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At the beginning of the COVID-19 pandemic, patients with primary and secondary immune disorders — including patients suffering from cancer — were generally regarded as a high-risk population in terms of COVID-19 disease severity and mortality. By now, scientific evidence indicates that there is substantial heterogeneity regarding the vulnerability towards COVID-19 in patients with immune disorders. In this review, we aimed to summarize the current knowledge about the effect of coexistent immune disorders on COVID-19 disease severity and vaccination response. In this context, we also regarded cancer as a secondary immune disorder. While patients with hematological malignancies displayed lower seroconversion rates after vaccination in some studies, a majority of cancer patients’ risk factors for severe COVID-19 disease were either inherent (such as metastatic or progressive disease) or comparable to the general population (age, male gender and comorbidities such as kidney or liver disease). A deeper understanding is needed to better define patient subgroups at a higher risk for severe COVID-19 disease courses. At the same time, immune disorders as functional disease models offer further insights into the role of specific immune cells and cytokines when orchestrating the immune response towards SARS-CoV-2 infection. Longitudinal serological studies are urgently needed to determine the extent and the duration of SARS-CoV-2 immunity in the general population, as well as immune-compromised and oncological patients.
Dark-haired dogs are predisposed to the development of digital squamous cell carcinoma (DSCC). This may potentially suggest an underlying genetic predisposition not yet completely elucidated. Some authors have suggested a potential correlation between the number of copies KIT Ligand (KITLG) and the predisposition of dogs to DSCC, containing a higher number of copies in those affected by the neoplasm. In this study, the aim was to evaluate a potential correlation between the number of copies of the KITLG and the histological grade of malignancy in dogs with DSCC. For this, 72 paraffin-embedded DSCCs with paired whole blood samples of 70 different dogs were included and grouped according to their haircoat color as follow: Group 0/unknown haircoat color (n = 11); Group 1.a/black non-Schnauzers (n = 15); group 1.b/black Schnauzers (n = 33); group 1.c/black and tan dogs (n = 7); group 2/tan animals (n = 4). The DSCCs were histologically graded. Additionally, KITLG Copy Number Variation (CNV) was determined by ddPCR. A significant correlation was observed between KITLG copy number and the histological grade and score value. This finding may suggest a possible factor for the development of canine DSCC, thus potentially having an impact on personalized veterinary oncological strategies and breeding programs.
During the COVID-19 pandemic, social distancing restricted psycho-oncological care. Therefore, this secondary analysis examines the changes in anxiety, fear of progression, fatigue, and depression in cancer patients after a video-based eHealth intervention. We used a prospective observational design with 155 cancer patients with mixed tumor entities. Data were assessed before and after the intervention and at a three-month follow-up using self-reported questionnaires (GAD-7, FOP-Q-SF, PHQ-8, and EORTC QLQ-FA12). The eight videos included psychoeducation, Acceptance and Commitment Therapy elements, and yoga and qigong exercises. The results showed that three months after finishing the video-based intervention, participants showed significantly reduced fear of progression (d = −0.23), depression (d = −0.27), and fatigue (d = −0.24) compared to the baseline. However, there was no change in anxiety (d = −0.09). Findings indicated marginal improvements in mental distress when using video-based intervention for cancer patients for up to three months, but long-term effectiveness must be confirmed using a controlled design.
Background
Medical resource management can be improved by assessing the likelihood of prolonged length of stay (LOS) for head and neck cancer surgery patients. The objective of this study was to develop predictive models that could be used to determine whether a patient's LOS after cancer surgery falls within the normal range of the cohort.
Methods
We conducted a retrospective analysis of a dataset consisting of 300 consecutive patients who underwent head and neck cancer surgery between 2017 and 2022 at a single university medical center. Prolonged LOS was defined as LOS exceeding the 75th percentile of the cohort. Feature importance analysis was performed to evaluate the most important predictors for prolonged LOS. We then constructed 7 machine learning and deep learning algorithms for the prediction modeling of prolonged LOS.
Results
The algorithms reached accuracy values of 75.40 (radial basis function neural network) to 97.92 (Random Trees) for the training set and 64.90 (multilayer perceptron neural network) to 84.14 (Random Trees) for the testing set. The leading parameters predicting prolonged LOS were operation time, ischemia time, the graft used, the ASA score, the intensive care stay, and the pathological stages. The results revealed that patients who had a higher number of harvested lymph nodes (LN) had a lower probability of recurrence but also a greater LOS. However, patients with prolonged LOS were also at greater risk of recurrence, particularly when fewer (LN) were extracted. Further, LOS was more strongly correlated with the overall number of extracted lymph nodes than with the number of positive lymph nodes or the ratio of positive to overall extracted lymph nodes, indicating that particularly unnecessary lymph node extraction might be associated with prolonged LOS.
Conclusions
The results emphasize the need for a closer follow-up of patients who experience prolonged LOS. Prospective trials are warranted to validate the present results.
The Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1), also known as CD66a, is a member of the immunoglobulin superfamily. CEACAM1 was shown to be a prognostic marker in patients suffering from cancer. In this review, we summarize pre-clinical and clinical evidence linking CEACAM1 to tumorigenicity and cancer progression. Furthermore, we discuss potential CEACAM1-based mechanisms that may affect cancer biology.
In tumor therapy anti-angiogenic approaches have the potential to increase the efficacy of a wide variety of subsequently or co-administered agents, possibly by improving or normalizing the defective tumor vasculature. Successful implementation of the concept of vascular normalization under anti-angiogenic therapy, however, mandates a detailed understanding of key characteristics and a respective scoring metric that defines an improved vasculature and thus a successful attempt. Here, we show that beyond commonly used parameters such as vessel patency and maturation, anti-angiogenic approaches largely benefit if the complex vascular network with its vessel interconnections is both qualitatively and quantitatively assessed. To gain such deeper insight the organization of vascular networks, we introduce a multi-parametric evaluation of high-resolution angiographic images based on light-sheet fluorescence microscopy images of tumors. We first could pinpoint key correlations between vessel length, straightness and diameter to describe the regular, functional and organized structure observed under physiological conditions. We found that vascular networks from experimental tumors diverted from those in healthy organs, demonstrating the dysfunctionality of the tumor vasculature not only on the level of the individual vessel but also in terms of inadequate organization into larger structures. These parameters proofed effective in scoring the degree of disorganization in different tumor entities, and more importantly in grading a potential reversal under treatment with therapeutic agents. The presented vascular network analysis will support vascular normalization assessment and future optimization of anti-angiogenic therapy.