Persistent physical symptoms reduction intervention: a system change and evaluation (PRINCE) - integrated GP care for persistent physical symptoms: protocol for a feasibility and cluster randomised waiting list, controlled trial

Introduction Persistent physical symptoms (PPS), also known as medically unexplained symptoms are associated with profound physical disability, psychological distress and high healthcare costs. England's annual National Health Service costs of attempting to diagnose and treat PPS amounts to approximately £3 billion. Current treatment relies on a positive diagnosis, life-style advice and drug therapy. However, many patients continue to suffer from ongoing symptoms and general practitioners (GPs) are challenged to find effective treatments. Training GPs in basic cognitive behavioural skills and providing self-help materials to patients could be useful, but availability in primary care settings is limited. Methods and analysis A cluster randomised waiting list, controlled trial will be conducted to assess the feasibility of an integrated approach to care in general practice. Approximately 240 patients with PPS will be recruited from 8 to 12 GP practices in London. GP practices will be randomised to 'integrated GP care plus treatment as usual' or waiting list control. Integrated GP care plus treatment as usual will include GP training in cognitive behavioural skills, GP supervision and written and audio visual materials for both GPs and participants. The primary objectives will be assessment of trial and intervention feasibility. Secondary objectives will include estimating the intracluster correlation coefficient for potential outcome measures for cluster effects in a sample size calculation. Feasibility parameters and identification of suitable primary and secondary outcomes for future trial evaluations will be assessed prerandomisation and at 12 and 24 weeks' postrandomisation, using a mixed-methods approach. Ethics and dissemination Ethical approval was granted by the Camberwell St Giles Ethics Committee. Results will be disseminated via peer-reviewed publications and conference presentations. This trial will inform researchers, clinicians, patients and healthcare providers about the feasibility and potential cost-effectiveness of an integrated approach to managing PPS in primary care. Trial registration number NCT02444520; Pre-results

Crystallization of calcium carbonate and magnesium hydroxide in the heat exchangers of once-through multistage flash (MSF-OT) desalination process

YesIn this paper, a dynamic model of fouling is presented to predict the crystallization of calcium carbonate and magnesium hydroxide inside the condenser tubes of Once-Through Multistage Flash (MSF-OT) desalination process. The model considers the combination of kinetic and mass diffusion rates taking into account the effect of temperature, velocity and salinity of the seawater. The equations for seawater carbonate system are used to calculate the concentration of the seawater species. The effects of salinity and temperature on the solubility of calcium carbonate and magnesium hydroxide are also considered. The results reveal an increase in the fouling inside the tubes caused by crystallization of CaCO3 and Mg(OH)2 with increase in the stage temperature. The intake seawater temperature and the Top Brine Temperature (TBT) are varied to investigate their impact on the fouling process. The results show that the (TBT) has greater impact than the seawater temperature on increasing the fouling

Anomalies in the review process and interpretation of the evidence in the NICE guideline for chronic fatigue syndrome and myalgic encephalomyelitis

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a disabling long-term condition of unknown cause. The National Institute for Health and Care Excellence (NICE) published a guideline in 2021 that highlighted the seriousness of the condition, but also recommended that graded exercise therapy (GET) should not be used and cognitive-behavioural therapy should only be used to manage symptoms and reduce distress, not to aid recovery. This U-turn in recommendations from the previous 2007 guideline is controversial.We suggest that the controversy stems from anomalies in both processing and interpretation of the evidence by the NICE committee. The committee: (1) created a new definition of CFS/ME, which 'downgraded' the certainty of trial evidence; (2) omitted data from standard trial end points used to assess efficacy; (3) discounted trial data when assessing treatment harm in favour of lower quality surveys and qualitative studies; (4) minimised the importance of fatigue as an outcome; (5) did not use accepted practices to synthesise trial evidence adequately using GRADE (Grading of Recommendations, Assessment, Development and Evaluations trial evidence); (6) interpreted GET as mandating fixed increments of change when trials defined it as collaborative, negotiated and symptom dependent; (7) deviated from NICE recommendations of rehabilitation for related conditions, such as chronic primary pain and (8) recommended an energy management approach in the absence of supportive research evidence.We conclude that the dissonance between this and the previous guideline was the result of deviating from usual scientific standards of the NICE process. The consequences of this are that patients may be denied helpful treatments and therefore risk persistent ill health and disability

Anomalies in the review process and interpretation of the evidence in the NICE guideline for chronic fatigue syndrome and myalgic encephalomyelitis

Chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) is a disabling long-term condition of unknown cause. The National Institute for Health and Care Excellence (NICE) published a guideline in 2021 that highlighted the seriousness of the condition, but also recommended that graded exercise therapy (GET) should not be used and cognitive-behavioural therapy should only be used to manage symptoms and reduce distress, not to aid recovery. This U-turn in recommendations from the previous 2007 guideline is controversial.We suggest that the controversy stems from anomalies in both processing and interpretation of the evidence by the NICE committee. The committee: (1) created a new definition of CFS/ME, which 'downgraded' the certainty of trial evidence; (2) omitted data from standard trial end points used to assess efficacy; (3) discounted trial data when assessing treatment harm in favour of lower quality surveys and qualitative studies; (4) minimised the importance of fatigue as an outcome; (5) did not use accepted practices to synthesise trial evidence adequately using GRADE (Grading of Recommendations, Assessment, Development and Evaluations trial evidence); (6) interpreted GET as mandating fixed increments of change when trials defined it as collaborative, negotiated and symptom dependent; (7) deviated from NICE recommendations of rehabilitation for related conditions, such as chronic primary pain and (8) recommended an energy management approach in the absence of supportive research evidence.We conclude that the dissonance between this and the previous guideline was the result of deviating from usual scientific standards of the NICE process. The consequences of this are that patients may be denied helpful treatments and therefore risk persistent ill health and disability

Diagnosis Of Sebaceous Lymphadenoma By Fine Needle Aspiration In A Patient With Cowden Syndrome: Case Report And Review Of The Literature

Sebaceous lymphadenoma (SLA) is a rare benign tumor of the salivary gland that commonly arises in the parotid gland in adults. It is rarely diagnosed correctly preoperatively. In addition, to the best of our knowledge, SLA has not been described yet in the literature in association with Cowden′s syndrome (CS). We present an extremely rare case of parotid SLA that was diagnosed preoperatively by fine needle aspiration in a patient with CS

UCF Brain Patch Batch 02

Pathologists diagnose biopsy samples with a stained specimen on the glass slide through a microscope. The entire specimen can be stored as a Whole Slide Image (WSI) for further analysis. However, managing and manually diagnosing hundreds of images is time-consuming and requires specific expertise. As a result, there is extensive ongoing research for computer-aided diagnosis of these digitally acquired pathology images. Deep learning has gained significant attention for its effectiveness with disease classification and segmentation of cancer cells from pathology images. For deep learning, a large number of annotated images are needed to build a robust and accurate model. However, there is a scarcity of a large number of annotated public images to validate and build a new model based on pathology images. To combat this limitation, we are introducing a public dataset where a large number of histopathology WSIs available from cadavers containing tissues of multiple organs such as lung, kidney, liver, pancreas, etc. We extract patches from each of the WSIs while discarding the white spaces in the slide. Later, we use the ImageNet model to train the model based on our processed dataset and classify patches from the WSI. Included in this paper is access to the full ~1700 WSIs with accurate labels by trained pathologists. Our dataset can be used as a benchmark dataset for training and validating deep learning models which contain a large number of WSIs with millions of extracted patches representative of 15-20 organ classes

UCF Lung Patch Batch 02

Pathologists diagnose biopsy samples with a stained specimen on the glass slide through a microscope. The entire specimen can be stored as a Whole Slide Image (WSI) for further analysis. However, managing and manually diagnosing hundreds of images is time-consuming and requires specific expertise. As a result, there is extensive ongoing research for computer-aided diagnosis of these digitally acquired pathology images. Deep learning has gained significant attention for its effectiveness with disease classification and segmentation of cancer cells from pathology images. For deep learning, a large number of annotated images are needed to build a robust and accurate model. However, there is a scarcity of a large number of annotated public images to validate and build a new model based on pathology images. To combat this limitation, we are introducing a public dataset where a large number of histopathology WSIs available from cadavers containing tissues of multiple organs such as lung, kidney, liver, pancreas, etc. We extract patches from each of the WSIs while discarding the white spaces in the slide. Later, we use the ImageNet model to train the model based on our processed dataset and classify patches from the WSI. Included in this paper is access to the full ~1700 WSIs with accurate labels by trained pathologists. Our dataset can be used as a benchmark dataset for training and validating deep learning models which contain a large number of WSIs with millions of extracted patches representative of 15-20 organ classes

UCF Lung Patch Batch 11

Pathologists diagnose biopsy samples with a stained specimen on the glass slide through a microscope. The entire specimen can be stored as a Whole Slide Image (WSI) for further analysis. However, managing and manually diagnosing hundreds of images is time-consuming and requires specific expertise. As a result, there is extensive ongoing research for computer-aided diagnosis of these digitally acquired pathology images. Deep learning has gained significant attention for its effectiveness with disease classification and segmentation of cancer cells from pathology images. For deep learning, a large number of annotated images are needed to build a robust and accurate model. However, there is a scarcity of a large number of annotated public images to validate and build a new model based on pathology images. To combat this limitation, we are introducing a public dataset where a large number of histopathology WSIs available from cadavers containing tissues of multiple organs such as lung, kidney, liver, pancreas, etc. We extract patches from each of the WSIs while discarding the white spaces in the slide. Later, we use the ImageNet model to train the model based on our processed dataset and classify patches from the WSI. Included in this paper is access to the full ~1700 WSIs with accurate labels by trained pathologists. Our dataset can be used as a benchmark dataset for training and validating deep learning models which contain a large number of WSIs with millions of extracted patches representative of 15-20 organ classes

UCF Lung Patch Batch 13

Pathologists diagnose biopsy samples with a stained specimen on the glass slide through a microscope. The entire specimen can be stored as a Whole Slide Image (WSI) for further analysis. However, managing and manually diagnosing hundreds of images is time-consuming and requires specific expertise. As a result, there is extensive ongoing research for computer-aided diagnosis of these digitally acquired pathology images. Deep learning has gained significant attention for its effectiveness with disease classification and segmentation of cancer cells from pathology images. For deep learning, a large number of annotated images are needed to build a robust and accurate model. However, there is a scarcity of a large number of annotated public images to validate and build a new model based on pathology images. To combat this limitation, we are introducing a public dataset where a large number of histopathology WSIs available from cadavers containing tissues of multiple organs such as lung, kidney, liver, pancreas, etc. We extract patches from each of the WSIs while discarding the white spaces in the slide. Later, we use the ImageNet model to train the model based on our processed dataset and classify patches from the WSI. Included in this paper is access to the full ~1700 WSIs with accurate labels by trained pathologists. Our dataset can be used as a benchmark dataset for training and validating deep learning models which contain a large number of WSIs with millions of extracted patches representative of 15-20 organ classes

UCF Lung Patch Batch 09

Pathologists diagnose biopsy samples with a stained specimen on the glass slide through a microscope. The entire specimen can be stored as a Whole Slide Image (WSI) for further analysis. However, managing and manually diagnosing hundreds of images is time-consuming and requires specific expertise. As a result, there is extensive ongoing research for computer-aided diagnosis of these digitally acquired pathology images. Deep learning has gained significant attention for its effectiveness with disease classification and segmentation of cancer cells from pathology images. For deep learning, a large number of annotated images are needed to build a robust and accurate model. However, there is a scarcity of a large number of annotated public images to validate and build a new model based on pathology images. To combat this limitation, we are introducing a public dataset where a large number of histopathology WSIs available from cadavers containing tissues of multiple organs such as lung, kidney, liver, pancreas, etc. We extract patches from each of the WSIs while discarding the white spaces in the slide. Later, we use the ImageNet model to train the model based on our processed dataset and classify patches from the WSI. Included in this paper is access to the full ~1700 WSIs with accurate labels by trained pathologists. Our dataset can be used as a benchmark dataset for training and validating deep learning models which contain a large number of WSIs with millions of extracted patches representative of 15-20 organ classes