosis; Neurodegenerative Diseases; Brain; Fatty Acids, Volatile; Fatty Acids
PubMed: 37894774
DOI: 10.3390/ijms242015094 -
JMIR Medical Informatics May 2024With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and... (Review)
Review
BACKGROUND
With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly used in disease detection and prediction, including for Parkinson disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world use. In particular, a lack of external validity may result in poor performance of these systems in clinical practice. Additional methodological issues in ML design and reporting can also hinder clinical adoption, even for applications that would benefit from such data-driven systems.
OBJECTIVE
To sample the current ML practices in PD applications, we conducted a systematic review of studies published in 2020 and 2021 that used ML models to diagnose PD or track PD progression.
METHODS
We conducted a systematic literature review in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines in PubMed between January 2020 and April 2021, using the following exact string: "Parkinson's" AND ("ML" OR "prediction" OR "classification" OR "detection" or "artificial intelligence" OR "AI"). The search resulted in 1085 publications. After a search query and review, we found 113 publications that used ML for the classification or regression-based prediction of PD or PD-related symptoms.
RESULTS
Only 65.5% (74/113) of studies used a holdout test set to avoid potentially inflated accuracies, and approximately half (25/46, 54%) of the studies without a holdout test set did not state this as a potential concern. Surprisingly, 38.9% (44/113) of studies did not report on how or if models were tuned, and an additional 27.4% (31/113) used ad hoc model tuning, which is generally frowned upon in ML model optimization. Only 15% (17/113) of studies performed direct comparisons of results with other models, severely limiting the interpretation of results.
CONCLUSIONS
This review highlights the notable limitations of current ML systems and techniques that may contribute to a gap between reported performance in research and the real-life applicability of ML models aiming to detect and predict diseases such as PD.
PubMed: 38771237
DOI: 10.2196/50117 -
Cartilage Dec 2023There are many intra-articular hyaluronic acid (IA-HA) products on the market that have known intrinsic differences in molecular size, source, and structure. The current...
INTRODUCTION
There are many intra-articular hyaluronic acid (IA-HA) products on the market that have known intrinsic differences in molecular size, source, and structure. The current review summarizes existing evidence describing and assessing these differences, while also identifying whether these differences have an impact on clinical outcomes.
METHODS
This systematic review summarized all literature that specifically addresses IA-HA product differences. Included studies summarized basic science and mechanism of action comparisons of IA-HA product differences, or systematic reviews that assess differences in clinical outcomes between IA-HA product differences.
RESULTS
A total of 20 investigations assessed basic science differences between IA-HA products, while 20 investigations provided assessments of the clinical outcome differences between IA-HA product characteristics. The published basic science literature provided a differentiation between low molecular weight (LMW) and high molecular weight (HMW) HA with regard to changes within the synovial fluid, driven by the interactions that these molecules have with receptors in the joint space. These differences in receptor interaction manifest within clinical outcomes, as meta-analyses comparing pain relief after IA-HA suggest that pain reduction is superior in patients who receive HMW HA as opposed to LMW HA.
CONCLUSION
This review highlights differences between IA-HA characteristics, and how important the molecular weight, derivation of the product, and structure are to variances in reported clinical outcomes to treat osteoarthritis (OA) of the knee. HMW IA-HAs have shown greater efficacy compared to the alternative of LMW products, while avian-derived and cross-linked products have potentially demonstrated an increase in inflammatory events over non-avian-derived, non-cross-linked HAs.
Topics: Humans; Hyaluronic Acid; Osteoarthritis, Knee; Viscosupplements; Injections, Intra-Articular; Pain
PubMed: 37314014
DOI: 10.1177/19476035231154530 -
International Journal of Molecular... Jul 2023The observation of neurogenic fever resulting from subarachnoid hemorrhage (SAH) in animal models is a useful tool for the interpretation of its pathophysiology in... (Review)
Review
The observation of neurogenic fever resulting from subarachnoid hemorrhage (SAH) in animal models is a useful tool for the interpretation of its pathophysiology in humans, which is still a major challenge in the management of neurocritical patients. This systematic review aims to identify the prognostic factors and pathophysiological elements that determine the onset of neurogenic fever and its severity in animal models. In addition, our study aims to analyze which pharmacological treatments are most effective. All the articles available in Pubmed, Embase, and the Biological Science Collection until August 2021 concerning in vivo experimental studies on SAH animal models, including full texts and abstracts written in English and Italian, were considered. The risk of bias was assessed with SYRCLE's Risk of Bias tool. In total, 81 records were retrieved; after excluding duplicates, 76 records were potentially relevant. A total of 64 articles was excluded after title and abstract screening. The remaining 12 studies were evaluated as full texts, and 6 other studies were excluded (SAH-induced animal studies without a body temperature assessment). In one study, body temperature was measured after SAH induction, but the authors did not report temperature recording. Therefore, only five studies met the search criteria. The high methodological heterogeneity (different animal species, different temperature measurement methods, and different methods of the induction of bleeding) prevented meta-analysis. Synthesis methodology without meta-analysis (SWiM) was used for data analysis. The total number of animals used as controls was 87 (23 rabbits, 32 mice, and 32 rats), while there were 130 animals used as interventions (54 rabbits, 44 mice, and 32 rats). The presence of blood in the subarachnoid space, particularly red blood cells, is responsible for neurogenic fever; the role of hemoglobin is unclear. The mechanism is apparently not mediated by prostaglandins. The autonomic nervous system innervating brown adipose tissue is undoubtedly implicated in the onset of neurogenic fever. The activation of the central adenosine-1 receptor is effective in controlling the temperature of animals with neurogenic fever (by inhibiting thermogenesis of brown adipose tissue).
Topics: Humans; Rats; Mice; Rabbits; Animals; Subarachnoid Hemorrhage; Autonomic Nervous System; Disease Models, Animal
PubMed: 37511267
DOI: 10.3390/ijms241411514 -
The Journal of Allergy and Clinical... May 2024Poor adherence to asthma and chronic obstructive pulmonary disease maintenance therapies impairs health outcomes. Proven and cost-effective programs to promote adherence...
Cost-Effectiveness and Impact on Health Care Utilization of Interventions to Improve Medication Adherence and Outcomes in Asthma and Chronic Obstructive Pulmonary Disease: A Systematic Literature Review.
BACKGROUND
Poor adherence to asthma and chronic obstructive pulmonary disease maintenance therapies impairs health outcomes. Proven and cost-effective programs to promote adherence and persistence are not yet in regular widespread use. Implementation costs are a potential barrier to uptake of such programs.
OBJECTIVE
We undertook a systematic literature review and narrative synthesis of studies investigating the cost-effectiveness of treatment adherence-promoting programs or that determined their impact on health care budget directly or via health care resource use (HCRU).
METHODS
We identified relevant publications using Medline and PreMEDLINE (PubMed), Embase (Embase.com, Elsevier), and EconLit for publications between January 2000 and July 2021. We also searched clinical trial databases and selected conference proceedings.
RESULTS
Of 1,910 potentially relevant articles, 26 met prespecified inclusion criteria and underwent data extraction. Eleven reported a direct assessment of adherence, 15 included economic evaluations, and 17 described HCRU. None included an analysis of biologic medication use. When they were studied, interventions were often found to be highly cost-effective, with dominant incremental cost-effectiveness ratios in some cases. Reductions in direct costs and HCRU (health care visits, hospital admissions, and/or the use of medications, including add-on/reliever treatment and antibiotics) were frequently reported. Reported use of maintenance treatments improved in some studies. Counseling and/or digitally informed programs were used in all cases in which favorable outcomes were observed.
CONCLUSIONS
Adherence-promoting interventions are mostly cost-effective and often result in reduced HCRU and associated costs. Multidisciplinary care involving one-to-one advice and digitally enhanced communications appear to offer the greatest benefit.
Topics: Humans; Cost-Benefit Analysis; Asthma; Pulmonary Disease, Chronic Obstructive; Medication Adherence; Patient Acceptance of Health Care
PubMed: 38182099
DOI: 10.1016/j.jaip.2023.12.049 -
Frontiers in Pharmacology 2023Older patients with dementia always need multiple drugs due to comorbidities and cognitive impairment, further complicating drug treatment and increasing the risk of...
Older patients with dementia always need multiple drugs due to comorbidities and cognitive impairment, further complicating drug treatment and increasing the risk of potentially inappropriate medication. The objective of our study is to estimate the global prevalence of polypharmacy and potentially inappropriate medication (PIM) and explore the factors of PIM for older patients with dementia. We searched PubMed, Embase (Ovid), and Web of Science databases to identify eligible studies from inception to 16 June 2023. We conducted a meta-analysis for observational studies reporting the prevalence of potentially inappropriate medication and polypharmacy in older patients with dementia using a random-effect model. The factors associated with PIM were meta-analyzed. Overall, 62 eligible studies were included, of which 53 studies reported the prevalence of PIM and 28 studies reported the prevalence of polypharmacy. The pooled estimate of PIM and polypharmacy was 43% (95% CI 38-48) and 62% (95% CI 52-71), respectively. Sixteen studies referred to factors associated with PIM use, and 15 factors were further pooled. Polypharmacy (2.83, 95% CI 1.80-4.44), diabetes (1.31, 95% CI 1.04-1.65), heart failure (1.17, 95% CI 1.00-1.37), depression (1.45, 95% CI 1.14-1.88), history of cancer (1.20, 95% CI 1.09-1.32), hypertension (1.46, 95% CI 1.05-2.03), ischemic heart disease (1.55, 95% CI 0.77-3.12), any cardiovascular disease (1.11, 95% CI 1.06-1.17), vascular dementia (1.09, 95% CI 1.03-1.16), chronic obstructive pulmonary disease (1.39, 95% CI 1.13-1.72), and psychosis (1.91, 95% CI 1.04-3.53) are positively associated with PIM use. PIM and polypharmacy were highly prevalent in older patients with dementia. Among different regions, the pooled estimate of PIM use and polypharmacy varied widely. Increasing PIM in older patients with dementia was closely associated with polypharmacy. For other comorbidities such as heart failure and diabetes, prescribing should be cautioned.
PubMed: 37693899
DOI: 10.3389/fphar.2023.1221069 -
International Journal of Molecular... Apr 2024Attention-Deficit/Hyperactivity Disorder (ADHD), characterized by clinical diversity, poses diagnostic challenges often reliant on subjective assessments. Metabolomics... (Review)
Review
Attention-Deficit/Hyperactivity Disorder (ADHD), characterized by clinical diversity, poses diagnostic challenges often reliant on subjective assessments. Metabolomics presents an objective approach, seeking biomarkers for precise diagnosis and targeted interventions. This review synthesizes existing metabolomic insights into ADHD, aiming to reveal biological mechanisms and diagnostic potentials. A thorough PubMed and Web of Knowledge search identified studies exploring blood/urine metabolites in ADHD-diagnosed or psychometrically assessed children and adolescents. Synthesis revealed intricate links between ADHD and altered amino acid metabolism, neurotransmitter dysregulation (especially dopamine and serotonin), oxidative stress, and the kynurenine pathway impacting neurotransmitter homeostasis. Sleep disturbance markers, notably in melatonin metabolism, and stress-induced kynurenine pathway activation emerged. Distinct metabolic signatures, notably in the kynurenine pathway, show promise as potential diagnostic markers. Despite limitations like participant heterogeneity, this review underscores the significance of integrated therapeutic approaches targeting amino acid metabolism, neurotransmitters, and stress pathways. While guiding future research, this overview of the metabolomic findings in ADHD suggests directions for precision diagnostics and personalized ADHD interventions.
Topics: Adolescent; Child; Humans; Attention Deficit Disorder with Hyperactivity; Biomarkers; Metabolome; Metabolomics; Neurotransmitter Agents; Oxidative Stress
PubMed: 38673970
DOI: 10.3390/ijms25084385 -
Artificial Intelligence in Medicine Sep 2023DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques... (Review)
Review
BACKGROUND
DNA methylation biomarkers have great potential in improving prognostic classification systems for patients with cancer. Machine learning (ML)-based analytic techniques might help overcome the challenges of analyzing high-dimensional data in relatively small sample sizes. This systematic review summarizes the current use of ML-based methods in epigenome-wide studies for the identification of DNA methylation signatures associated with cancer prognosis.
METHODS
We searched three electronic databases including PubMed, EMBASE, and Web of Science for articles published until 2 January 2023. ML-based methods and workflows used to identify DNA methylation signatures associated with cancer prognosis were extracted and summarized. Two authors independently assessed the methodological quality of included studies by a seven-item checklist adapted from 'A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies (PROBAST)' and from the 'Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK). Different ML methods and workflows used in included studies were summarized and visualized by a sunburst chart, a bubble chart, and Sankey diagrams, respectively.
RESULTS
Eighty-three studies were included in this review. Three major types of ML-based workflows were identified. 1) unsupervised clustering, 2) supervised feature selection, and 3) deep learning-based feature transformation. For the three workflows, the most frequently used ML techniques were consensus clustering, least absolute shrinkage and selection operator (LASSO), and autoencoder, respectively. The systematic review revealed that the performance of these approaches has not been adequately evaluated yet and that methodological and reporting flaws were common in the identified studies using ML techniques.
CONCLUSIONS
There is great heterogeneity in ML-based methodological strategies used by epigenome-wide studies to identify DNA methylation markers associated with cancer prognosis. In theory, most existing workflows could not handle the high multi-collinearity and potentially non-linearity interactions in epigenome-wide DNA methylation data. Benchmarking studies are needed to compare the relative performance of various approaches for specific cancer types. Adherence to relevant methodological and reporting guidelines are urgently needed.
Topics: Humans; DNA Methylation; Epigenome; Prognosis; Neoplasms; Machine Learning
PubMed: 37673571
DOI: 10.1016/j.artmed.2023.102589