Is Antiviral Treatment with Remdesivir at the Acute Phase of SARS-CoV-2 Infection Effective for Decreasing the Risk of Long-Lasting Post-COVID Symptoms?

The aim of this study was to investigate the effects of administrating Remdesivir at the acute COVID-19 phase on developing post-COVID symptoms in previously hospitalized COVID-19 survivors by controlling factors such as age, sex, body mass index, and vaccination status. A case-control study was performed. Hospitalized COVID-19 survivors who had received intravenous Remdesivir during the acute phase (n = 216) were matched by age, sex, body mass index, and vaccination status with survivors who did not receive antiviral treatment (n = 216). Participants were asked to self-report the presence of any post-COVID symptom (defined as a symptom that started no later than three months after infection) and whether the symptom persisted at the time of study (mean: 18.4, SD: 0.8 months). Anxiety levels (HADS-A), depressive symptoms (HADS-D), sleep quality (PSQI), and severity/disability (FIC) were also compared. The multivariate analysis revealed that administration of Remdesivir at the acute COVID-19 phase was a protective factor for long-term COVID development (OR0.401, 95%CI 0.256-0.628) and specifically for the following post-COVID symptoms: fatigue (OR0.399, 95%CI 0.270-0.590), pain (OR0.368, 95% CI 0.248-0.548), dyspnea at rest (OR0.580, 95%CI 0.361-0.933), concentration loss (OR0.368, 95%CI 0.151-0.901), memory loss (OR0.399, 95%CI 0.270-0.590), hair loss (OR0.103, 95%CI 0.052-0.207), and skin rashes (OR0.037, 95%CI 0.005-0.278). This study supports the potential protective role of intravenous administration of Remdesivir during the COVID-19 acute phase for long-lasting post-COVID symptoms in previously hospitalized COVID-19 survivors.

Inflammatory Polymorphisms (IL-6 rs1800796, IL-10 rs1800896, TNF-α rs1800629, and IFITM3 rs12252) Are Not Associated with Post-COVID Symptoms in Previously Hospitalized COVID-19 Survivors.

The aim of this study was to identify the association between four selected inflammatory polymorphisms with the development of long-term post-COVID symptoms in subjects who had been hospitalized due to SARS-CoV-2 infection during the first wave of the pandemic. These polymorphisms were selected as they are associated with severe COVID-19 disease and cytokine storm, so they could be important to prognoses post-COVID. A total of 408 (48.5% female, age: 58.5 ± 14.0 years) previously hospitalized COVID-19 survivors participated. The three potential genotypes of the following four single-nucleotide polymorphisms, IL-6 rs1800796, IL-10 rs1800896, TNF-α rs1800629, and IFITM3 rs12252, were obtained from non-stimulated saliva samples of the participants. The participants were asked to self-report the presence of any post-COVID symptoms (defined as symptoms that had started no later than one month after SARS-CoV-2 acute infection) and whether the symptoms persisted at the time of the study. At the time of the study (mean: 15.6, SD: 5.6 months after discharge), 89.4% of patients reported at least one post-COVID symptom (mean number of symptoms: 3.0; SD: 1.7). Fatigue (69.3%), pain (40.9%), and memory loss (27.2%) were the most prevalent post-COVID symptoms in the total sample. Overall, no differences in the post-COVID symptoms depending on the IL-6 rs1800796, IL-10 rs1800896, TNF-α rs1800629, and IFITM3 rs12252 genotypes were seen. The four SNPs assessed, albeit having been previously associated with inflammation and COVID-19 severity, did not cause a predisposition to the development of post-COVID symptoms in the previously hospitalized COVID-19 survivors.

Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge.

Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.

Clustering analysis identifies two subgroups of women with fibromyalgia with different psychological, cognitive, health-related, and physical features but similar widespread pressure pain sensitivity.

OBJECTIVE: Given that identification of groups of patients can help to better understand risk factors related to each group and to improve personalized therapeutic strategies, this study aimed to identify subgroups (clusters) of women with fibromyalgia syndrome (FMS) according to pain, pain-related disability, neurophysiological, cognitive, health, psychological, or physical features. METHODS: Demographic, pain, sensory, pain-related disability, psychological, health, cognitive, and physical variables were collected in 113 women with FMS. Widespread pressure pain thresholds were also assessed. K-means clustering was used to identify groups of women without any previous assumption. RESULTS: Two clusters exhibiting similar widespread sensitivity to pressure pain (pressure pain thresholds) but differing in the remaining variables were identified. Overall, women in one cluster exhibited higher pain intensity and pain-related disability; more sensitization-associated and neuropathic pain symptoms; higher kinesiophobia, hypervigilance, and catastrophism levels; worse sleep quality; higher anxiety/depressive levels; lower health-related function; and worse physical function than women in the other cluster. CONCLUSIONS: Cluster analysis identified one group of women with FMS exhibiting worse sensory, psychological, cognitive, and health-related features. Widespread sensitivity to pressure pain seems to be a common feature of FMS. The present results suggest that this group of women with FMS might need to be treated differently.

Bayesian Linear Regressions Applied to Fibromyalgia Syndrome for Understanding the Complexity of This Disorder.

A better understanding of the connection between factors associated with pain sensitivity and related disability in people with fibromyalgia syndrome may assist therapists in optimizing therapeutic programs. The current study applied mathematical modeling to analyze relationships between pain-related, psychological, psychophysical, health-related, and cognitive variables with sensitization symptom and related disability by using Bayesian Linear Regressions (BLR) in women with fibromyalgia syndrome (FMS). The novelty of the present work was to transfer a mathematical background to a complex pain condition with widespread symptoms. Demographic, clinical, psychological, psychophysical, health-related, cognitive, sensory-related, and related-disability variables were collected in 126 women with FMS. The first BLR model revealed that age, pain intensity at rest (mean-worst pain), years with pain (history of pain), and anxiety levels have significant correlations with the presence of sensitization-associated symptoms. The second BLR showed that lower health-related quality of life and higher pain intensity at rest (mean-worst pain) and pain intensity with daily activities were significantly correlated with related disability. These results support an application of mathematical modeling for identifying different interactions between a sensory (i.e., Central Sensitization Score) and a functional (i.e., Fibromyalgia Impact Questionnaire) aspect in women with FMS.

Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images.

Although the emergence of multi-parametric magnetic resonance imaging (mpMRI) has had a profound impact on the diagnosis of prostate cancers (PCa), analyzing these images remains still complex even for experts. This paper proposes a fully automatic system based on Deep Learning that performs localization, segmentation and Gleason grade group (GGG) estimation of PCa lesions from prostate mpMRIs. It uses 490 mpMRIs for training/validation and 75 for testing from two different datasets: ProstateX and Valencian Oncology Institute Foundation. In the test set, it achieves an excellent lesion-level AUC/sensitivity/specificity for the GGG[Formula: see text]2 significance criterion of 0.96/1.00/0.79 for the ProstateX dataset, and 0.95/1.00/0.80 for the IVO dataset. At a patient level, the results are 0.87/1.00/0.375 in ProstateX, and 0.91/1.00/0.762 in IVO. Furthermore, on the online ProstateX grand challenge, the model obtained an AUC of 0.85 (0.87 when trained only on the ProstateX data, tying up with the original winner of the challenge). For expert comparison, IVO radiologist's PI-RADS 4 sensitivity/specificity were 0.88/0.56 at a lesion level, and 0.85/0.58 at a patient level. The full code for the ProstateX-trained model is openly available at https://github.com/OscarPellicer/prostate_lesion_detection . We hope that this will represent a landmark for future research to use, compare and improve upon.

Artificial intelligence: the unstoppable revolution in ophthalmology.

Artificial intelligence (AI) is an unstoppable force that is starting to permeate all aspects of our society as part of the revolution being brought into our lives (and into medicine) by the digital era, and accelerated by the current COVID-19 pandemic. As the population ages and developing countries move forward, AI-based systems may be a key asset in streamlining the screening, staging, and treatment planning of sight-threatening eye conditions, offloading the most tedious tasks from the experts, allowing for a greater population coverage, and bringing the best possible care to every patient. This paper presents a review of the state of the art of AI in the field of ophthalmology, focusing on the strengths and weaknesses of current systems, and defining the vision that will enable us to advance scientifically in this digital era. It starts with a thorough yet accessible introduction to the algorithms underlying all modern AI applications. Then, a critical review of the main AI applications in ophthalmology is presented, including diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, glaucoma, and other AI-related topics such as image enhancement. The review finishes with a brief discussion on the opportunities and challenges that the future of this field might hold.

Enhanced prediction of hemoglobin concentration in a very large cohort of hemodialysis patients by means of deep recurrent neural networks.

Erythropoiesis Stimulating Agents (ESAs) have become a standard anemia management tool for End Stage Renal Disease (ESRD) patients. However, dose optimization constitutes an extremely challenging task due to huge inter and intra-patient variability in the responses to ESA administration. Current data-based approaches to anemia control focus on learning accurate hemoglobin prediction models, which can be later utilized for testing competing treatment choices and choosing the optimal one. These methods, despite being proven effective in practice, present several shortcomings which this paper intends to tackle. Namely, they are limited to a small cohort of patients and, even then, they fail to provide suggestions when some strict requirements are not met (such as having a three month history prior to the prediction). Here, recurrent neural networks (RNNs) are used to model whole patient histories, providing predictions at every time step since the very first day. Furthermore, an unprecedented amount of data (∼110,000 patients from many different medical centers in twelve countries, without exclusion criteria) was used to train it, thus allowing it to generalize for every single patient. The resulting model outperforms state-of-the-art Hemoglobin prediction, providing excellent results even when tested on a prospective dataset. Simultaneously, it allows to bring the benefits of algorithmic anemia control to a very large group of patients.