Fecal Microbiota Transplantation for Recurrent Clostridioides difficile Infection Associates With Functional Alterations in Circulating microRNAs.

BACKGROUND AND AIMS: The molecular mechanisms underlying successful fecal microbiota transplantation (FMT) for recurrent Clostridioides difficile infection (rCDI) remain poorly understood. The primary objective of this study was to characterize alterations in microRNAs (miRs) following FMT for rCDI. METHODS: Sera from 2 prospective multicenter randomized controlled trials were analyzed for miRNA levels with the use of the Nanostring nCounter platform and quantitative reverse-transcription (RT) polymerase chain reaction (PCR). In addition, rCDI-FMT and toxin-treated animals and ex vivo human colonoids were used to compare intestinal tissue and circulating miRs. miR inflammatory gene targets in colonic epithelial and peripheral blood mononuclear cells were evaluated by quantitative PCR (qPCR) and 3'UTR reporter assays. Colonic epithelial cells were used for mechanistic, cytoskeleton, cell growth, and apoptosis studies. RESULTS: miRNA profiling revealed up-regulation of 64 circulating miRs 4 and 12 weeks after FMT compared with screening, of which the top 6 were validated in the discovery cohort by means of RT-qPCR. In a murine model of relapsing-CDI, RT-qPCR analyses of sera and cecal RNA extracts demonstrated suppression of these miRs, an effect reversed by FMT. In mouse colon and human colonoids, C difficile toxin B (TcdB) mediated the suppressive effects of CDI on miRs. CDI dysregulated DROSHA, an effect reversed by FMT. Correlation analyses, qPCR ,and 3'UTR reporter assays revealed that miR-23a, miR-150, miR-26b, and miR-28 target directly the 3'UTRs of IL12B, IL18, FGF21, and TNFRSF9, respectively. miR-23a and miR-150 demonstrated cytoprotective effects against TcdB. CONCLUSIONS: These results provide novel and provocative evidence that modulation of the gut microbiome via FMT induces alterations in circulating and intestinal tissue miRs. These findings contribute to a greater understanding of the molecular mechanisms underlying FMT and identify new potential targets for therapeutic intervention in rCDI.

Phycochemical analyses and pharmacological activities of seven macroalgae of Arabian Sea (Northern coast line).

Seaweeds have been used as nutraceuticals because of certain metabolites present in some species including polyphenols. Seven species of seaweeds collected from the Karachi coast were screened for the first time both for phycochemical analysis and pharmacological activities. The phycochemicals quantified included phenols, flavonoids, tannins and saponins. Melanothamnus afaqhusainii showed the highest content of phenols (2.16mg GAE/g) while highest flavonoids were observed in Coelarthrum muelleri (4.59mg RE/g). Tannins were found in low amounts while saponins were observed as major constituents among the seaweeds ranging from 1.34-3.47% in Sargassum swartzii and Codium flabellatum, respectively. Saponins were not analysed in Rhodophyta due to gel formation. Pharmacological screening revealed analgesic and anti-inflammatory effects. Both the methods of analgesic activities have shown significant increase in reaction time when methanol extracts were used. The reason for delayed anti-inflammatory activity of Solieria robusta and C. muelleri was found correlating with its gel forming ability. While Cystoseira indica and C. flabellatum exhibited highly significant effect from 1st to 5th h. Results suggested that selected seaweeds had potential in combating both acute and chronic inflammation and pain and hence can be used for therapeutic efficacy.

Are Liaison and Diversion interventions in policing delivering the planned impact: A longitudinal evaluation in two constabularies?

Liaison and Diversion (L&D) has twin objectives: improving mental health outcomes and reducing re-offending. Early diversion from police custody seems promising, but evidence of benefit is required to sustain such programmes. To test the hypothesis that contact with L&D services while in police custody would lead to improved mental health outcomes and a reduction in type and level of offending, we used a pre-post service use design. National Health Service (NHS) records in two counties were searched for evidence that patients had been involved with L&D services while in police custody during the period July 2009-December 2017. We defined January 2009-July 2014 as the pre-intervention period and any time after contact as the post-intervention period. Data from the Police National Computer were gathered for each period for these individuals, to assess their pre-post L&D contact offending histories. NHS Trust data were similarly gathered to assess their pre-post use of mental health legislation. 4,462 individuals were identified who had used L&D services in police custody. There were statistically significant reductions in the amount of offending following contact with the L&D service (whether one or two contacts), regardless of offence type. Statistically significant reductions were also observed in use of the four most commonly used legislative powers for detaining patients in hospital on mental disorder grounds, regardless of offending status (prolific/non-prolific). Our results indicate positive associations between the L&D interventions and change in offending and use of compulsory hospital detention. Whilst our research does not allow a direct causal relationship to be established in either area, the findings go beyond other impact assessments of L&D which have either been with small samples or relied only on qualitative data or expert opinion.

Police interactions and interventions with suspects flagged as experiencing mental health problems.

BACKGROUND: Interactions between individuals experiencing mental health (MH) problems and the police are complex, and effectiveness of innovative support and diversion models in England and Wales not yet fully evaluated. AIMS/HYPOTHESES: Our aims were to examine police interactions with suspects and to measure the immediate effectiveness of police/NHS MH interventions, including liaison and diversion and embedded staff in police contact and control rooms. We hypothesised that those with an MH flag would have significantly greater benefit from such interventions than those without in terms of how far they are taken down the criminal justice pathway and how long they spend in police custody. METHODS: We examined police interactions with suspects with and without flagged MH problems in relation to key outcome measures over a 15-month period, overall or when flagged by nurses alone. 'MH flagging' is defined as the presence of a marker on police systems, including both historical and current information, that alerts control room staff and response officers that the call may involve an individual/s with MH problems. Serial cross-sectional analysis of material from a database of individual cases integrating information from three police sources (N = 13,472) was used to test for associations between 'mental health flagging' and outcomes. RESULTS: Individuals with an MH flag have almost identical police dispatch response profiles to those without; they were arrested for and charged with similar offences. Those with an MH flag were significantly more likely to be charged with a criminal offence, less likely to receive a caution and spent longer periods in police custody than people under similar accusations but no MH flag. CONCLUSIONS: MH flagging appeared to disadvantage the people flagged, despite the presence of theoretically appropriate interventions. Further research is needed to understand this. It may be that indicating this form of vulnerability if the person is not judged to qualify for a MH service is discriminatory and may even account for excessive rates of mental disorder among prisoners.

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach.

BACKGROUND: Patient activation is defined as a patient's confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. OBJECTIVE: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. METHODS: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. RESULTS: The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. CONCLUSIONS: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions.

Study protocol for a multicentre longitudinal mixed methods study to explore the Outcomes of ChildrEn and fAmilies in the first year after paediatric Intensive Care: the OCEANIC study.

INTRODUCTION: Annually in the UK, 20 000 children become very ill or injured and need specialist care within a paediatric intensive care unit (PICU). Most children survive. However, some children and their families may experience problems after they have left the PICU including physical, functional and/or emotional problems. It is unknown which children and families experience such problems, when these occur or what causes them. The aim of this mixed-method longitudinal cohort study is to understand the physical, functional, emotional and social impact of children surviving PICU (aged: 1 month-17 years), their parents and siblings, during the first year after a PICU admission. METHODS AND ANALYSIS: A quantitative study involving 300 child survivors of PICU; 300 parents; and 150-300 siblings will collect data (using self-completion questionnaires) at baseline, PICU discharge, 1, 3, 6 and 12 months post-PICU discharge. Questionnaires will comprise validated and reliable instruments. Demographic data, PICU admission and treatment data, health-related quality of life, functional status, strengths and difficulties behaviour and post-traumatic stress symptoms will be collected from the child. Parent and sibling data will be collected on the impact of paediatric health conditions on the family's functioning capabilities, levels of anxiety and social impact of the child's PICU admission. Data will be analysed using descriptive and inferential statistics. Concurrently, an embedded qualitative study involving semistructured interviews with 24 enrolled families at 3 months and 9 months post-PICU discharge will be undertaken. Framework analysis will be used to analyse the qualitative data. ETHICS AND DISSEMINATION: The study has received ethical approval from the National Health Services Research Ethics Committee (Ref: 19/WM/0290) and full governance clearance. This will be the first UK study to comprehensively investigate physical, functional, emotional and social consequences of PICU survival in the first-year postdischarge.Clinical Trials Registration Number: ISRCTN28072812 [Pre-results].

Short and Long term predictions of Hospital emergency department attendances.

OBJECTIVE: Emergency departments in the United Kingdom (UK) experience significant difficulties in achieving the 95% NHS access standard due to unforeseen variations in patient flow. In order to maximize efficiency and minimize clinical risk, better forecasting of patient demand is necessary. The objective is therefore to create a tool that accurately predicts attendance at emergency departments to support optimal planning of human and physical resources. METHODS: Historical attendance data between Jan-2011 - December-2015 from four hospitals were used as a training set to develop and validate a forecasting model. To handle weekday variations, the data was first segmented into each weekday time series and a separate model for each weekday was performed. Seasonality testing was performed, followed by Box-Cox transformations. A modified heuristics based on a fuzzy time series model was then developed and compared with autoregressive integrated moving average and neural networks models using Harvey, Leybourne and Newbold (HLN) test. The time series models were tested in four emergency department sites to assess forecasting accuracy using the root mean square error and mean absolute percentage error. The models were tested for (i) short term prediction (four weeks ahead), using weekday time series; and (ii) long term predictions (four months ahead) using monthly time series. RESULTS: Data analysis revealed that presentations to emergency department and subsequent admissions to hospital were not a purely random process and therefore could be predicted with acceptable accuracy. Prediction accuracy improved as the forecast time intervals became wider (from daily to monthly). For each weekday time series modelling using fuzzy time series, for forecasting daily admissions, the mean absolute percentage error ranged from 2.63% to 4.72% while for monthly time series mean absolute percentage error varied from 2.01%-2.81%. For weekday time series, the mean absolute percentage error for autoregressive integrated moving average and neural network forecasting models ranged from 6.25% to 7.47% and 6.04%-7.42% respectively. The proposed fuzzy time series model proved to have statistically significant performance using Harvey, Leybourne and Newbold (HLN) test. This was explained by variations in attendances in different sites and weekdays. CONCLUSIONS: This paper described a heuristic-based fuzzy logic model for predicting emergency department attendances which could help resource allocation and reduce pressure on busy hospitals. Valid and reproducible prediction tools could be generated from these hospital data. The methodology had an acceptable accuracy over a relatively short time period, and could be used to assist better bed management, staffing and elective surgery scheduling. When compared to other prediction models usually applied for emergency department attendances prediction, the proposed heuristic model had better accuracy.

Effective fecal microbiota transplantation for recurrent Clostridioides difficile infection in humans is associated with increased signalling in the bile acid-farnesoid X receptor-fibroblast growth factor pathway.

The mechanisms of efficacy for fecal microbiota transplantation (FMT) in treating recurrent Clostridioides difficile infection (rCDI) remain poorly defined, with restored gut microbiota-bile acid interactions representing one possible explanation. Furthermore, the potential implications for host physiology of these FMT-related changes in gut bile acid metabolism are also not well explored. In this study, we investigated the impact of FMT for rCDI upon signalling through the farnesoid X receptor (FXR)-fibroblast growth factor (FGF) pathway. Herein, we identify that in addition to restoration of gut microbiota and bile acid profiles, FMT for rCDI is accompanied by a significant, sustained increase in circulating levels of FGF19 and reduction in FGF21. These FGF changes were associated with weight gain post-FMT, to a level not exceeding the pre-rCDI baseline. Collectively, these data support the hypothesis that the restoration of gut microbial communities by FMT for rCDI is associated with an upregulated FXR-FGF pathway, and highlight the potential systemic effect of FMT.