Mesh-associated pain syndrome: predictors for continence and prolapse mesh removal surgery in a single centre.

Objective: Over the last two decades one of the main surgical treatment for stress urinary incontinence (SUI) and pelvic organ prolapse (POP) surgery was the insertion of non-absorbable mesh to restore continence and prolapse respectively. Over time complications arose including mesh-associated pain syndrome (MAPS), mesh exposure, mesh, erosion, chronic bladder/vaginal infections, and dyspareunia. Consequently, women chose surgical mesh removal to counter these problems.

Tree: Reducing the use of restrictive practices on psychiatric wards through virtual reality immersive technology training.

Background: Restrictive practices (RPs) are defined by measures linked to physical and chemical restraints to reduce the movement or control behaviours during any emergency. Seclusion is an equal part of RPs intended to isolate and reduce the sensory stimulation to safeguard the patient and those within the vicinity. Using interventions by way of virtual reality (VR) could assist with reducing the need for RPs as it could help reduce anxiety or agitation by way of placing users into realistic and immersive environments.

Technological Advances in SPECT and SPECT/CT Imaging.

Single photon emission tomography/computed tomography (SPECT/CT) is a mature imaging technology with a dynamic role in the diagnosis and monitoring of a wide array of diseases. This paper reviews the technological advances, clinical impact, and future directions of SPECT and SPECT/CT imaging. The focus of this review is on signal amplifier devices, detector materials, camera head and collimator designs, image reconstruction techniques, and quantitative methods.

A systematic review and bayesian meta-analysis of medical devices used in chronic pain management.

Whilst pharmacological therapies remain the cornerstone of pain management in chronic pain, factors including the current opioid epidemic have led to non-pharmacological techniques becoming a more attractive proposition. We explored the prevalence of medical device use and their treatment efficacy in non-cancer pain management. A systematic methodology was developed, peer reviewed and published in PROSPERO (CRD42021235384).

Surgical Treatment for Endometrial Cancer, Hysterectomy Performed via Minimally Invasive Routes Compared with Open Surgery: A Systematic Review and Network Meta-Analysis.

: Total hysterectomy with bilateral salpingo-oophorectomy via minimally invasive surgery (MIS) has emerged as the standard of care for early-stage endometrial cancer (EC). Prior systematic reviews and meta-analyses have focused on outcomes reported solely from randomised controlled trials (RCTs), overlooking valuable data from non-randomised studies. This inaugural systematic review and network meta-analysis comprehensively compares clinical and oncological outcomes between MIS and open surgery for early-stage EC, incorporating evidence from randomised and non-randomised studies.

A systematic review and network meta-analysis of pharmaceutical interventions used to manage chronic pain.

It is estimated 1.5 billion of the global population suffer from chronic pain with prevalence increasing with demographics including age. It is suggested long-term exposure to chronic could cause further health challenges reducing people's quality of life.

Synthetic data & the future of Women's Health: A synergistic relationship.

Objectives: The aim of this perspective is to report the use of synthetic data as a viable method in women's health given the current challenges linked to obtaining life-course data within a short period of time and accessing electronic healthcare data.

Methods: We used a 3-point perspective method to report an overview of data science, common applications, and ethical implications.

Results: There are several ethical challenges linked to using real-world data, consequently, generating synthetic data provides an alternative method to conduct comprehensive research when used effectively.

Translating digital healthcare to enhance clinical management: a protocol for an observational study using a digital health technology system to monitor medication adherence and its effect on mobility in people with Parkinson's.

Introduction: In people with Parkinson's (PwP) impaired mobility is associated with an increased falls risk. To improve mobility, dopaminergic medication is typically prescribed, but complex medication regimens result in suboptimal adherence. Exploring medication adherence and its impact on mobility in PwP will provide essential insights to optimise medication regimens and improve mobility.

A systematic review and meta-analysis of long COVID symptoms.

Background: Ongoing symptoms or the development of new symptoms following a SARS-CoV-2 diagnosis has caused a complex clinical problem known as "long COVID" (LC). This has introduced further pressure on global healthcare systems as there appears to be a need for ongoing clinical management of these patients. LC personifies heterogeneous symptoms at varying frequencies.

Feasibility and usability of a digital health technology system to monitor mobility and assess medication adherence in mild-to-moderate Parkinson's disease.

Introduction: Parkinson's disease (PD) is a neurodegenerative disorder which requires complex medication regimens to mitigate motor symptoms. The use of digital health technology systems (DHTSs) to collect mobility and medication data provides an opportunity to objectively quantify the effect of medication on motor performance during day-to-day activities. This insight could inform clinical decision-making, personalise care, and aid self-management.

The prevalence of mental ill-health in women during pregnancy and after childbirth during the Covid-19 pandemic: a systematic review and Meta-analysis.

Background: This systematic review aims to explore the prevalence of the impact of the COVID-19, MERS, and SARS pandemics on the mental health of pregnant women.

Methods: All COVID-19, SARS and MERS studies that evaluated the mental health of pregnant women with/without gynaecological conditions that were reported in English between December 2000 - July 2021 were included. The search criteria were developed based upon the research question using PubMed, Science Direct, Ovid PsycINFO and EMBASE databases.

Evaluation of a culturally adapted cognitive behavior therapy-based, third-wave therapy manual.

Background: Recommendations for psychotherapy have evolved over the years, with cognitive behavioral therapy (CBT) taking precedence since its inception within clinical guidelines in the United Kingdom and United States. The use of CBT for severe mental illness is now more common globally.

Aim: To investigate the feasibility and acceptability of a culturally adapted, CBT-based, third-wave therapy manual using the Comprehend, Cope, and Connect approach with individuals from a diverse population presenting to primary and secondary healthcare services.

A systematic review and meta-analysis of digital application use in clinical research in pain medicine.

Importance: Pain is a silent global epidemic impacting approximately a third of the population. Pharmacological and surgical interventions are primary modes of treatment. Cognitive/behavioural management approaches and interventional pain management strategies are approaches that have been used to assist with the management of chronic pain.

Mental health impact on Black, Asian and Minority Ethnic populations with preterm birth: A systematic review and meta-analysis.

Background: Preterm birth (PTB) is one of the main causes of neonatal deaths globally, with approximately 15 million infants are born preterm. Women from the Black, Asian, and Minority Ethnic (BAME) populations maybe at higher risk of PTB, therefore, the mental health impact on mothers experiencing a PTB is particularly important, within the BAME populations.

Aim: To determine the prevalence of mental health conditions among BAME women with PTB as well as the methods of mental health assessments used to characterise the mental health outcomes.

Mental health impact of the Middle East respiratory syndrome, SARS, and COVID-19: A comparative systematic review and meta-analysis.

Background: Over the last few decades, 3 pathogenic pandemics have impacted the global population; severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory syndrome coronavirus (MERS-CoV) and SARS-CoV-2. The global disease burden has attributed to millions of deaths and morbidities, with the majority being attributed to SARS-CoV-2. As such, the evaluation of the mental health (MH) impact across healthcare professionals (HCPs), patients and the general public would be an important facet to evaluate to better understand short, medium and long-term exposures.

Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson's Disease Classification Using Machine Learning.

Unlabelled: Parkinson's disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life.

Model-based joint curve registration and classification.

In this paper, we consider the problem of classification of misaligned multivariate functional data. We propose to use a model-based approach for the joint registration and classification of such data. The observed functional inputs are modeled as a functional nonlinear mixed effects model containing a nonlinear functional fixed effect constructed upon warping functions to account for curve alignment, and a nonlinear functional random effects component to address the variability among subjects.

A systematic review and meta-analysis of gestational diabetes mellitus and mental health among BAME populations.

Background: Gestational diabetes mellitus (GDM) is a common complication of pregnancy and is associated with an increased risk of mental health (MH) disorders including antenatal and postnatal depression (PND), anxiety and post-traumatic-stress-disorder (PTSD). We hypothesized GDM and MH disorders will disproportionately affect individuals from Black, Asian and Minority Ethnic backgrounds.

Methods: A systematic methodology was developed, and a protocol was published in PROSPERO (CRD42020210863) and a systematic review of publications between 1st January 1990 and 30th January 2021 was conducted.

A systematic review and meta-analysis of the Endometriosis and Mental-Health Sequelae; The ELEMI Project.

Background: It is important to evaluate sequalae for complex chronic health conditions such as endometriosis and mental health disorders. Endometriosis impacts 1 in 10 women. Mental health outcomes can be a primary determinant in many physical health conditions although this is an area not well researched particularly in women's health.

Artificial intelligence: A rapid case for advancement in the personalization of Gynaecology/Obstetric and Mental Health care.

To evaluate and holistically treat the mental health sequelae and potential psychiatric comorbidities associated with obstetric and gynaecological conditions, it is important to optimize patient care, ensure efficient use of limited resources and improve health-economic models. Artificial intelligence applications could assist in achieving the above. The World Health Organization and global healthcare systems have already recognized the use of artificial intelligence technologies to address 'system gaps' and automate some of the more cumbersome tasks to optimize clinical services and reduce health inequalities.

Evaluating upper limb function after stroke using the free-living accelerometer data.

Accelerometer devices are becoming efficient tools in clinical studies for automatically measuring the activities of daily living. Such data provides a time series describing activity level at every second and displays a subject's activity pattern throughout a day. However, the analysis of such data is very challenging due to the large number of observations produced each second and the variability among subjects.

Accelerometry-Based Digital Gait Characteristics for Classification of Parkinson's Disease: What Counts?

Gait may be a useful biomarker that can be objectively measured with wearable technology to classify Parkinson's disease (PD). This study aims to: (i) comprehensively quantify a battery of commonly utilized gait digital characteristics (spatiotemporal and signal-based), and (ii) identify the best discriminative characteristics for the optimal classification of PD. Six partial least square discriminant analysis (PLS-DA) models were trained on subsets of 210 characteristics measured in 142 subjects (81 people with PD, 61 controls (CL)).