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This review focuses on the effects of exercise on various health-related outcomes in cancer survivors, encompassing body composition, cognitive function (including sleep), and gut microbiome health. By analyzing multiple studies, we aimed to summarize the existing evidence and shed light on underlying mechanisms. The findings strongly suggest that exercise serves as a multifaceted non-pharmacological strategy, playing a significant role in improving the overall health of cancer survivors by effectively reducing inflammation and oxidative stress. Exercise plays a crucial role in preventing muscle wasting, diminishing the presence of reactive oxygen species and pro-inflammatory cytokines, and enhancing antioxidant systems. Furthermore, exercise displays notable benefits in terms of executive cognitive functioning and fatigue alleviation, largely attributed to its anti-inflammatory impact on the central nervous system and its ability to induce neurogenesis via growth factors. Additionally, exercise positively influences microbial diversity, reduces gut inflammation, and enhances neurogenesis through the gut-brain axis. Our key findings underscore the reduction of oxidative stress and inflammation as primary mechanisms by which exercise effectively enhances health outcomes in cancer survivors. By delving deeper into these candidate mechanisms, we aim to provide valuable guidance for future research and interventions targeting the symptoms experienced by cancer survivors.

Vaccination against 2019 coronavirus disease (COVID-19) can reduce disease incidence and severity. Dialysis patients demonstrate a delayed immunologic response to vaccines. We determined factors affecting the immunologic response to COVID-19 vaccines in haemodialysis patients. All patients within a Swedish haemodialysis network, vaccinated with two doses of COVID-19 vaccine 2-8 weeks before inclusion, were eligible for this cross-sectional study. Severe adult respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein antibody levels were determined by EliA SARS-CoV-2-Sp1 IgG test (Thermo Fisher Scientific, Phadia AB) and related to clinical and demographic parameters. Eighty-nine patients were included. Patients were vaccinated with two doses of Comirnaty (BNT162b2, 73%) or Spikevax (mRNA-1273, 23,6%). Three patients received combinations of different vaccines. Response rate (antibody titres >7 U/mL) was 89.9%, while 39.3% developed high antibody titres (>204 U/mL), 47 (43-50) days after the second dose. A previous COVID-19 infection associated with higher antibody titres (median (25th-75th percentile) 1558.5 (814.5-3,763.8) U/mL vs 87 (26-268) U/mL, P = .002), while time between vaccine doses did not differ between groups (P = .7). Increasing SARS-CoV-2 antibody titres were independently associated with increasing time between vaccine doses (B 0.241, P = .02), decreasing serum calcium levels (B -0.233, P = .007) and previous COVID-19 (B 1.078, P < .001). In conclusion, a longer interval between COVID-19 mRNA vaccine doses, lower calcium and a previous COVID-19 infection were independently associated with a stronger immunologic vaccination response in haemodialysis patients. While the response rate was good, only a minority developed high antibody titres, 47 (43-50) days after the second vaccine dose., (© 2022 The Authors. Scandinavian Journal of Immunology published by John Wiley & Sons Ltd on behalf of The Scandinavian Foundation for Immunology.)

Background: Measures of malaria burden using microscopy and rapid diagnostic tests (RDTs) in cross-sectional household surveys may incompletely describe the burden of malaria in low-transmission settings. This study describes the pattern of malaria transmission in Ethiopia using serological antibody estimates derived from a nationwide household survey completed in 2015., Methods: Dried blood spot (DBS) samples were collected during the Ethiopian Malaria Indicator Survey in 2015 from malarious areas across Ethiopia. Samples were analysed using bead-based multiplex assays for IgG antibodies for six Plasmodium antigens: four human malaria species-specific merozoite surface protein-1 19kD antigens (MSP-1) and Apical Membrane Antigen-1 (AMA-1) for Plasmodium falciparum and Plasmodium vivax. Seroprevalence was estimated by age, elevation and region. The seroconversion rate was estimated using a reversible catalytic model fitted with maximum likelihood methods., Results: Of the 10,278 DBS samples available, 93.6% (9622/10,278) had valid serological results. The mean age of participants was 15.8 years and 53.3% were female. National seroprevalence for antibodies to P. falciparum was 32.1% (95% confidence interval (CI) 29.8-34.4) and 25.0% (95% CI 22.7-27.3) to P. vivax. Estimated seroprevalences for Plasmodium malariae and Plasmodium ovale were 8.6% (95% CI 7.6-9.7) and 3.1% (95% CI 2.5-3.8), respectively. For P. falciparum seroprevalence estimates were significantly higher at lower elevations (< 2000 m) compared to higher (2000-2500 m) (aOR 4.4; p < 0.01). Among regions, P. falciparum seroprevalence ranged from 11.0% (95% CI 8.8-13.7) in Somali to 65.0% (95% CI 58.0-71.4) in Gambela Region and for P. vivax from 4.0% (95% CI 2.6-6.2) in Somali to 36.7% (95% CI 30.0-44.1) in Amhara Region. Models fitted to measure seroconversion rates showed variation nationally and by elevation, region, antigen type, and within species., Conclusion: Using multiplex serology assays, this study explored the cumulative malaria burden and regional dynamics of the four human malarias in Ethiopia. High malaria burden was observed in the northwest compared to the east. High transmission in the Gambela and Benishangul-Gumuz Regions and the neglected presence of P. malariae and P. ovale may require programmatic attention. The use of a multiplex assay for antibody detection in low transmission settings has the potential to act as a more sensitive biomarker.

We propose a new method for rapid 3D object indexing that combines feature-based methods with coarse alignment-based matching techniques. Our approach achieves a sublinear complexity on the number of models, maintaining at the same time a high degree of performance for real 3D sensed data that is acquired in largely uncontrolled settings. The key component of our method is to first index surface descriptors computed at salient locations from the scene into the whole model database using the Locality Sensitive Hashing (LSH), a probabilistic approximate nearest neighbor method. Progressively complex geometric constraints are subsequently enforced to further prune the initial candidates and eliminate false correspondences due to inaccuracies in the surface descriptors and the errors of the LSH algorithm. The indexed models are selected based on the MAP rule using posterior probability of the models estimated in the joint 3D-signature space. Experiments with real 3D data employing a large database of vehicles, most of them very similar in shape, containing 1,000,000 features from more than 365 models demonstrate a high degree of performance in the presence of occlusion and obscuration, unmodeled vehicle interiors and part articulations, with an average processing time between 50 and 100 seconds per query.

Histograms of shape signature or prototypical shapes, called shapemes, have been used effectively in previous work for 2D/3D shape matching and recognition. We extend the idea of shapeme histogram to recognize partially observed query objects from a database of complete model objects. We propose representing each model object as a collection of shapeme histograms and match the query histogram to this representation in two steps: 1) compute a constrained projection of the query histogram onto the subspace spanned by all the shapeme histograms of the model and 2) compute a match measure between the query histogram and the projection. The first step is formulated as a constrained optimization problem that is solved by a sampling algorithm. The second step is formulated under a Bayesian framework, where an implicit feature selection process is conducted to improve the discrimination capability of shapeme histograms. Results of matching partially viewed range objects with a 243 model database demonstrate better performance than the original shapeme histogram matching algorithm and other approaches.