3,947 research outputs found
Iatrogenic fornix rupture caused during retrograde manipulation of the ureter: a case report
Iatrogenic fornix rupture caused during retrograde manipulation of the ureter is a rather rare or rarely diagnosed phenomenon. A 22 year-old female patient presented with a fornix rupture following endoscopic ureteral stone extraction under uretero-renoscopy, the rupture having become symptomatic two days later
Increased admissions to paediatric wards with a primary mental health diagnosis: results of a survey of a network of eating disorder paediatricians in England
Three operational taxonomic units of Eimeria are common in Nigerian chickens and may undermine effective diagnosis of coccidiosis
30 days wild: development and evaluation of a large-scale nature engagement campaign to improve well-being
There is a need to increase people’s engagement with and connection to nature, both for human well-being and the conservation of nature itself. In order to suggest ways for people to engage with nature and create a wider social context to normalise nature engagement, The Wildlife Trusts developed a mass engagement campaign, 30 Days Wild. The campaign asked people to engage with nature every day for a month. 12,400 people signed up for 30 Days Wild via an online sign-up with an estimated 18,500 taking part overall, resulting in an estimated 300,000 engagements with nature by participants. Samples of those taking part were found to have sustained increases in happiness, health, connection to nature and pro-nature behaviours. With the improvement in health being predicted by the improvement in happiness, this relationship was mediated by the change in connection to nature
Relationship between temporomandibular joint dynamics and mouthguards: feasibility of a test method
A test system was developed establishing the feasibility of collecting biomechanical data as they relate to the use of mouthguards. Previous experimental studies have examined the physical and mechanical properties of mouthguard materials. This information has been used as a guide for establishing material standards and specifications for the fabrication of mouthguards, but it lacks the key biomechanical parameters required for a thorough mouthguard evaluation. The current study was designed to assess whether the impact force, condylar deflection, and strain superior to the temporomandibular joint region could be measured. A drop test was conducted on a cadaveric specimen to simulate loading at the chin point. To measure the force of impact, an accelerometer was attached to an impactor of known mass. High-speed biplanar (1000 frames per second) radiographs were used to determine condylar displacement. Radio-opaque markers were inserted into the bone at predetermined locations. Total displacement of these markers was determined in reference to anatomical landmarks. Strain gauges were attached to the mandible and skull to monitor the effects of the condyle impacting the base of the skull. Based on the data collected, forces were calculated by determining the product of the time-based acceleration and known mass. A measurable change in force between the mouthguards and the control (no mouthguard) was demonstrated. The average condylar displacement was successfully measured and indicated as an increase in total deflection for impacts conducted with mouthguards. Quantifiable strain was measured in the region above the mandibular fossa with and without the insertion of a mouthguard at all impact conditions. However, it was determined that additional gauges would provide critical data. Key biomechanical parameters for chin-point impacts were determined in the current study. The technique demonstrated that both displacement within the mandibular fossa and loading of the condyles occur during the impact event. Although the current study established a technique that can be used to examine the relationship between mouthguards and jaw-joint injuries, the role, if any, mouthguards play in the reduction of injuries cannot be established until a thorough analysis is completed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74031/1/j.1600-9657.2004.00213.x.pd
Rapid Reactivation of Extralymphoid CD4 T Cells during Secondary Infection
After infection, extralymphoid tissues are enriched with effector and memory T cells of a highly activated phenotype. The capacity for rapid effector cytokine response from extralymphoid tissue-memory T cells suggests these cells may perform a ‘sentinel’ function in the tissue. While it has been demonstrated that extralymphoid CD4+ T cells can directly respond to secondary infection, little is known about how rapidly this response is initiated, and how early activation of T cells in the tissue may affect the innate response to infection. Here we use a mouse model of secondary heterosubtypic influenza infection to show that CD4+ T cells in the lung airways are reactivated within 24 hours of secondary challenge. Airway CD4+ T cells initiate an inflammatory cytokine and chemokine program that both alters the composition of the early innate response and contributes to the reduction of viral titers in the lung. These results show that, unlike a primary infection, extralymphoid tissue-memory CD4+ T cells respond alongside the innate response during secondary infection, thereby shaping the overall immune profile in the airways. These data provide new insights into the role of extralymphoid CD4+ T cells during secondary immune responses
Fluorescence characterization of clinically-important bacteria
Healthcare-associated infections (HCAI/HAI) represent a substantial threat to patient health during hospitalization and incur billions of dollars additional cost for subsequent treatment. One promising method for the detection of bacterial contamination in a clinical setting before an HAI outbreak occurs is to exploit native fluorescence of cellular molecules for a hand-held, rapid-sweep surveillance instrument. Previous studies have shown fluorescence-based detection to be sensitive and effective for food-borne and environmental microorganisms, and even to be able to distinguish between cell types, but this powerful technique has not yet been deployed on the macroscale for the primary surveillance of contamination in healthcare facilities to prevent HAI. Here we report experimental data for the specification and design of such a fluorescence-based detection instrument. We have characterized the complete fluorescence response of eleven clinically-relevant bacteria by generating excitation-emission matrices (EEMs) over broad wavelength ranges. Furthermore, a number of surfaces and items of equipment commonly present on a ward, and potentially responsible for pathogen transfer, have been analyzed for potential issues of background fluorescence masking the signal from contaminant bacteria. These include bedside handrails, nurse call button, blood pressure cuff and ward computer keyboard, as well as disinfectant cleaning products and microfiber cloth. All examined bacterial strains exhibited a distinctive double-peak fluorescence feature associated with tryptophan with no other cellular fluorophore detected. Thus, this fluorescence survey found that an emission peak of 340nm, from an excitation source at 280nm, was the cellular fluorescence signal to target for detection of bacterial contamination. The majority of materials analysed offer a spectral window through which bacterial contamination could indeed be detected. A few instances were found of potential problems of background fluorescence masking that of bacteria, but in the case of the microfiber cleaning cloth, imaging techniques could morphologically distinguish between stray strands and bacterial contamination
Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows
Background: Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. Methods: In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". Results: The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Conclusions: Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval rather than sometime in the future and allowing clinicians to timely adjust treatments and clinical appointments.FCT under the Neuroclinomics2 project [PTDC/EEI-SII/1937/2014, SFRH/BD/95846/2013]; INESC-ID plurianual [UID/CEC/50021/2013]; LASIGE Research Unit [UID/CEC/00408/2013
Understanding emotionally relevant situations in primary dental practice. 3. Emerging narratives
Background and aims. Dentists experience considerable occupational stress. Stressful clinical situations can provoke high levels of negative emotions, and situations which are associated with positive emotions tend to be overlooked by practitioners. Reflection regarding difficult situations is encouraged to facilitate learning. Cognitive behavioural therapy (CBT) formulations may be applied to situations appraised both positively and negatively. Analysis and interpretation of the dentist's coping behaviour and the consequent outcomes facilitate learning and reflection upon individual interactions with patients.
Method. Twenty primary care dental practitioners in the greater Lincoln area participated in a semi-structured interview which explored their stressful and positive clinical experiences. Some of the episodes were analysed to create CBT formulations.
Results and discussion. CBT formulations are presented and the learning points highlighted by this structured presentation are discussed. In particular, it is suggested that this structured reconstruction of events, which highlights dentists' emotions, responses and the transactional effects of coping responses, might well facilitate objective reflective learning either individually or as part of peer to peer support. It should facilitate dentists' emotional processing of events and may thus contribute to stress reduction.
Conclusion. CBT formulations of positive and negative dental scenarios may be constructed. It is proposed that this is a useful technique to foster reflection and learning in clinical situations and should lead to improved communication skills and shared decision-making, resulting in fewer complaints and thereby reduced stress. It should also improve dentists' emotional processing
Can CNN-based species classification generalise across variation in habitat within a camera trap survey?
Camera trap surveys are a popular ecological monitoring tool that produce vast numbers of images making their annotation extremely time-consuming. Advances in machine learning, in the form of convolutional neural networks, have demonstrated potential for automated image classification, reducing processing time. These networks often have a poor ability to generalise, however, which could impact assessments of species in habitats undergoing change. Here, we (i) compare the performance of three network architectures in identifying species in camera trap images taken from tropical forest of varying disturbance intensities; (ii) explore the impacts of training dataset configuration; (iii) use habitat disturbance categories to investigate network generalisability and (iv) test whether classification performance and generalisability improve when using images cropped to bounding boxes. Overall accuracy (72.8%) was improved by excluding the rarest species and by adding extra training images (76.3% and 82.8%, respectively). Generalisability to new camera locations within a disturbance level was poor (mean F1-score: 0.32). Performance across unseen habitat disturbance levels was worse (mean F1-score: 0.27). Training the network on multiple disturbance levels improved generalisability (mean F1-score on unseen disturbance levels: 0.41). Cropping images to bounding boxes improved overall performance (F1-score: 0.77 vs. 0.47) and generalisability (mean F1-score on unseen disturbance levels: 0.73), but at a cost of losing images that contained animals which the detector failed to detect. These results suggest researchers should consider using an object detector before passing images to a classifier, and an improvement in classification might be seen if labelled images from other studies are added to their training data. Composition of training data was shown to be influential, but including rarer classes did not compromise performance on common classes, providing support for the inclusion of rare species to inform conservation efforts. These findings have important implications for use of these methods for long-term monitoring of habitats undergoing change, as they highlight the potential for misclassifications due to poor generalisability to impact subsequent ecological analyses. These methods therefore need to be considered as dynamic, in that changes to the study site would need to be reflected in the updated training of the network
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