Are Therapies That Target α-Synuclein Effective at Halting Parkinson’s Disease Progression? A Systematic Review

There are currently no pharmacological treatments available that completely halt or reverse the progression of Parkinson’s Disease (PD). Hence, there is an unmet need for neuroprotective therapies. Lewy bodies are a neuropathological hallmark of PD and contain aggregated α-synuclein (α-syn) which is thought to be neurotoxic and therefore a suitable target for therapeutic interventions. To investigate this further, a systematic review was undertaken to evaluate whether anti-α-syn therapies are effective at preventing PD progression in preclinical in vivo models of PD and via current human clinical trials. An electronic literature search was performed using MEDLINE and EMBASE (Ovid), PubMed, the Web of Science Core Collection, and Cochrane databases to collate clinical evidence that investigated the targeting of α-syn. Novel preclinical anti-α-syn therapeutics provided a significant reduction of α-syn aggregations. Biochemical and immunohistochemical analysis of rodent brain tissue demonstrated that treatments reduced α-syn-associated pathology and rescued dopaminergic neuronal loss. Some of the clinical studies did not provide endpoints since they had not yet been completed or were terminated before completion. Completed clinical trials displayed significant tolerability and efficacy at reducing α-syn in patients with PD with minimal adverse effects. Collectively, this review highlights the capacity of anti-α-syn therapies to reduce the accumulation of α-syn in both preclinical and clinical trials. Hence, there is potential and optimism to target α-syn with further clinical trials to restrict dopaminergic neuronal loss and PD progression and/or provide prophylactic protection to avoid the onset of α-syn-induced PD

Patterns of pediatric trauma in Ramadan: an observational study

Introduction Motor vehicle crashes are a major cause of death among the Saudi population. In Ramadan, the working hours and the road traffic rush hours differ from other months of the year; the pattern of trauma may also differ. We compared trauma in the pediatric age group in Ramadan with non-Ramadan months in terms of frequency, patterns, and severity.Methods We conducted a retrospective study, which included all pediatric trauma cases, from 2001 to 2009, who were registered in King Abdulaziz Medical City Trauma Registry. Trauma patterns were divided into two groups according to the date of occurrence: victims in Ramadan versus victims in non-Ramadan.Results A total of 3766 patients were included. The average number of trauma per month was 39.2 versus 44 for non-Ramadan and Ramadan months, respectively (P = 0.79). The mean patient age in Ramadan was 8.04 years compared with 8.07 years in non-Ramadan months (P = 0.037). Blunt trauma was the most common type in both groups. The median of the Injury Severity Score was the same and equal to 4. In both groups, neurological and vascular injuries were more common in Ramadan: P = 0.02 and P = 0.03 respectively.Conclusion There were no significant differences between trauma in Ramadan and non-Ramadan months, except for the higher percentage of vascular and neurological injuries in Ramadan.Key words: children, head injury, motor vehicle accidents, Ramadan, Saudi, trauma, vascular injur

A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography

Background: Chronic pulmonary embolism (PE) may result in pulmonary hypertension (CTEPH). Automated CT pulmonary angiography (CTPA) interpretation using artificial intelligence (AI) tools has the potential for improving diagnostic accuracy, reducing delays to diagnosis and yielding novel information of clinical value in CTEPH. This systematic review aimed to identify and appraise existing studies presenting AI tools for CTPA in the context of chronic PE and CTEPH. Methods: MEDLINE and EMBASE databases were searched on 11 September 2023. Journal publications presenting AI tools for CTPA in patients with chronic PE or CTEPH were eligible for inclusion. Information about model design, training and testing was extracted. Study quality was assessed using compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: Five studies were eligible for inclusion, all of which presented deep learning AI models to evaluate PE. First study evaluated the lung parenchymal changes in chronic PE and two studies used an AI model to classify PE, with none directly assessing the pulmonary arteries. In addition, a separate study developed a CNN tool to distinguish chronic PE using 2D maximum intensity projection reconstructions. While another study assessed a novel automated approach to quantify hypoperfusion to help in the severity assessment of CTEPH. While descriptions of model design and training were reliable, descriptions of the datasets used in training and testing were more inconsistent. Conclusion: In contrast to AI tools for evaluation of acute PE, there has been limited investigation of AI-based approaches to characterising chronic PE and CTEPH on CTPA. Existing studies are limited by inconsistent reporting of the data used to train and test their models. This systematic review highlights an area of potential expansion for the field of AI in medical image interpretation. There is limited knowledge of A systematic review of artificial intelligence tools for chronic pulmonary embolism in CT. This systematic review provides an assessment on research that examined deep learning algorithms in detecting CTEPH on CTPA images, the number of studies assessing the utility of deep learning on CTPA in CTEPH was unclear and should be highlighted

“A good little tool to get to know yourself a bit better”: a qualitative study on users’ experiences of app-supported menstrual tracking in Europe

Background: Menstrual apps facilitate observation and analysis of menstrual cycles and associated factors through the collection and interpretation of data entered by users. As a subgroup of health-related apps, menstrual apps form part of one of the most dynamic and rapidly growing developments in biomedicine and health care. However, despite their popularity, qualitative research on how people engaging in period-tracking use and experience these apps remains scarce. Results: An inductive content analysis was performed and eight characteristics of app-supported menstrual tracking were identified: 1) tracking menstrual cycle dates and regularities, 2) preparing for upcoming periods, 3) getting to know menstrual cycles and bodies, 4) verifying menstrual experiences and sensations, 5) informing healthcare professionals, 6) tracking health, 7) contraception and seeking pregnancy, and 8) changes in tracking. Our study finds that period-tracking via apps has the potential to be an empowering practice as it helps users to be more aware of their menstrual cycles and health and to gain new knowledge. However, we also show that menstrual tracking can have negative consequences as it leads to distress in some cases, to privacy issues, and the work it requires can result in cessation. Finally, we present practical implications for healthcare providers and app developers. Conclusions: This qualitative study gives insight into users’ practices and experiences of app-supported menstrual tracking. The results provide information for researchers, health care providers and app designers about the implications of app-supported period-tracking and describe opportunities for patient-doctor interactions as well as for further development of menstrual apps.This research has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 675378

What Do Men Want from a Health Screening Mobile App? A Qualitative Study.

There is a lack of mobile app which aims to improve health screening uptake developed for men. As part of the study to develop an effective mobile app to increase health screening uptake in men, we conducted a needs assessment to find out what do men want from a health screening mobile app. In-depth interviews and focus group discussions were conducted with 31 men from a banking institution in Kuala Lumpur. The participants were purposely sampled according to their job position, age, ethnicity and screening status. The recruitment was stopped once data saturation was achieved. The audio-recorded interviews were transcribed verbatim and analyzed using thematic approach. Three themes emerged from the analysis and they were: content, feature and dissemination. In terms of the content, men wanted the app to provide information regarding health screening and functions that can assess their health; which must be personalized to them and are trustable. The app must have user-friendly features in terms of information delivery, ease of use, attention allocation and social connectivity. For dissemination, men proposed that advertisements, recommendations by health professionals, providing incentive and integrating the app as into existing systems may help to increase the dissemination of the app. This study identified important factors that need to be considered when developing a mobile app to improve health screening uptake. Future studies on mobile app development should elicit users' preference and need in terms of its content, features and dissemination strategies to improve the acceptability and the chance of successful implementation

Towards wireless highly sensitive capacitive strain sensors based on gold colloidal nanoparticles

International audienceWe designed, produced and characterized new capacitive strain sensors based on colloidal gold nanoparticles. The active area of these sensors, made up of a 1 mm2 close-packed assembly of gold nanoparticles between interdigitated electrodes, was designed to achieve measurable capacitance (>∼1 pF) and overcome parasitic capacitances. Electro-mechanical experiments revealed that the sensitivity of such capacitive sensors increases in relation to the size of the nanoparticles. In the case of 14 nm gold NPs, such sensors present a relative capacitance variation of −5.2% for a strain of 1.5%, which is more than 5 times higher than that observed for conventional capacitive strain gauges. The existence of two domains (pure capacitive domain and mixed capacitive–resistance domain) as a function of the frequency measurement allows for the adaptation of sensitivity of these capacitive sensors. A simple low-cost circuit based on a microcontroller board was finally developed to detect the capacitance variations of such NP based strain sensors. This low-cost equipment paves the way for the development of an entirely wireless application set-up

Electro-mechanical sensing in freestanding monolayered gold nanoparticle membranes

The electro-mechanical sensing properties of freestanding monolayered membranes of dodecanethiol coated 7 nm gold nanoparticles (NPs) are investigated using AFM force spectroscopy and conductive AFM simultaneously. The electrical resistance of the NP membranes increases sensitively with the point-load force applied in the center of the membranes using an AFM tip. Numerical simulations of electronic conduction in a hexagonally close-packed two-dimensional (2D) array of NPs under point load-deformation are carried out on the basis of electronic transport measurements at low temperatures and strain modeling of the NP membranes by finite element analysis. These simulations, supporting AFM-based electro-mechanical measurements, attribute the high strain sensitivity of the monolayered NP membranes to the exponential dependence of the tunnel electron transport in 2D NP arrays on the strain-induced length variation of the interparticle junctions. This work thus evidences a new class of highly sensitive nano-electro-mechanical systems based on freestanding monolayered gold NP membranes