Improved immunologic response to COVID-19 vaccine with prolonged dosing interval in haemodialysis patients.

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.

Electrochemotherapy and Other Clinical Applications of Electroporation for the Targeted Therapy of Metastatic Melanoma.

Electrochemotherapy (ECT) is an effective bioelectrochemical procedure that uses controlled electrical pulses to facilitate the increase of intracellular concentration of certain substances (electropermeabilization/ reversible electroporation). ECT using antitumor drugs such as bleomycin and cisplatin is a minimally invasive targeted therapy that can be used as an alternative for oncologic patients not eligible for surgery or other standard therapies. Even though ECT is mainly applied as palliative care for metastases, it may also be used for primary tumors that are unresectable due to size and location. Skin neoplasms are the main clinical indication of ECT, the procedure reporting good curative results and high efficiency across all tumor types, including melanoma. In daily practice, there are many cases in which the patient's quality of life can be significantly improved by a safe procedure such as ECT. Its popularity must be increased because it has a safe profile and minor local adverse reactions. The method can be used by dermatologists, oncologists, and surgeons. The aim of this paper is to review recent literature concerning electrochemotherapy and other clinical applications of electroporation for the targeted therapy of metastatic melanoma.

Concentrations of Lead, Mercury, Arsenic, Cadmium, Manganese, and Aluminum in Blood of Romanian Children Suspected of Having Autism Spectrum Disorder.

Environmental exposure to lead (Pb), mercury (Hg), arsenic (As), cadmium (Cd), manganese (Mn), and aluminum (Al) has been associated with neurodevelopmental disorders including autism spectrum disorder (ASD). We conducted a pilot study during May 2015-May 2107 to estimate blood concentrations of six metals (Pb, Hg, As, Cd, Mn, and Al) and identify their associated factors for children with ASD or suspected of having ASD in Romania. Sixty children, age 2-8 years, were administered versions of ADOS or ADI-R translated from English to Romanian. After assessment, 2-3 mL of blood was obtained and analyzed for the concentrations of the six metals. The mean age of children was 51.9 months and about 90% were male. More than half (65%) of the children were born in Bucharest. Over 90% of concentrations of As and Cd were below limits of detection. Geometric mean concentrations of Pb, Mn, Al, and Hg were 1.14 µg/dL, 10.84 µg/L, 14.44 µg/L, and 0.35 µg/L, respectively. Multivariable linear regression analysis revealed that children who were female, had less educated parents, exhibited pica, and ate cold breakfast (e.g., cereal), watermelon, and lamb had significantly higher concentrations of Pb compared to their respective referent categories (all p < 0.05 except for eating lamb, which was marginally significant, p = 0.053). Although this is the first study that provides data on concentrations of the six metals for Romanian children with ASD, the findings from this study could be useful for designing future epidemiologic studies for investigating the role of these six metals in ASD in Romanian children.

Estimation of nonlinear errors-in-variables models for computer vision applications.

In an errors-in-variables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer vision problems. We show that the estimation of such nonlinear EIV models can be reduced to iteratively estimating a linear model having point dependent, i.e., heteroscedastic, noise process. Particular cases of the proposed heteroscedastic errors-in-variables (HEIV) estimator are related to other techniques described in the vision literature: the Sampson method, renormalization, and the fundamental numerical scheme. In a wide variety of tasks, the HEIV estimator exhibits the same, or superior, performance as these techniques and has a weaker dependence on the quality of the initial solution than the Levenberg-Marquardt method, the standard approach toward estimating nonlinear models.

Rapid object indexing using locality sensitive hashing and joint 3D-signature space estimation.

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.

Shapeme histogram projection and matching for partial object recognition.

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.