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Frontiers in Neuroscience 2022It is well known that the intestinal bacteria substantially affect physiological processes in many body organs. Especially, through a bidirectional communication called...
INTRODUCTION
It is well known that the intestinal bacteria substantially affect physiological processes in many body organs. Especially, through a bidirectional communication called as gut-microbiota-brain axis, the gut microbiota deeply influences development and function of the nervous system. Hippocampus, as a part of medial temporal lobe, is known to be involved in cognition, emotion, and anxiety. Growing evidence indicates that the hippocampus is a target of the gut microbiota. We used a broad search linking the hippocampus with the gut microbiota and probiotics.
METHODS
All experimental studies and clinical trials published until end of 2021 were reviewed. Influence of the gut microbiota on the behavioral, electrophysiological, biochemical and histological aspects of the hippocampus were evaluated in this review.
RESULTS
The effect of disrupted gut microbiota and probiotic supplements on the microbiota-hippocampus link is also considered. Studies show that a healthy gut microbiota is necessary for normal hippocampus dependent learning and memory and synaptic plasticity. The known current mechanisms are production and modulation of neurotrophins, neurotransmitters and receptors, regulation of intracellular molecular processes, normalizing the inflammatory/anti-inflammatory and oxidative/antioxidant factors, and histological stability of the hippocampus. Activity of the hippocampal neuronal circuits as well as behavioral functions of the hippocampus positively respond to different mixtures of probiotic bacteria.
DISCUSSION
Growing evidence from animal researches indicate a close association between the hippocampus with the gut microbiota and probiotic bacteria as well. However, human studies and clinical trials verifying such a link are scant. Since the most of papers on this topic have been published over the past 3 years, intensive future research awaits.
PubMed: 36620458
DOI: 10.3389/fnins.2022.1065995 -
Diabetes Research and Clinical Practice May 2012A systematic review of studies reporting data on the relationship between diabetic eye disease and cognitive impairment in Type 2 diabetes was conducted. The increase in... (Review)
Review
A systematic review of studies reporting data on the relationship between diabetic eye disease and cognitive impairment in Type 2 diabetes was conducted. The increase in cognitive impairment has mirrored the global increase in diabetes. The aim of the systematic review was to determine the level of association between diabetic retinopathy and cognitive impairment. Item selection, data extraction and critical appraisal were undertaken using standard procedures and independently verified by two researchers. 3 out of 10 potentially relevant studies were included. All studies showed a level of association between diabetic retinopathy and cognitive impairment, suggesting a near threefold increased risk of cognitive impairment in patients with diabetic retinopathy compared to those without. An association of cognitive impairment and severity of diabetic retinopathy was found in males. Diabetic retinopathy was more strongly linked to impairment in the cognitive domains of verbal learning and recent memory. An increased risk of cognitive impairment in patients with diabetic retinopathy was found in the reviewed studies. However, the relationship of severity of diabetic retinopathy and cognitive impairment has not been established. Further studies with standardized measurements for cognitive impairment and diabetic retinopathy are required to delineate this relationship and the role of other factors in this relationship.
Topics: Cognition Disorders; Dementia; Diabetes Mellitus, Type 2; Diabetic Retinopathy; Humans
PubMed: 22154373
DOI: 10.1016/j.diabres.2011.11.010 -
Journal of Child Health Care : For... Jun 2024This study aimed to systematically review evidence of the association between learning disorders, motor function, and primitive reflexes in preschool children. Seven... (Review)
Review
This study aimed to systematically review evidence of the association between learning disorders, motor function, and primitive reflexes in preschool children. Seven databases were systematically searched (EMBASE, CINAHL, Academic Search Complete, Medline, PsycINFO, ScienceDirect, and Cochrane) with no restrictions. Inclusion criteria were full text peer-reviewed articles reporting new empirical data, assessing any two of three phenomena in preschool children: learning disorders, motor function, or primitive reflexes. Intervention studies or studies examining congenital, chromosomal or acquired neurological, or pathological conditions and prematurity were excluded. Included papers ( = 27) were assessed for methodological quality by the Hoy et al. Risk of bias tool. Learning and motor function were assessed in all except two articles and motor deficits found to be associated with speech/language and executive function as well as several areas of academic performance. Three studies included primitive reflexes, with high levels of the asymmetrical tonic neck reflex positively correlated with fine motor skills, "school readiness" and "impulsivity, hyperactivity and inattention." Caution must be used when interpreting the review results due to significant study heterogeneity. Further research is needed to further understand common underlying mechanisms that may inform earlier diagnostic methods for these three phenomena. PROSPERO: CRD42021265793.
Topics: Humans; Child, Preschool; Learning Disabilities; Motor Skills; Reflex
PubMed: 35830652
DOI: 10.1177/13674935221114187 -
BMC Pregnancy and Childbirth Apr 2022Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this...
Machine Learning (ML) has been widely used in predicting the mode of childbirth and assessing the potential maternal risks during pregnancy. The primary aim of this review study is to explore current research and development perspectives that utilizes the ML techniques to predict the optimal mode of childbirth and to detect various complications during childbirth. A total of 26 articles (published between 2000 and 2020) from an initial set of 241 articles were selected and reviewed following a Systematic Literature Review (SLR) approach. As outcomes, this review study highlighted the objectives or focuses of the recent studies conducted on pregnancy outcomes using ML; explored the adopted ML algorithms along with their performances; and provided a synthesized view of features used, types of features, data sources and its characteristics. Besides, the review investigated and depicted how the objectives of the prior studies have changed with time being; and the association among the objectives of the studies, uses of algorithms, and the features. The study also delineated future research opportunities to facilitate the existing initiatives for reducing maternal complacent and mortality rates, such as: utilizing unsupervised and deep learning algorithms for prediction, revealing the unknown reasons of maternal complications, developing usable and useful ML-based clinical decision support systems to be used by the expecting mothers and health professionals, enhancing dataset and its accessibility, and exploring the potentiality of surgical robotic tools. Finally, the findings of this review study contributed to the development of a conceptual framework for advancing the ML-based maternal healthcare system. All together, this review will provide a state-of-the-art paradigm of ML-based maternal healthcare that will aid in clinical decision-making, anticipating pregnancy problems and delivery mode, and medical diagnosis and treatment.
Topics: Algorithms; Delivery of Health Care; Female; Health Personnel; Humans; Machine Learning; Parturition; Pregnancy
PubMed: 35546393
DOI: 10.1186/s12884-022-04594-2 -
BMC Infectious Diseases Nov 2021Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Convalescent plasma has been widely used to treat COVID-19 and is under investigation in numerous randomized clinical trials, but results are publicly available only for a small number of trials. The objective of this study was to assess the benefits of convalescent plasma treatment compared to placebo or no treatment and all-cause mortality in patients with COVID-19, using data from all available randomized clinical trials, including unpublished and ongoing trials (Open Science Framework, https://doi.org/10.17605/OSF.IO/GEHFX ).
METHODS
In this collaborative systematic review and meta-analysis, clinical trial registries (ClinicalTrials.gov, WHO International Clinical Trials Registry Platform), the Cochrane COVID-19 register, the LOVE database, and PubMed were searched until April 8, 2021. Investigators of trials registered by March 1, 2021, without published results were contacted via email. Eligible were ongoing, discontinued and completed randomized clinical trials that compared convalescent plasma with placebo or no treatment in COVID-19 patients, regardless of setting or treatment schedule. Aggregated mortality data were extracted from publications or provided by investigators of unpublished trials and combined using the Hartung-Knapp-Sidik-Jonkman random effects model. We investigated the contribution of unpublished trials to the overall evidence.
RESULTS
A total of 16,477 patients were included in 33 trials (20 unpublished with 3190 patients, 13 published with 13,287 patients). 32 trials enrolled only hospitalized patients (including 3 with only intensive care unit patients). Risk of bias was low for 29/33 trials. Of 8495 patients who received convalescent plasma, 1997 died (23%), and of 7982 control patients, 1952 died (24%). The combined risk ratio for all-cause mortality was 0.97 (95% confidence interval: 0.92; 1.02) with between-study heterogeneity not beyond chance (I = 0%). The RECOVERY trial had 69.8% and the unpublished evidence 25.3% of the weight in the meta-analysis.
CONCLUSIONS
Convalescent plasma treatment of patients with COVID-19 did not reduce all-cause mortality. These results provide strong evidence that convalescent plasma treatment for patients with COVID-19 should not be used outside of randomized trials. Evidence synthesis from collaborations among trial investigators can inform both evidence generation and evidence application in patient care.
Topics: COVID-19; Humans; Immunization, Passive; Randomized Controlled Trials as Topic; SARS-CoV-2; Treatment Outcome; COVID-19 Serotherapy
PubMed: 34800996
DOI: 10.1186/s12879-021-06829-7 -
The Cochrane Database of Systematic... Feb 2021Communication is a common element in all medical consultations, affecting a range of outcomes for doctors and patients. The increasing demand for medical students to be... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Communication is a common element in all medical consultations, affecting a range of outcomes for doctors and patients. The increasing demand for medical students to be trained to communicate effectively has seen the emergence of interpersonal communication skills as core graduate competencies in medical training around the world. Medical schools have adopted a range of approaches to develop and evaluate these competencies.
OBJECTIVES
To assess the effects of interventions for medical students that aim to improve interpersonal communication in medical consultations.
SEARCH METHODS
We searched five electronic databases: Cochrane Central Register of Controlled Trials, MEDLINE, Embase, PsycINFO, and ERIC (Educational Resource Information Centre) in September 2020, with no language, date, or publication status restrictions. We also screened reference lists of relevant articles and contacted authors of included studies.
SELECTION CRITERIA
We included randomised controlled trials (RCTs), cluster-RCTs (C-RCTs), and non-randomised controlled trials (quasi-RCTs) evaluating the effectiveness of interventions delivered to students in undergraduate or graduate-entry medical programmes. We included studies of interventions aiming to improve medical students' interpersonal communication during medical consultations. Included interventions targeted communication skills associated with empathy, relationship building, gathering information, and explanation and planning, as well as specific communication tasks such as listening, appropriate structure, and question style.
DATA COLLECTION AND ANALYSIS
We used standard methodological procedures expected by Cochrane. Two review authors independently reviewed all search results, extracted data, assessed the risk of bias of included studies, and rated the quality of evidence using GRADE.
MAIN RESULTS
We found 91 publications relating to 76 separate studies (involving 10,124 students): 55 RCTs, 9 quasi-RCTs, 7 C-RCTs, and 5 quasi-C-RCTs. We performed meta-analysis according to comparison and outcome. Among both effectiveness and comparative effectiveness analyses, we separated outcomes reporting on overall communication skills, empathy, rapport or relationship building, patient perceptions/satisfaction, information gathering, and explanation and planning. Overall communication skills and empathy were further divided as examiner- or simulated patient-assessed. The overall quality of evidence ranged from moderate to very low, and there was high, unexplained heterogeneity. Overall, interventions had positive effects on most outcomes, but generally small effect sizes and evidence quality limit the conclusions that can be drawn. Communication skills interventions in comparison to usual curricula or control may improve both overall communication skills (standardised mean difference (SMD) 0.92, 95% confidence interval (CI) 0.53 to 1.31; 18 studies, 1356 participants; I² = 90%; low-quality evidence) and empathy (SMD 0.64, 95% CI 0.23 to 1.05; 6 studies, 831 participants; I² = 86%; low-quality evidence) when assessed by experts, but not by simulated patients. Students' skills in information gathering probably also improve with educational intervention (SMD 1.07, 95% CI 0.61 to 1.54; 5 studies, 405 participants; I² = 78%; moderate-quality evidence), but there may be little to no effect on students' rapport (SMD 0.18, 95% CI -0.15 to 0.51; 9 studies, 834 participants; I² = 81%; low-quality evidence), and effects on information giving skills are uncertain (very low-quality evidence). We are uncertain whether experiential interventions improve overall communication skills in comparison to didactic approaches (SMD 0.08, 95% CI -0.02 to 0.19; 4 studies, 1578 participants; I² = 4%; very low-quality evidence). Electronic learning approaches may have little to no effect on students' empathy scores (SMD -0.13, 95% CI -0.68 to 0.43; 3 studies, 421 participants; I² = 82%; low-quality evidence) or on rapport (SMD 0.02, 95% CI -0.33 to 0.38; 3 studies, 176 participants; I² = 19%; moderate-quality evidence) compared to face-to-face approaches. There may be small negative effects of electronic interventions on information giving skills (low-quality evidence), and effects on information gathering skills are uncertain (very low-quality evidence). Personalised/specific feedback probably improves overall communication skills to a small degree in comparison to generic or no feedback (SMD 0.58, 95% CI 0.29 to 0.87; 6 studies, 502 participants; I² = 56%; moderate-quality evidence). There may be small positive effects of personalised feedback on empathy and information gathering skills (low quality), but effects on rapport are uncertain (very low quality), and we found no evidence on information giving skills. We are uncertain whether role-play with simulated patients outperforms peer role-play in improving students' overall communication skills (SMD 0.17, 95% CI -0.33 to 0.67; 4 studies, 637 participants; I² = 87%; very low-quality evidence). There may be little to no difference between effects of simulated patient and peer role-play on students' empathy (low-quality evidence) with no evidence on other outcomes for this comparison. Descriptive syntheses of results that could not be included in meta-analyses across outcomes and comparisons were mixed, as were effects of different interventions and comparisons on specific communication skills assessed by the included trials. Quality of evidence was downgraded due to methodological limitations across several risk of bias domains, high unexplained heterogeneity, and imprecision of results. In general, results remain consistent in sensitivity analysis based on risk of bias and adjustment for clustering. No adverse effects were reported. AUTHORS' CONCLUSIONS: This review represents a substantial body of evidence from which to draw, but further research is needed to strengthen the quality of the evidence base, to consider the long-term effects of interventions on students' behaviour as they progress through training and into practice, and to assess effects of interventions on patient outcomes. Efforts to standardise assessment and evaluation of interpersonal skills will strengthen future research efforts.
Topics: Communication; Education, Medical; Empathy; Humans; Information Management; Interpersonal Relations; Medical History Taking; Non-Randomized Controlled Trials as Topic; Patient Satisfaction; Patient Simulation; Randomized Controlled Trials as Topic; Role Playing; Students, Medical
PubMed: 33559127
DOI: 10.1002/14651858.CD012418.pub2 -
Analyzing adverse drug reaction using statistical and machine learning methods: A systematic review.Medicine Jun 2022Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have...
BACKGROUND
Adverse drug reactions (ADRs) are unintended negative drug-induced responses. Determining the association between drugs and ADRs is crucial, and several methods have been proposed to demonstrate this association. This systematic review aimed to examine the analytical tools by considering original articles that utilized statistical and machine learning methods for detecting ADRs.
METHODS
A systematic literature review was conducted based on articles published between 2015 and 2020. The keywords used were statistical, machine learning, and deep learning methods for detecting ADR signals. The study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines.
RESULTS
We reviewed 72 articles, of which 51 and 21 addressed statistical and machine learning methods, respectively. Electronic medical record (EMR) data were exclusively analyzed using the regression method. For FDA Adverse Event Reporting System (FAERS) data, components of the disproportionality method were preferable. DrugBank was the most used database for machine learning. Other methods accounted for the highest and supervised methods accounted for the second highest.
CONCLUSIONS
Using the 72 main articles, this review provides guidelines on which databases are frequently utilized and which analysis methods can be connected. For statistical analysis, >90% of the cases were analyzed by disproportionate or regression analysis with each spontaneous reporting system (SRS) data or electronic medical record (EMR) data; for machine learning research, however, there was a strong tendency to analyze various data combinations. Only half of the DrugBank database was occupied, and the k-nearest neighbor method accounted for the greatest proportion.
Topics: Adverse Drug Reaction Reporting Systems; Databases, Factual; Drug-Related Side Effects and Adverse Reactions; Electronic Health Records; Humans; Machine Learning
PubMed: 35758373
DOI: 10.1097/MD.0000000000029387 -
JAMA Psychiatry Aug 2023Social anxiety disorder (SAD) can be adequately treated with cognitive behavioral therapy (CBT). However, there is a large gap in knowledge on factors associated with... (Meta-Analysis)
Meta-Analysis
Baseline Severity as a Moderator of the Waiting List-Controlled Association of Cognitive Behavioral Therapy With Symptom Change in Social Anxiety Disorder: A Systematic Review and Individual Patient Data Meta-analysis.
IMPORTANCE
Social anxiety disorder (SAD) can be adequately treated with cognitive behavioral therapy (CBT). However, there is a large gap in knowledge on factors associated with prognosis, and it is unclear whether symptom severity predicts response to CBT for SAD.
OBJECTIVE
To examine baseline SAD symptom severity as a moderator of the association between CBT and symptom change in patients with SAD.
DATA SOURCES
For this systematic review and individual patient data meta-analysis (IPDMA), PubMed, PsycInfo, Embase, and the Cochrane Library were searched from January 1, 1990, to January 13, 2023. Primary search topics were social anxiety disorder, cognitive behavior therapy, and randomized controlled trial.
STUDY SELECTION
Inclusion criteria were randomized clinical trials comparing CBT with being on a waiting list and using the Liebowitz Social Anxiety Scale (LSAS) in adults with a primary clinical diagnosis of SAD.
DATA EXTRACTION AND SYNTHESIS
Authors of included studies were approached to provide individual-level data. Data were extracted by pairs of authors following the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline, and risk of bias was assessed using the Cochrane tool. An IPDMA was conducted using a 2-stage approach for the association of CBT with change in LSAS scores from baseline to posttreatment and for the interaction effect of baseline LSAS score by condition using random-effects models.
MAIN OUTCOMES AND MEASURES
The main outcome was the baseline to posttreatment change in symptom severity measured by the LSAS.
RESULTS
A total of 12 studies including 1246 patients with SAD (mean [SD] age, 35.3 [10.9] years; 738 [59.2%] female) were included in the meta-analysis. A waiting list-controlled association between CBT and pretreatment to posttreatment LSAS change was found (b = -20.3; 95% CI, -24.9 to -15.6; P < .001; Cohen d = -0.95; 95% CI, -1.16 to -0.73). Baseline LSAS scores moderated the differences between CBT and waiting list with respect to pretreatment to posttreatment symptom reductions (b = -0.22; 95% CI, -0.39 to -0.06; P = .009), indicating that individuals with severe symptoms had larger waiting list-controlled symptom reductions after CBT (Cohen d = -1.13 [95% CI, -1.39 to -0.88] for patients with very severe SAD; Cohen d = -0.54 [95% CI, -0.80 to -0.29] for patients with mild SAD).
CONCLUSIONS AND RELEVANCE
In this systematic review and IPDMA, higher baseline SAD symptom severity was associated with greater (absolute but not relative) symptom reductions after CBT in patients with SAD. The findings contribute to personalized care by suggesting that clinicians can confidently offer CBT to individuals with severe SAD symptoms.
Topics: Adult; Humans; Female; Male; Phobia, Social; Waiting Lists; Cognitive Behavioral Therapy; Randomized Controlled Trials as Topic
PubMed: 37256597
DOI: 10.1001/jamapsychiatry.2023.1291 -
BMJ Open Gastroenterology Jun 2021Colorectal cancer (CRC) is the third most common cancer for women and men and the second leading cause of cancer death in the USA. There is emerging evidence that the... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND AND AIMS
Colorectal cancer (CRC) is the third most common cancer for women and men and the second leading cause of cancer death in the USA. There is emerging evidence that the gut microbiome plays a role in CRC development, and antibiotics are one of the most common exposures that can alter the gut microbiome. We performed a systematic review and meta-analysis to characterise the association between antibiotic use and colorectal neoplasia.
METHODS
We searched PubMed, EMBASE, and Web of Science for articles that examined the association between antibiotic exposure and colorectal neoplasia (cancer or adenoma) through 15 December 2019. A total of 6031 citations were identified and 6 papers were included in the final analysis. We assessed the association between the level of antibiotic use (defined as number of courses or duration of therapy) and colorectal neoplasia using a random effects model.
RESULTS
Six studies provided 16 estimates of the association between level of antibiotic use and colorectal neoplasia. Individuals with the highest levels of antibiotic exposure had a 10% higher risk of colorectal neoplasia than those with the lowest exposure (effect size: 1.10, 95% CI 1.01 to 1.18). We found evidence of high heterogeneity (I=79%, p=0.0001) but not of publication bias.
CONCLUSIONS
Higher levels of antibiotic exposure is associated with an increased risk of colorectal neoplasia. Given the widespread use of antibiotics in childhood and early adulthood, additional research to further characterise this relationship is needed.
Topics: Adenoma; Adult; Anti-Bacterial Agents; Colorectal Neoplasms; Female; Humans; Male
PubMed: 34083227
DOI: 10.1136/bmjgast-2021-000601 -
JAMA Network Open Mar 2021An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost... (Comparative Study)
Comparative Study
IMPORTANCE
An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer).
OBJECTIVES
To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation.
EVIDENCE REVIEW
A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed.
FINDINGS
A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I.
CONCLUSIONS AND RELEVANCE
This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs.
Topics: Clinical Competence; Decision Support Systems, Clinical; Humans; Machine Learning
PubMed: 33704476
DOI: 10.1001/jamanetworkopen.2021.1276