The process of recovery of people with mental illness: The perspectives of patients, family members and care providers: Part 1

<p>Abstract</p> <p>Background</p> <p>It is a qualitative design study that examines points of divergence and convergence in the perspectives on recovery of 36 participants or 12 triads. Each triad comprising a patient, a family member/friend, a care provider and documents the procedural, analytic of triangulating perspectives as a means of understanding the recovery process which is illustrated by four case studies. Variations are considered as they relate to individual characteristics, type of participant (patient, family, member/friend and care provider), and mental illness. This paper which is part of a larger study and is based on a qualitative research design documents the process of recovery of people with mental illness: Developing a Model of Recovery in Mental Health: A middle range theory.</p> <p><b>Methods</b></p> <p>Data were collected in field notes through semi-structured interviews based on three interview guides (one for patients, one for family members/friends, and one for caregivers). Cross analysis and triangulation methods were used to analyse the areas of convergence and divergence on the recovery process of all triads.</p> <p>Results</p> <p>In general, with the 36 participants united in 12 triads, two themes emerge from the cross-analysis process or triangulation of data sources (12 triads analysis in 12 cases studies). Two themes emerge from the analysis process of the content of 36 interviews with participants: (1) <it>Revealing dynamic context</it>, situating patients in their dynamic context; and (2) <it>Relationship issues in a recovery process</it>, furthering our understanding of such issues. We provide four case studies examples (among 12 cases studies) to illustrate the variations in the way recovery is perceived, interpreted and expressed in relation to the different contexts of interaction.</p> <p>Conclusion</p> <p>The perspectives of the three participants (patients, family members/friends and care providers) suggest that recovery depends on constructing meaning around mental illness experiences and that the process is based on each person's dynamic context (e.g., social network, relationship), life experiences and other social determinants (e.g., symptoms, environment). The findings of this study add to existing knowledge about the determinants of the recovery of persons suffering with a mental illness and significant other utilizing public mental health services in Montreal, Canada.</p

Generating dermatopathology reports from gigapixel whole slide images with HistoGPT

Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consuming, labor-intensive, and non-standardized. To address this problem, we present HistoGPT, a vision language model that generates pathology reports from a patient's multiple full-resolution histology images. It is trained on 15,129 whole slide images from 6705 dermatology patients with corresponding pathology reports. The generated reports match the quality of human-written reports for common and homogeneous malignancies, as confirmed by natural language processing metrics and domain expert analysis. We evaluate HistoGPT in an international, multi-center clinical study and show that it can accurately predict tumor subtypes, tumor thickness, and tumor margins in a zero-shot fashion. Our model demonstrates the potential of artificial intelligence to assist pathologists in evaluating, reporting, and understanding routine dermatopathology cases

The psychopathological and psychosocial outcome of early-onset schizophrenia: Preliminary data of a 13-year follow-up

<p>Abstract</p> <p>Background</p> <p>Relatively little is known about the long-term psychopathological and psychosocial outcome of early-onset schizophrenia. The existing literature describes more severe courses of illness in these patients compared with adult-onset schizophrenia. This article reports preliminary data of a study exploring the outcome of early-onset schizophrenia 13.4 years (mean) after first admission. Predictors for interindividual outcomes were investigated.</p> <p>Methods</p> <p>We retrospectively assessed 27 former patients (mean age at first admission 15.5 years, SD = 2.0) that were consecutively admitted to the Department of Child and Adolescent Psychiatry at the University of Wuerzburg between 1990 and 2000. A multidimensional approach was chosen to assess the outcome consisting of a mail survey including different questions about psychopathological symptoms, psychosocial parameters, and standardized self-reports (ESI and ADS).</p> <p>Results</p> <p>Concerning the psychopathological outcome, 22.2% reported having acute schizophrenic symptoms. Almost one third (30.8%) described symptoms of depression and 37.0% reported having tried to commit suicide or seriously thought about it. 77.8% of the former patients were still in outpatient treatment. Compared to the general population, the number of patients without a school graduation was relatively high (18.5%). Almost half of participants still live with their parents (48.1%) or in assisted or semi-assisted living conditions (33.3%). Only 18.5% were working in the open market.</p> <p>Conclusion</p> <p>Schizophrenia with an early onset has an unfavourable prognosis. Our retrospective study of the psychopathological and psychosocial outcome concludes with a generally poor rating.</p

Differential Behavior of Young Eucalyptus Clones in Response to Nitrogen Supply

Eucalyptus requires large amounts of nitrogen (N); however, it responds in diverse manners to the application of this nutrient. The aim of this study was to evaluate the differential performance in growth, mineral nutrition, and gas exchanges of N-fertilized Eucalyptus clones. The treatments consisted of two Eucalyptus clones (VM-01 and I-144) and six N application rates (0, 0.74, 2.93, 4.39, 5.85, and 8 mmol L-1 NH4NO3) arranged in a randomized complete block design with five replications. VM-01 had greater plant height and greater height/collar diameter ratio, as well as higher leaf concentrations of all macronutrients and of Cu, Fe, Mo, and Zn. In terms of total and root dry matter production, root/shoot ratio, and collar diameter, as well as stomatal conductance and transpiration, I-144 performed better. The performance of the clones was clearly differentiated, and the growth of I-144, despite lower leaf N concentration, was in general better than VM-01

Image-based multiplex immune profiling of cancer tissues: translational implications. A report of the International Immuno-oncology Biomarker Working Group on Breast Cancer

Recent advances in the field of immuno-oncology have brought transformative changes in the management of cancer patients. The immune profile of tumours has been found to have key value in predicting disease prognosis and treatment response in various cancers. Multiplex immunohistochemistry and immunofluorescence have emerged as potent tools for the simultaneous detection of multiple protein biomarkers in a single tissue section, thereby expanding opportunities for molecular and immune profiling while preserving tissue samples. By establishing the phenotype of individual tumour cells when distributed within a mixed cell population, the identification of clinically relevant biomarkers with high-throughput multiplex immunophenotyping of tumour samples has great potential to guide appropriate treatment choices. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumour microenvironment, including the potential interplay between various cell types. However, there are significant challenges to widespread integration of these technologies in daily research and clinical practice. This review addresses the challenges and potential solutions within a structured framework of action from a regulatory and clinical trial perspective. New developments within the field of immunophenotyping using multiplexed tissue imaging platforms and associated digital pathology are also described, with a specific focus on translational implications across different subtypes of cancer

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting