Hybrid immunity protection against SARS-CoV-2 and severe COVID-19 in kidney transplantation: A retrospective, comparative cohort study.

Hybrid immunity, resulting from a combination of SARS-CoV-2 infection and vaccination, offers robust protection against COVID-19 in the general population. However, its impact on immunocompromised patients remains unexplored. We investigated the effect of hybrid immunity against the Omicron variant in a population of kidney transplant recipients receiving the fourth dose mRNA monovalent vaccination. By extracting data from the clinical records and performing individual interviews, participants were categorized into the hybrid cohort (previously infected and vaccinated individuals) and the vaccine cohort (vaccinated-only individuals). The study comprised 1114 participants, 442 in the hybrid and 672 in the vaccine cohorts. From April 2022 to August 2023, 286 infections, 38 hospitalizations and 9 deaths were reported. The cumulative incidence of infection was 12.1% (95% confidence interval [CI], 9.03-16.03) for the hybrid cohort and 36.54% (95% CI, 32.81-40.54) for the vaccine cohort after 300 days of follow-up. Hybrid immunity was associated to a 72% lower risk of infection (adjusted hazard ratio, 0.28; 95% CI, 0.21-0.38) and a 96% lower risk of hospitalization (adjusted hazard ratio, 0.04; 95% CI, 0.01-0.32). No deaths occurred in the hybrid cohort. Hybrid immunity was associated with a lower incidence of SARS-CoV-2 infection and severe COVID-19, underscoring its importance for risk stratification in this vulnerable patient population.

Neural patient-specific 3D-2D registration in laparoscopic liver resection.

PURPOSE: Augmented reality guidance in laparoscopic liver resection requires the registration of a preoperative 3D model to the intraoperative 2D image. However, 3D-2D liver registration poses challenges owing to the liver's flexibility, particularly in the limited visibility conditions of laparoscopy. Although promising, the current registration methods are computationally expensive and often necessitate manual initialisation. METHODS: The first neural model predicting the registration (NM) is proposed, represented as 3D model deformation coefficients, from image landmarks. The strategy consists in training a patient-specific model based on synthetic data generated automatically from the patient's preoperative model. A liver shape modelling technique, which further reduces time complexity, is also proposed. RESULTS: The NM method was evaluated using the target registration error measure, showing an accuracy on par with existing methods, all based on numerical optimisation. Notably, NM runs much faster, offering the possibility of achieving real-time inference, a significant step ahead in this field. CONCLUSION: The proposed method represents the first neural method for 3D-2D liver registration. Preliminary experimental findings show comparable performance to existing methods, with superior computational efficiency. These results suggest a potential to deeply impact liver registration techniques.

Detection of Anomalies in Daily Activities Using Data from Smart Meters.

The massive deployment of smart meters in most Western countries in recent decades has allowed the creation and development of a significant variety of applications, mainly related to efficient energy management. The information provided about energy consumption has also been dedicated to the areas of social work and health. In this context, smart meters are considered single-point non-intrusive sensors that might be used to monitor the behaviour and activity patterns of people living in a household. This work describes the design of a short-term behavioural alarm generator based on the processing of energy consumption data coming from a commercial smart meter. The device captured data from a household for a period of six months, thus providing the consumption disaggregated per appliance at an interval of one hour. These data were used to train different intelligent systems, capable of estimating the predicted consumption for the next one-hour interval. Four different approaches have been considered and compared when designing the prediction system: a recurrent neural network, a convolutional neural network, a random forest, and a decision tree. By statistically analysing these predictions and the actual final energy consumption measurements, anomalies can be detected in the undertaking of three different daily activities: sleeping, breakfast, and lunch. The recurrent neural network achieves an F1-score of 0.8 in the detection of these anomalies for the household under analysis, outperforming other approaches. The proposal might be applied to the generation of a short-term alarm, which can be involved in future deployments and developments in the field of ambient assisted living.

Robust Isometric Non-Rigid Structure-From-Motion.

Non-Rigid Structure-from-Motion (NRSfM) reconstructs a deformable 3D object from keypoint correspondences established between monocular 2D images. Current NRSfM methods lack statistical robustness, which is the ability to cope with correspondence errors. This prevents one to use automatically established correspondences, which are prone to errors, thereby strongly limiting the scope of NRSfM. We propose a three-step automatic pipeline to solve NRSfM robustly by exploiting isometry. Step (i) computes the optical flow from correspondences, step (ii) reconstructs each 3D point's normal vector using multiple reference images and integrates them to form surfaces with the best reference and step (iii) rejects the 3D points that break isometry in their local neighborhood. Importantly, each step is designed to discard or flag erroneous correspondences. Our contributions include the robustification of optical flow by warp estimation, new fast analytic solutions to local normal reconstruction and their robustification, and a new scale-independent measure of 3D local isometric coherence. Experimental results show that our robust NRSfM method consistently outperforms existing methods on both synthetic and real datasets.

Using Perspective-n-Point Algorithms for a Local Positioning System Based on LEDs and a QADA Receiver.

The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver. The receiver is based on a quadrant photodiode angular diversity aperture (QADA) plus an aperture placed over it. This configuration can be modelled as a perspective camera, where the image position of the transmitters can be used to recover the receiver's 3D pose. This process is known as the perspective-n-point (PnP) problem, which is well known in computer vision and photogrammetry. This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space of 2 × 2 m2 based on four co-planar transmitters and with a distance from transmitters to receiver up to 3.4 m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver. In addition, correlation techniques (match filtering) are used to determine the image points projected from each emitter on the QADA. This work uses Monte Carlo simulations to characterize the absolute errors for a grid of test points under noisy measurements, as well as the robustness of the system when varying the 3D location of one transmitter. The IPPE algorithm obtained the best performance in this configuration. The proposal has also been experimentally evaluated in a real setup. The estimation of the receiver's position at three particular points for roll angles of the receiver of γ={0°, 120°, 210° and 300°} using the IPPE algorithm achieves average absolute errors and standard deviations of 4.33 cm, 3.51 cm and 28.90 cm; and 1.84 cm, 1.17 cm and 19.80 cm in the coordinates x, y and z, respectively. These positioning results are in line with those obtained in previous work using triangulation techniques but with the addition that the complete pose of the receiver (x, y, z, α, ß, γ) is obtained in this proposal.

Local Deformable 3D Reconstruction with Cartan's Connections.

3D reconstruction of deformable objects using inter-image visual motion from monocular images has been studied under Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM). Most methods have been developed for simple deformation models, primarily isometry. They may treat a surface as a discrete set of points and draw constraints from the points only or they may use a non-parametric representation and use both points and differentials to express constraints. We propose a differential framework based on Cartan's theory of connections and moving frames. It is applicable to SfT and NRSfM, and to deformation models other than isometry. It utilises infinitesimal-level assumptions on the surface's geometry and mappings. It has the following properties. 1) It allows one to derive existing solutions in a simpler way. 2) It models SfT and NRSfM in a unified way. 3) It allows us to introduce a new skewless deformation model and solve SfT and NRSfM for it. 4) It facilitates a generic solution to SfT which does not require deformation modeling. Our framework is complete: it solves deformable 3D reconstruction for a whole class of algebraic deformation models including isometry. We compared our solutions with the state-of-the-art methods and show that ours outperform in terms of both accuracy and computation time.

An Indoor Positioning Approach Based on Fusion of Cameras and Infrared Sensors.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4 × 3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

Stability and visual outcomes yielded by three intraocular trifocal lenses with same optical zone design but differing material or toricity.

PURPOSE: To compare rotational stability, centration and visual outcomes provided by three trifocal lens models that have the same optical zone design but different material, composition, and/or toricity. METHODS: The study included 78 patients with symmetric bilateral intraocular lens implantation. The lenses under evaluation were trifocal intraocular lenses made of hydrophilic acrylic material: a spherical lens 26% hydrophilic acrylic (POD FineVision), a similar lens but having a toric design (POD Toric FineVision), and a trifocal lens 25% hydrophilic acrylic material (FineVision/MicroF). Moreover, the lenses share the same optical zone design. The lenses' rotational stability and centration were measured by means of the PIOLET software, which relies on recording and image processing techniques to determine lens rotation and centration based on slit-lamp images. We also assessed patients' visual quality by means of 25, 40, and 80 cm VA tests. RESULTS: The best centration results were achieved with the POD Toric FineVision model, although the differences were not statistically significant. As for lens rotation, it was below 5° in all cases under study. Regarding VA, all subjects attained at least 0.3 logMAR for far distance uncorrected VA, at 80 cm VA was about 0.2 logMAR, at 40 cm it was above 0.15 logMAR, and at 25 cm it was about 0.3 logMAR for both lens types. CONCLUSION: All three intraocular lens models yield excellent visual results at far, near as well as intermediate distances. The POD FineVision and POD Toric FineVision models, with double C-loop design, yielded the best results centration-wise and rotation-wise. Differences had no clinical relevance.

Towards End-to-End Acoustic Localization Using Deep Learning: From Audio Signals to Source Position Coordinates.

This paper presents a novel approach for indoor acoustic source localization using microphone arrays, based on a Convolutional Neural Network (CNN). In the proposed solution, the CNN is designed to directly estimate the three-dimensional position of a single acoustic source using the raw audio signal as the input information and avoiding the use of hand-crafted audio features. Given the limited amount of available localization data, we propose, in this paper, a training strategy based on two steps. We first train our network using semi-synthetic data generated from close talk speech recordings. We simulate the time delays and distortion suffered in the signal that propagate from the source to the array of microphones. We then fine tune this network using a small amount of real data. Our experimental results, evaluated on a publicly available dataset recorded in a real room, show that this approach is able to produce networks that significantly improve existing localization methods based on SRP-PHAT strategies and also those presented in very recent proposals based on Convolutional Recurrent Neural Networks (CRNN). In addition, our experiments show that the performance of our CNN method does not show a relevant dependency on the speaker's gender, nor on the size of the signal window being used.

Inextensible Non-Rigid Structure-from-Motion by Second-Order Cone Programming.

We present a global and convex formulation for the template-less 3D reconstruction of a deforming object with the perspective camera. We show for the first time how to construct a Second-Order Cone Programming (SOCP) problem for Non-Rigid Structure-from-Motion (NRSfM) using the Maximum-Depth Heuristic (MDH). In this regard, we deviate strongly from the general trend of using affine cameras and factorization-based methods to solve NRSfM, which do not perform well with complex nonlinear deformations. In MDH, the points' depths are maximized so that the distance between neighbouring points in camera space are upper bounded by the geodesic distance. In NRSfM both geodesic and camera space distances are unknown. We show that, nonetheless, given point correspondences and the camera's intrinsics the whole problem can be solved with SOCP. This is the first convex formulation for NRSfM with physical constraints. We further present how robustness and temporal continuity can be included in the formulation to handle outliers and decrease the problem size, respectively. We show with extensive experiments that our methods accurately reconstruct quasi-isometric objects from partial views under articulated and strong deformations. Compared to the previous methods, our approach gives better or similar accuracy. It naturally handles missing correspondences, non-smooth objects and is very simple to implement compared to previous methods, with only one free parameter (the neighbourhood size).

Isometric Non-Rigid Shape-from-Motion with Riemannian Geometry Solved in Linear Time.

We study Isometric Non-Rigid Shape-from-Motion (Iso-NRSfM): given multiple intrinsically calibrated monocular images, we want to reconstruct the time-varying 3D shape of a thin-shell object undergoing isometric deformations. We show that Iso-NRSfM is solvable from local warps, the inter-image geometric transformations. We propose a new theoretical framework based on the Riemmanian manifold to represent the unknown 3D surfaces as embeddings of the camera's retinal plane. This allows us to use the manifold's metric tensor and Christoffel Symbol (CS) fields. These are expressed in terms of the first and second order derivatives of the inverse-depth of the 3D surfaces, which are the unknowns for Iso-NRSfM. We prove that the metric tensor and the CS are related across images by simple rules depending only on the warps. This forms a set of important theoretical results. We show that current solvers cannot solve for the first and second order derivatives of the inverse-depth simultaneously. We thus propose an iterative solution in two steps. 1) We solve for the first order derivatives assuming that the second order derivatives are known. We initialise the second order derivatives to zero, which is an infinitesimal planarity assumption. We derive a system of two cubics in two variables for each image pair. The sum-of-squares of these polynomials is independent of the number of images and can be solved globally, forming a well-posed problem for $N\geq 3$ images. 2) We solve for the second order derivatives by initialising the first order derivatives from the previous step. We solve a linear system of $4N-4$ equations in three variables. We iterate until the first order derivatives converge. The solution for the first order derivatives gives the surfaces' normal fields which we integrate to recover the 3D surfaces. The proposed method outperforms existing work in terms of accuracy and computation cost on synthetic and real datasets.

Model-Based Real-Time Non-Rigid Tracking.

This paper presents a sequential non-rigid reconstruction method that recovers the 3D shape and the camera pose of a deforming object from a video sequence and a previous shape model of the object. We take PTAM (Parallel Mapping and Tracking), a state-of-the-art sequential real-time SfM (Structure-from-Motion) engine, and we upgrade it to solve non-rigid reconstruction. Our method provides a good trade-off between processing time and reconstruction error without the need for specific processing hardware, such as GPUs. We improve the original PTAM matching by using descriptor-based features, as well as smoothness priors to better constrain the 3D error. This paper works with perspective projection and deals with outliers and missing data. We evaluate the tracking algorithm performance through different tests over several datasets of non-rigid deforming objects. Our method achieves state-of-the-art accuracy and can be used as a real-time method suitable for being embedded in portable devices.

Use of augmented reality in laparoscopic gynecology to visualize myomas.

OBJECTIVE: To report the use of augmented reality (AR) in gynecology. DESIGN: AR is a surgical guidance technology that enables important hidden surface structures to be visualized in endoscopic images. AR has been used for other organs, but never in gynecology and never with a very mobile organ like the uterus. We have developed a new AR approach specifically for uterine surgery and demonstrated its use for myomectomy. SETTING: Tertiary university hospital. PATIENT(S): Three patients with one, two, and multiple myomas, respectively. INTERVENTION(S): AR was used during laparoscopy to localize the myomas. MAIN OUTCOME MEASURE(S): Three-dimensional (3D) models of the patient's uterus and myomas were constructed before surgery from T2-weighted magnetic resonance imaging. The intraoperative 3D shape of the uterus was determined. These models were automatically aligned and "fused" with the laparoscopic video in real time. RESULT(S): The live fused video made the uterus appear semitransparent, and the surgeon can see the location of the myoma in real time while moving the laparoscope and the uterus. With this information, the surgeon can easily and quickly decide on how best to access the myoma. CONCLUSION(S): We developed an AR system for gynecologic surgery and have used it to improve laparoscopic myomectomy. Technically, the software we developed is very different to approaches tried for other organs, and it can handle significant challenges, including image blur, fast motion, and partial views of the organ.

A Stable Analytical Framework for Isometric Shape-from-Template by Surface Integration.

Shape-from-Template (SfT) reconstructs the shape of a deforming surface from a single image, a 3D template and a deformation prior. For isometric deformations, this is a well-posed problem. However, previous methods which require no initialization break down when the perspective effects are small, which happens when the object is small or viewed from larger distances. That is, they do not handle all projection geometries. We propose stable SfT methods that accurately reconstruct the 3D shape for all projection geometries. We follow the existing approach of using first-order differential constraints and obtain local analytical solutions for depth and the first-order quantities: the depth-gradient or the surface normal. Previous methods use the depth solution directly to obtain the 3D shape. We prove that the depth solution is unstable when the projection geometry tends to affine, while the solution for the first-order quantities remain stable for all projection geometries. We therefore propose to solve SfT by first estimating the first-order quantities (either depth-gradient or surface normal) and integrating them to obtain shape. We validate our approach with extensive synthetic and real-world experiments and obtain significantly more accurate results compared to previous initialization-free methods. Our approach does not require any optimization, which makes it very fast.

Augmented reality in gynecologic surgery: evaluation of potential benefits for myomectomy in an experimental uterine model.

BACKGROUND: Augmented Reality (AR) is a technology that can allow a surgeon to see subsurface structures. This works by overlaying information from another modality, such as MRI and fusing it in real time with the endoscopic images. AR has never been developed for a very mobile organ like the uterus and has never been performed for gynecology. Myomas are not always easy to localize in laparoscopic surgery when they do not significantly change the surface of the uterus, or are at multiple locations. OBJECTIVE: To study the accuracy of myoma localization using a new AR system compared to MRI-only localization. METHODS: Ten residents were asked to localize six myomas (on a uterine model into a laparoscopic box) when either using AR or in conditions that simulate a standard method (only the MRI was available). Myomas were randomly divided in two groups: the control group (MRI only, AR not activated) and the AR group (AR activated). Software was used to automatically measure the distance between the point of contact on the uterine surface and the myoma. We compared these distances to the true shortest distance to obtain accuracy measures. The time taken to perform the task was measured, and an assessment of the complexity was performed. RESULTS: The mean accuracy in the control group was 16.80 mm [0.1-52.2] versus 0.64 mm [0.01-4.71] with AR. In the control group, the mean time to perform the task was 18.68 [6.4-47.1] s compared to 19.6 [3.9-77.5] s with AR. The mean score of difficulty (evaluated for each myoma) was 2.36 [1-4] versus 0.87 [0-4], respectively, for the control and the AR group. DISCUSSION: We developed an AR system for a very mobile organ. This is the first user study to quantitatively evaluate an AR system for improving a surgical task. In our model, AR improves localization accuracy.

Resultados del seguimiento a seis meses del manejo del pie equino varo aducto congenito con el metodo Ponseti

Clasificacion de Dimeglio

Computer-Aided Classification of Gastrointestinal Lesions in Regular Colonoscopy.

We have developed a technique to study how good computers can be at diagnosing gastrointestinal lesions from regular (white light and narrow banded) colonoscopic videos compared to two levels of clinical knowledge (expert and beginner). Our technique includes a novel tissue classification approach which may save clinician's time by avoiding chromoendoscopy, a time-consuming staining procedure using indigo carmine. Our technique also discriminates the severity of individual lesions in patients with many polyps, so that the gastroenterologist can directly focus on those requiring polypectomy. Technically, we have designed and developed a framework combining machine learning and computer vision algorithms, which performs a virtual biopsy of hyperplastic lesions, serrated adenomas and adenomas. Serrated adenomas are very difficult to classify due to their mixed/hybrid nature and recent studies indicate that they can lead to colorectal cancer through the alternate serrated pathway. Our approach is the first step to avoid systematic biopsy for suspected hyperplastic tissues. We also propose a database of colonoscopic videos showing gastrointestinal lesions with ground truth collected from both expert image inspection and histology. We not only compare our system with the expert predictions, but we also study if the use of 3D shape features improves classification accuracy, and compare our technique's performance with three competitor methods.

Atención tanatológica para mejorar el bienestar espiritual de los pacientes hospitalizados con quemaduras / Thanatology care to improve spiritual well-being of hospitalized patients with burns

Resumen:

Objetivo: evaluar el efecto de la atención tanatológica en pacientes con quemaduras, a través de la escala de bienestar espiritual FACIT-Spl2.

Metodología: el estudio se realizó en pacientes con quemaduras de diversos grados. Se recolectaron datos sociodemográficos y a los pacientes se les aplicó la encuesta FACIT-Spl2 para evaluar su bienestar espiritual al inicio y al final de su estancia hospitalaria. Se les proporcionó atención de tanatología durante todo el periodo de hospitalización. El análisis incluyó estadística descriptiva y no paramétrica.

Resultados: se seleccionaron 107 pacientes que obtuvieron un promedio de 35.7 puntos en la encuesta FACIT-Spl2 aplicada al inicio. La puntuación aumentó a 40.7 al final del tratamiento hospitalario, lo que nos indica una mejoría en el bienestar espiritual de los pacientes, puesto que la diferencia fue estadísticamente significativa (Z = -6.53, p = 0.000).

Conclusiones: la atención tanatológica debe incluirse en el tratamiento integral proporcionado a pacientes con quemaduras, ya que demostró que mejoraba su bienestar espiritual, influyendo positivamente en su recuperación durante su estancia hospitalaria.

Abstract:

Objective: to evaluate the effect of Thanatology counseling in burn patients through the FaCIT-Spl2 spiritual well-being scale.

Methodolog: the study population comprised of hospitalized patients with several burn injuries. Socio-demographic information was collected, and participants completed the FACIT-Spl2 scale for spiritual well-being assessment the first and last day of hospital stay. Thanatology counseling was provided to them through the complete hospitalization time. Descriptive statistics was calculated for socio-demographic variables and non-parametric tests to calculate differences between obtained scores.

Results: the sample included 107 burn patients and scored 35.7 in the FACIT-Spl2 scale applied at baseline. This score increased to 40.7 at the end of treatment, which indicates an improvement in the spiritual well-being of patients and this difference was statistically significant (Z = -6.53, p = 0.000).

Conclusions: thanatology counseling should be included as part of the integral treatment given to burn patients because it has showed to improve their spiritual well-being, therefore it has a positive effect in their recovery during their hospital stay.