Pain and transition: evaluating fibromyalgia in transgender individuals.

OBJECTIVES: As members of a gender minority, transgender individuals face many challenges. Many experience distress, depression, anxiety and suicidal ideation related to gender non-conformity and transphobia. Stress and trauma may contribute to the development of fibromyalgia (FM) syndrome, characterised by widespread pain and fatigue. The prevalence of FM among transgenders is not known. METHODS: Transgender participants were recruited at a specialised clinic. Questionnaires included the Widespread Pain Index (WPI), the Symptom Severity Score (SSS) and the SF-36. Data concerning hormonal treatment protocols was retrieved from charts. The current prevalence of FM was determined, as well as the prevalence before and after testosterone treatment among TM. Pearson correlations were calculated between all measures. RESULTS: 115 participants were recruited, 62.6% transgender men (TM), 37.4% transgender women (TW). 17 individuals (14.8%) fulfilled the 2011 modified ACR FM criteria, for a rate of 19.4% among TM and 6.98% among TW. Among TM, FM was associated with younger age, smoking and SF-36 sub-scales related to physical functioning, role limitation due to physical pain, fatigue, pain and general health. Among TW, FM was associated with social status, employment, depression, existing medical treatment and substance abuse, as well as SF-36 subscales related to role limitations affected due to pain. CONCLUSIONS: Fibromyalgia symptoms are highly prevalent among Israeli transgender individuals and may be related to psychological distress and gender dysphoria. Healthcare professionals treating transgenders should remain vigilant for the occurrence of chronic pain, fatigue and other FM-related symptoms and be prepared to treat and/or refer such patients accordingly.

Long COVID-19 Enigma: Unmasking the Role of Distinctive Personality Profiles as Risk Factors.

Background: The COVID-19 (Coronavirus disease 2019) pandemic has prompted extensive research into lingering effects, especially in 'Long COVID' patients. Despite exploration, contributing factors remain elusive; Objective: This study explores the potential link between distinctive personality profiles, particularly type D personality, and an increased risk of Long COVID; Methods: A retrospective cross-sectional study at Tel-Aviv Sourasky Medical Center's Post-COVID clinic analyzed data from 373 Long COVID patients through comprehensive questionnaires covering Long COVID syndrome, Fibromyalgia criteria, personality assessments, social support, and subjective evaluations of cognitive decline, health and life quality. In total, 116 out of 373 patients completed the questionnaire, yielding a 31% participation rate; Results: Cluster analysis revealed two groups, with Cluster 1 (N = 58) exhibiting Type D personality traits while Cluster 2 (N = 56) not meeting criteria for Type D personality. In comparison to Cluster 2, Cluster 1 patients reported heightened anxiety, depression, reduced social support, increased pain symptoms, manifestations of fibromyalgia, cognitive decline, and poor sleep quality, contributing to a diminished quality-of-life perception; Conclusions: findings highlight diverse personality profiles among Long COVID patients, emphasizing the need for tailored care. This approach shows potential for improving Long COVID patient care, aligning with the evolving personalized medicine paradigm.

Impact of repeated operations for progressive low-grade gliomas.

BACKGROUND: Maximal, aggressive resection of diffuse low-grade gliomas (DLGG) is well established as the standard of care in neuro-oncology. The role of repeat resection for tumor progression is unclear. OBJECTIVE: To assess the role of repeated operation for DLGG, and the effect on malignant transformation and survival. METHODS: We conducted a historical cohort study in which all patients undergoing multiple resections of DLGG between the years 1995-2019 were evaluated for overall survival (OS) and time to transformation (TTT). We then compared the outcome of this group with that of a matched control group comprised of patients who underwent only one operation despite being eligible for repeat surgery at tumor progression, but had received non-surgical oncological therapy or declined additional treatment. RESULTS: Of 607 patients in our departmental DLGG database, 93 patients underwent 2 or more surgeries and had sufficient follow-up and imaging data to be included in the study group. Thirty-eight patients were included in the matched control group. Early (less than 1 year) progression was associated with decreased survival and shorter TTT in the study group. Patients undergoing multiple resections had significantly longer TTT and OS compared to patients who underwent a single surgery. This effect was especially noted in patients who had radiological evidence of tumor transformation. CONCLUSIONS: Repeated resections of LGG are safe and offer survival benefit in select patients. Early progression following resection is associated with worse prognosis. Patients with evidence of radiological transformation may benefit the most from re-resection.

Virtual biopsy using MRI radiomics for prediction of BRAF status in melanoma brain metastasis.

Brain metastases are common in patients with advanced melanoma and constitute a major cause of morbidity and mortality. Between 40% and 60% of melanomas harbor BRAF mutations. Selective BRAF inhibitor therapy has yielded improvement in clinical outcome; however, genetic discordance between the primary lesion and the metastatic tumor has been shown to occur. Currently, the only way to characterize the genetic landscape of a brain metastasis is by tissue sampling, which carries risks and potential complications. The aim of this study was to investigate the use of radiomics analysis for non-invasive identification of BRAF mutation in patients with melanoma brain metastases, based on conventional magnetic resonance imaging (MRI) data. We applied a machine-learning method, based on MRI radiomics features for noninvasive characterization of the BRAF status of brain metastases from melanoma (BMM) and applied it to BMM patients from two tertiary neuro-oncological centers. All patients underwent surgical resection for BMM, and their BRAF mutation status was determined as part of their oncological work-up. Their routine preoperative MRI study was used for radiomics-based analysis in which 195 features were extracted and classified according to their BRAF status via a support vector machine. The BRAF status of 53 study patients, with 54 brain metastases (25 positive, 29 negative for BRAF mutation) was predicted with mean accuracy = 0.79 ± 0.13, mean precision = 0.77 ± 0.14, mean sensitivity = 0.72 ± 0.20, mean specificity = 0.83 ± 0.11 and with a 0.78 area under the receiver operating characteristic curve for positive BRAF mutation prediction. Radiomics-based noninvasive genetic characterization is feasible and should be further verified using large prospective cohorts.