Diversidad de ninfas de Leptohyphidae (Ephemeroptera) en el río Quenane-Quenanito, piedemonte llanero colombiano / Diversity of Leptohyphidae (Ephemeroptera) nymphs in the Quenane-Quenanito river, Colombian plain foothills

Resumen Introducción: Varias presiones antrópicas sufren los ecosistemas acuáticos del piedemonte llanero en Colombia. La respuesta a estresores ambientales aún se desconoce en organismos bioindicadores como Leptohyphidae. Objetivo: Determinar la diversidad de ninfas de Leptohyphidae del río Quenane-Quenanito, en dos periodos hidrológicos contrastantes y su relación con algunas variables fisicoquímicas. Métodos: En diciembre (2014) y febrero (2015) se recolectaron organismos con red Surber en seis estaciones a lo largo del río. Se analizó la diversidad alfa y beta y se aplicó análisis de redundancia y modelos lineales generalizados con el fin de establecer la relación entre los taxones y las variables ambientales. Resultados: Se identificaron 369 organismos pertenecientes a cuatro géneros (Amanahyphes, Traverhyphes, Tricorythopsis y Tricorythodes), dos especies y ocho morfoespecies. Se reporta por primera vez para el departamento del Meta Amanahyphes saguassu. Se registró la mayor diversidad de ninfas en la transición a la sequía y la mayor abundancia en sequía. La diversidad beta señaló que la configuración del ensamblaje cambia a nivel espacial y temporal. Conclusiones: Los organismos de Leptohyphidae prefieren hábitats de corrientes, particularmente en el periodo de sequía, donde hallan alimento (hojarasca, detritos) y refugio para establecerse exitosamente; actividades antrópicas como la urbanización afectan notablemente la diversidad. La alta diversidad registrada en este pequeño río de piedemonte llanero refleja la necesidad de incrementar este tipo de trabajos y esfuerzos de recolección de material de estudio en la región.

Abstract Introduction: Various anthropic pressures affect the aquatic ecosystems of the foothills of Colombia. The response to environmental stressors is still unknown in bioindicator organisms such as Leptohyphidae. Objective: To determine the diversity of Leptohyphidae nymphs of the Quenane-Quenanito river, in two contrasting hydrological periods and its relationship with some physicochemical variables. Methods: In December (2014) and February (2015), organisms were collected with a Surber net at six stations along the current. Alpha and beta diversity was analyzed and redundancy analysis and generalized linear model were applied to establish the relationship between taxa and environmental variables. Results: Were identified 369 organisms belonging to four genera (Amanahyphes, Traverhyphes, Tricorythopsis, and Tricorythodes), two species, and eight morphospecies. Amanahyphes saguassu is reported for the first time for the Meta department. High diversity of Leptohyphidae nymphs was recorded in the transition to drought season and greater abundance in drought. Beta diversity indicated that the configuration of the assemblage changes spatially and temporally. Conclusions: Leptohyphidae organisms prefer fast habitats, particularly in the dry period where they find food (leaf litter, detritus) and shelter to establish themselves successfully; anthropic activities such as urbanization notably affect diversity. The high diversity recorded in this small river in the foothills of the plains reflects the need to increase this type of works and collection efforts of study material in the region.

Generalizable Deep Learning for the Detection of Incomplete and Complete Retinal Pigment Epithelium and Outer Retinal Atrophy: A MACUSTAR Report.

Purpose: The purpose of this study was to develop a deep learning algorithm for detecting and quantifying incomplete retinal pigment epithelium and outer retinal atrophy (iRORA) and complete retinal pigment epithelium and outer retinal atrophy (cRORA) in optical coherence tomography (OCT) that generalizes well to data from different devices and to validate in an intermediate age-related macular degeneration (iAMD) cohort. Methods: The algorithm comprised a domain adaptation (DA) model, promoting generalization across devices, and a segmentation model for detecting granular biomarkers defining iRORA/cRORA, which are combined into iRORA/cRORA segmentations. Manual annotations of iRORA/cRORA in OCTs from different devices in the MACUSTAR study (168 patients with iAMD) were compared to the algorithm's output. Eye level classification metrics included sensitivity, specificity, and quadratic weighted Cohen's κ score (κw). Segmentation performance was assessed quantitatively using Bland-Altman plots and qualitatively. Results: For ZEISS OCTs, sensitivity and specificity for iRORA/cRORA classification were 38.5% and 93.1%, respectively, and 60.0% and 96.4% for cRORA. For Spectralis OCTs, these were 84.0% and 93.7% for iRORA/cRORA, and 62.5% and 97.4% for cRORA. The κw scores for 3-way classification (none, iRORA, and cRORA) were 0.37 and 0.73 for ZEISS and Spectralis, respectively. Removing DA reduced κw from 0.73 to 0.63 for Spectralis. Conclusions: The DA-enabled iRORA/cRORA segmentation algorithm showed superior consistency compared to human annotations, and good generalization across OCT devices. Translational Relevance: The application of this algorithm may help toward precise and automated tracking of iAMD-related lesion changes, which is crucial in clinical settings and multicenter longitudinal studies on iAMD.

Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome.

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare condition that affects about 15% of monochorionic pregnancies, in which identical twins share a single placenta. Fetoscopic laser photocoagulation (FLP) is the standard treatment for TTTS, which significantly improves the survival of fetuses. The aim of FLP is to identify abnormal connections between blood vessels and to laser ablate them in order to equalize blood supply to both fetuses. However, performing fetoscopic surgery is challenging due to limited visibility, a narrow field of view, and significant variability among patients and domains. In order to enhance the visualization of placental vessels during surgery, we propose TTTSNet, a network architecture designed for real-time and accurate placental vessel segmentation. Our network architecture incorporates a novel channel attention module and multi-scale feature fusion module to precisely segment tiny placental vessels. To address the challenges posed by FLP-specific fiberscope and amniotic sac-based artifacts, we employed novel data augmentation techniques. These techniques simulate various artifacts, including laser pointer, amniotic sac particles, and structural and optical fiber artifacts. By incorporating these simulated artifacts during training, our network architecture demonstrated robust generalizability. We trained TTTSNet on a publicly available dataset of 2060 video frames from 18 independent fetoscopic procedures and evaluated it on a multi-center external dataset of 24 in-vivo procedures with a total of 2348 video frames. Our method achieved significant performance improvements compared to state-of-the-art methods, with a mean Intersection over Union of 78.26% for all placental vessels and 73.35% for a subset of tiny placental vessels. Moreover, our method achieved 172 and 152 frames per second on an A100 GPU, and Clara AGX, respectively. This potentially opens the door to real-time application during surgical procedures. The code is publicly available at https://github.com/SanoScience/TTTSNet.

Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography.

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.

Multi-omic signatures of sarcoidosis and progression in bronchoalveolar lavage cells.

BACKGROUND: Sarcoidosis is a heterogeneous granulomatous disease with no accurate biomarkers of disease progression. Therefore, we profiled and integrated the DNA methylome, mRNAs, and microRNAs to identify molecular changes associated with sarcoidosis and disease progression that might illuminate underlying mechanisms of disease and potential biomarkers. METHODS: Bronchoalveolar lavage cells from 64 sarcoidosis subjects and 16 healthy controls were used. DNA methylation was profiled on Illumina HumanMethylationEPIC arrays, mRNA by RNA-sequencing, and miRNAs by small RNA-sequencing. Linear models were fit to test for effect of sarcoidosis diagnosis and progression phenotype, adjusting for age, sex, smoking, and principal components of the data. We built a supervised multi-omics model using a subset of features from each dataset. RESULTS: We identified 1,459 CpGs, 64 mRNAs, and five miRNAs associated with sarcoidosis versus controls and four mRNAs associated with disease progression. Our integrated model emphasized the prominence of the PI3K/AKT1 pathway, which is important in T cell and mTOR function. Novel immune related genes and miRNAs including LYST, RGS14, SLFN12L, and hsa-miR-199b-5p, distinguished sarcoidosis from controls. Our integrated model also demonstrated differential expression/methylation of IL20RB, ABCC11, SFSWAP, AGBL4, miR-146a-3p, and miR-378b between non-progressive and progressive sarcoidosis. CONCLUSIONS: Leveraging the DNA methylome, transcriptome, and miRNA-sequencing in sarcoidosis BAL cells, we detected widespread molecular changes associated with disease, many which are involved in immune response. These molecules may serve as diagnostic/prognostic biomarkers and/or drug targets, although future testing is required for confirmation.

GIGANTEA adjusts the response to shade at dusk by directly impinging on PHYTOCHROME INTERACTING FACTOR 7 function.

For plants adapted to bright light, a decrease in the amount of light received can be detrimental to their growth and survival. Consequently, in response to shade from surrounding vegetation, they initiate a suite of molecular and morphological changes known as the shade avoidance response through which stems and petioles elongate in search for light. Under sunlight-night cycles, the plant's responsiveness to shade varies across the day, being maximal at dusk time. While a role for the circadian clock in this regulation has long been proposed, mechanistic understanding of how it is achieved is incomplete. Here, we show that the clock component GIGANTEA (GI) directly interacts with the transcriptional regulator PHYTOCHROME INTERACTING FACTOR 7 (PIF7), a key player in the response to shade. GI represses PIF7 transcriptional activity and the expression of its target genes in response to shade, thereby fine-tuning the magnitude of the response to limiting light conditions. We find that under light/dark cycles, this function of GI is required to adequately modulate the gating of the response to shade at dusk. Importantly, we also show that this circuit primarily operates in epidermal cells, highlighting the relevance of tissue-specific clock-output connections for the regulation of plant development in resonance with the environment.

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions.

Histopathological assessment of esophageal biopsies is a key part in the management of patients with Barrett esophagus (BE) but prone to observer variability and reliable diagnostic methods are needed. Artificial intelligence (AI) is emerging as a powerful tool for aided diagnosis but often relies on abstract test and validation sets while real-world behavior is unknown. In this study, we developed a 2-stage AI system for histopathological assessment of BE-related dysplasia using deep learning to enhance the efficiency and accuracy of the pathology workflow. The AI system was developed and trained on 290 whole-slide images (WSIs) that were annotated at glandular and tissue levels. The system was designed to identify individual glands, grade dysplasia, and assign a WSI-level diagnosis. The proposed method was evaluated by comparing the performance of our AI system with that of a large international and heterogeneous group of 55 gastrointestinal pathologists assessing 55 digitized biopsies spanning the complete spectrum of BE-related dysplasia. The AI system correctly graded 76.4% of the WSIs, surpassing the performance of 53 out of the 55 participating pathologists. Furthermore, the receiver-operating characteristic analysis showed that the system's ability to predict the absence (nondysplastic BE) versus the presence of any dysplasia was with an area under the curve of 0.94 and a sensitivity of 0.92 at a specificity of 0.94. These findings demonstrate that this AI system has the potential to assist pathologists in assessment of BE-related dysplasia. The system's outputs could provide a reliable and consistent secondary diagnosis in challenging cases or be used for triaging low-risk nondysplastic biopsies, thereby reducing the workload of pathologists and increasing throughput.

Dual role of valosin-containing protein (VCP/p97) in mouse sperm during capacitation.

Valosin-containing protein (VCP; aka p97), a member of the AAA (ATPases Associated with various cellular Activities) family, has been associated with a wide range of cellular functions. While previous evidence has shown its presence in mammalian sperm, our study unveils its function in mouse sperm. Notably, we found that mouse VCP does not undergo tyrosine phosphorylation during capacitation and exhibits distinct localization patterns. In the sperm head, it resides within the equatorial segment and, following acrosomal exocytosis, it is released and cleaved. In the flagellum, VCP is observed in the principal and midpiece. Furthermore, our research highlights a unique role for VCP in the cAMP/PKA pathway during capacitation. Pharmacological inhibition of sperm VCP led to reduced intracellular cAMP levels that resulted in decreased phosphorylation in PKA substrates and tyrosine residues and diminished fertilization competence. Our results show that in mouse sperm, VCP plays a pivotal role in regulating cAMP production, probably by the modulation of soluble adenylyl cyclase activity.

Incremento del riesgo hemorrágico en pacientes con fibrilación auricular y enfermedad renal crónica en hemodiálisis tratados con anticoagulantes. Serie de cuatro centros / Increased risk of bleeding in patients with atrial fibrillation and chronic kidney disease on hemodialysis treated with anticoagulants. A four center series

Resumen Objetivo: El objetivo de este estudio fue estimar si el uso de anticoagulantes se asociaba con una diferencia en la frecuencia de trombosis de cualquier sitio, hemorragia mayor y mortalidad en adultos con coexistencia de ambas patologías. Método: Se realizó un estudio de cohorte retrospectivo en cuatro centros de alta complejidad. Se incluyeron mayores de 18 años con ERC en hemodiálisis y FA no valvular, con indicación de anticoagulación (CHA2DS2VASc ≥ 2). El desenlace primario fue la ocurrencia de sangrado mayor, evento trombótico (accidente vascular cerebral, infarto agudo al miocardio o enfermedad tromboembólica venosa) o muerte. Se realizó ajuste por variables de confusión por regresión logística. Resultados: De los 158 pacientes incluidos, el 61% (n = 97) recibieron anticoagulante. El desenlace principal se encontró en el 84% de quienes recibieron anticoagulación y en el 70% de quienes no la recibieron (OR: 2.12, IC95%: 0.98-4.57; luego del ajuste OR: 2.13, IC95%: 1.04-4.36). De los desenlaces mayores se presentaron sangrado en el 52% vs. el 34% (OR: 2.03; IC95%: 1.05-3.93), trombosis en el 35% vs. el 34% (OR: 1.03; IC95%: 0.52-2.01) y muerte en el 46% vs. el 41% (OR: 1.25; IC95%: 0.65-2.38). Conclusiones: Los resultados de este estudio sugieren un incremento en el riesgo de sangrado en los pacientes con FA y ERC en hemodiálisis que reciben anticoagulación, sin disminución del riesgo de eventos trombóticos ni de muerte.

Abstract Objective: The aim of this study was to estimate whether the consumption of anticoagulants was associated with a difference in the frequency of thrombosis of any site, major bleeding and mortality, in adults with both diseases. Method: A retrospective cohort study was carried out in four high complexity centers. Patients older than 18 years with CKD on hemodialysis and non-valvular AF, with an indication for anticoagulation (CHA2DS2VASc ≥ 2), were included. The primary outcome was the occurrence of: major bleeding, thrombotic event (cerebrovascular accident, acute myocardial infarction or venous thromboembolic disease) or death. Adjustment for confounding variables was performed using logistic regression. Results: From 158 patients included, 61% (n = 97) received an anticoagulant. The main outcome was found in 84% of those who received anticoagulation and 70% of those who did not (OR: 2.12, 95%CI: 0.98-4.57; after the adjusted analysis OR: 2.13, 95%CI: 1.04-4.36). Separate outcomes were bleeding in 52% vs. 34% (OR: 2.03; 95%CI: 1.05-3.93), thrombosis in 35% vs. 34% (OR: 1.03; 95%CI: 0.52-2-01) and death in 46% vs. 41% (OR: 1.25; 95%CI: 0.65-2.38). Conclusions: The results of this study suggest an increased risk of bleeding in patients with AF and CKD on hemodialysis receiving anticoagulation, without a decrease in the risk of thrombotic events or all-cause mortality.

Nano-organization of synaptic calcium signaling.

Recent studies suggest an exquisite structural nano-organization within single synapses, where sites of evoked fusion - marked by clustering of synaptic vesicles, active zone proteins and voltage-gated calcium channels - are directly juxtaposed to postsynaptic receptor clusters within nanocolumns. This direct nanometer scale alignment between presynaptic fusion apparatus and postsynaptic receptors is thought to ensure the fidelity of synaptic signaling and possibly allow multiple distinct signals to occur without interference from each other within a single active zone. The functional specificity of this organization is made possible by the inherent nano-organization of calcium signals, where all the different calcium sources such as voltage-gated calcium channels, intracellular stores and store-operated calcium entry have dedicated local targets within their nanodomain to ensure precision of action. Here, we discuss synaptic nano-organization from the perspective of calcium signals, where some of the principal findings from early work in the 1980s continue to inspire current studies that exploit new genetic tools and super-resolution imaging technologies.

Galunisertib downregulates mutant type I collagen expression and promotes MSCs osteogenesis in pediatric osteogenesis imperfecta.

Qualitative alterations in type I collagen due to pathogenic variants in the COL1A1 or COL1A2 genes, result in moderate and severe Osteogenesis Imperfecta (OI), a rare disease characterized by bone fragility. The TGF-ß signaling pathway is overactive in OI patients and certain OI mouse models, and inhibition of TGF-ß through anti-TGF-ß monoclonal antibody therapy in phase I clinical trials in OI adults is rendering encouraging results. However, the impact of TGF-ß inhibition on osteogenic differentiation of mesenchymal stem cells from OI patients (OI-MSCs) is unknown. The following study demonstrates that pediatric skeletal OI-MSCs have imbalanced osteogenesis favoring the osteogenic commitment. Galunisertib, a small molecule inhibitor (SMI) that targets the TGF-ß receptor I (TßRI), favored the final osteogenic maturation of OI-MSCs. Mechanistically, galunisertib downregulated type I collagen expression in OI-MSCs, with greater impact on mutant type I collagen, and concomitantly, modulated the expression of unfolded protein response (UPR) and autophagy markers. In vivo, galunisertib improved trabecular bone parameters only in female oim/oim mice. These results further suggest that type I collagen is a tunable target within the bone ECM that deserves investigation and that the SMI, galunisertib, is a promising new candidate for the anti-TGF-ß targeting for the treatment of OI.

Can linezolid be validly measured in endotracheal aspiration in critically ill patients? A proof-of-concept trial.

BACKGROUND: Therapeutic drug monitoring (TDM) of anti-infectives such as linezolid is routinely performed in blood of intensive care unit (ICU) patients to optimize target attainment. However, the concentration at the site of infection is considered more important for a successful therapy. Until now, bronchoalveolar lavage (BAL) is the gold standard to measure intrapulmonary concentrations of anti-infective agents. However, it is an invasive method and unsuitable for regular TDM. The aim of this proof-of-concept study was to investigate whether it is possible to reliably determine the intrapulmonary concentration of linezolid from endotracheal aspiration (ENTA). METHODS: Intubated ICU patients receiving 600 mg intravenous linezolid twice daily were examined in steady state. First, preliminary experiments were performed in six patients to investigate which patients are suitable for linezolid measurement in ENTA. In a second step, trough and peak linezolid concentrations of plasma and ENTA were determined in nine suitable patients. RESULTS: Linezolid can validly be detected in ENTA with viscous texture and > 0.5 mL volume. The mean (SD) linezolid trough concentration was 2.02 (1.27) mg/L in plasma and 1.60 (1.36) mg/L in ENTA, resulting in a median lung penetration rate of 104%. The mean (SD) peak concentration in plasma and ENTA was 10.77 (5.93) and 4.74 (2.66) mg/L. CONCLUSIONS: Linezolid can validly be determined in ENTA with an adequate texture and volume. The penetration rate is comparable to already published BAL concentrations. This method might offer a simple and non-invasive method for TDM at the site of infection "lung". Due to promising results of the feasibility study, comparison of ENTA and BAL in the same patient should be investigated in a further trial.

Fate of synthetic chemicals in the agronomic insect pest Spodoptera littoralis: experimental feeding-contact assay and toxicokinetic model.

Insecticides prevent or reduce insect crop damage, maintaining crop quality and quantity. Physiological traits, such as an insect's feeding behavior, influence the way insecticides are absorbed and processed in the body (toxicokinetics), which can be exploited to improve species selectivity. To fully understand the uptake of insecticides, it is essential to study their total uptake and toxicokinetics independent of their toxic effects on insects. We studied the toxicokinetics (TK) of insecticidally inactive test compounds incorporating agro-like structural motifs in larvae of the Egyptian cotton leafworm (Spodoptera littoralis, Lepidoptera), and their distribution across all biological matrices, using laboratory experiments and modeling. We measured Spodoptera larval behavior and temporal changes of whole-body concentrations of test compounds during feeding on treated soybean leaf disks and throughout a subsequent depuration period. Differences in the distribution of the total quantities of compounds were found between the biological matrices leaf, larva, and feces. Rate constants for uptake and elimination of test compounds were derived by calibrating a toxicokinetic model to the whole-body concentrations. Uptake and elimination rate constants depended on the physicochemical properties of the test compounds. Increasing hydrophobicity increased the bioaccumulation potential of test compounds. Incomplete quantities in larval matrices indicated that some compounds may undergo biotransformation. As fecal excretion was a major elimination pathway, the variable time of release and number of feces pellets led to a high variability in the body burden. We provide quantitative models to predict the toxicokinetics and bioaccumulation potential of inactive insecticide analogs (parent compounds) in Spodoptera.

Brain-derived neurotrophic factor scales presynaptic calcium transients to modulate excitatory neurotransmission.

Brain-derived neurotrophic factor (BDNF) plays a critical role in synaptic physiology, as well as mechanisms underlying various neuropsychiatric diseases and their treatment. Despite its clear physiological role and disease relevance, BDNF's function at the presynaptic terminal, a fundamental unit of neurotransmission, remains poorly understood. In this study, we evaluated single synapse dynamics using optical imaging techniques in hippocampal cell cultures. We find that exogenous BDNF selectively increases evoked excitatory neurotransmission without affecting spontaneous neurotransmission. However, acutely blocking endogenous BDNF has no effect on evoked or spontaneous release, demonstrating that different approaches to studying BDNF may yield different results. When we suppressed BDNF-Tropomyosin receptor kinase B (TrkB) activity chronically over a period of days to weeks using a mouse line enabling conditional knockout of TrkB, we found that evoked glutamate release was significantly decreased while spontaneous release remained unchanged. Moreover, chronic blockade of BDNF-TrkB activity selectively downscales evoked calcium transients without affecting spontaneous calcium events. Via pharmacological blockade by voltage-gated calcium channel (VGCC) selective blockers, we found that the changes in evoked calcium transients are mediated by the P/Q subtype of VGCCs. These results suggest that BDNF-TrkB activity increases presynaptic VGCC activity to selectively increase evoked glutamate release.

Nodule Detection and Generation on Chest X-Rays: NODE21 Challenge.

Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lung nodules in chest X-rays. However, the lack of gold-standard public datasets slows down the progression of the research and prevents benchmarking of methods for this task. To address this, we organized a public research challenge, NODE21, aimed at the detection and generation of lung nodules in chest X-rays. While the detection track assesses state-of-the-art nodule detection systems, the generation track determines the utility of nodule generation algorithms to augment training data and hence improve the performance of the detection systems. This paper summarizes the results of the NODE21 challenge and performs extensive additional experiments to examine the impact of the synthetically generated nodule training images on the detection algorithm performance.

Activation of endodontic irrigants using a 9.3 µm CO2 and diode lasers: A laboratory proof of concept model.

PURPOSE: To investigate the differences between irrigant propagation and temperature changes using laser-activated irrigation (LAI) at different settings in an artificial root canal model. METHODS: Using an artificial resin root canal model, irrigant activation was achieved in 19 experimental groups with eight samples each. A 9,300 nm CO2 laser, two diode lasers with different settings (wavelengths 455, 808, 970, and 980 nm) were compared to 2,940 nm Er:YAG laser and traditional needle irrigation. Er:YAG and CO2 laser were activated in the pulpal chamber only, while diode lasers and needles were inserted into the main root canal. Lasers were activated for 5x 20 seconds resulting in 100 seconds of activation or rinsing for each sample. After each activation of 20 seconds, a photo was taken of the side canals and the propagation of the dye was measured with a digital measuring tool after calibration. Further, the temperature of the irrigant was reported after activation of 20 seconds and repeated 5 times. Data were checked for normality and statistically compared. RESULTS: All lasers increased the irrigant propagation compared to conventional irrigation. Significant differences were found between groups regarding propagation and temperature (P< 0.0027). Er:YAG and CO2 laser had similar effects on irrigant propagation in middle and apical located side-canals with specific power parameters and were superior to diode lasers and syringe irrigation. The irrigant's temperature increased significantly with the diode and CO2 lasers. CLINICAL SIGNIFICANCE: Diode lasers and CO2 lasers have not been established for irrigant activation. 9,300 nm CO2 lasers absorb well in water and were shown to introduce vapor bubble formation and streaming in water. Diode lasers are highly accepted in periodontics. The laser light is not absorbed in water but interacts with bacteria as well as soft tissues and contributes therefore to infection control. With a modified laser tip it was however possible to introduce cavitation and streaming in irrigants.

[Increased risk of bleeding in patients with atrial fibrillation and chronic kidney disease on hemodialysis treated with anticoagulants. A four center serie]. / Incremento del riesgo hemorrágico en pacientes con fibrilación auricular y enfermedad renal crónica en hemodiálisis tratados con anticoagulantes. Serie de cuatro centros.

Background: Because the benefits and risks of anticoagulation are still unknown in patients with atrial fibrillation (AF) and with chronic kidney disease (CKD) on hemodialysis. Objective: The aim of this study was to estimate whether the consumption of anticoagulants was associated with a difference in the frequency of thrombosis of any site, major bleeding and mortality, in adults with both diseases. Method: A retrospective cohort study was carried out in four high complexity centers. Patients older than 18 years with CKD on hemodialysis and non-valvular AF, with an indication for anticoagulation (CHA2DS-2VASc ≥ 2), were included. The primary outcome was the occurrence of: major bleeding, thrombotic event (cerebrovascular accident, acute myocardial infarction or venous thromboembolic disease) or death. Adjustment for confounding variables was performed using logistic regression. Results: From 158 patients included, 61% (n = 97) received an anticoagulant. The main outcome was found in 84% of those who received anticoagulation and 70% of those who did not (OR: 2.12, 95%CI: 0.98-4.57; after the adjusted analysis OR: 2.13, 95%CI: 1.04-4.36). Separate outcomes were bleeding in 52% vs. 34% (OR: 2.03; 95%CI: 1.05-3.93), thrombosis in 35% vs. 34% (OR: 1.03; 95%CI: 0.52-2-01) and death in 46% vs 41% (OR: 1.25; 95%CI: 0.65-2.38). Conclusions: The results of this study suggest an increased risk of bleeding in patients with AF and CKD on hemodialysis receiving anticoagulation, without a decrease in the risk of thrombotic events or all-cause mortality.

Antecedentes: Puesto que se desconocen el beneficio y los riesgos de la anticoagulación en pacientes con fibrilación auricular (FA) y enfermedad renal crónica (ERC) terminal en hemodiálisis. Objetivo: El objetivo de este estudio fue estimar si el uso de anticoagulantes se asociaba con una diferencia en la frecuencia de trombosis de cualquier sitio, hemorragia mayor y mortalidad en adultos con coexistencia de ambas patologías. Método: Se realizó un estudio de cohorte retrospectivo en cuatro centros de alta complejidad. Se incluyeron mayores de 18 años con ERC en hemodiálisis y FA no valvular, con indicación de anticoagulación (CHA2DS2VASc ≥ 2). El desenlace primario fue la ocurrencia de sangrado mayor, evento trombótico (accidente vascular cerebral, infarto agudo al miocardio o enfermedad tromboembólica venosa) o muerte. Se realizó ajuste por variables de confusión por regresión logística. Resultados: De los 158 pacientes incluidos, el 61% (n = 97) recibieron anticoagulante. El desenlace principal se encontró en el 84% de quienes recibieron anticoagulación y en el 70% de quienes no la recibieron (OR: 2.12, IC95%: 0.98-4.57; luego del ajuste OR: 2.13, IC95%: 1.04-4.36). De los desenlaces mayores se presentaron sangrado en el 52% vs. el 34% (OR: 2.03; IC95%: 1.05-3.93), trombosis en el 35% vs. el 34% (OR: 1.03; IC95%: 0.52-2.01) y muerte en el 46% vs. el 41% (OR: 1.25; IC95%: 0.65-2.38). Conclusiones: Los resultados de este estudio sugieren un incremento en el riesgo de sangrado en los pacientes con FA y ERC en hemodiálisis que reciben anticoagulación, sin disminución del riesgo de eventos trombóticos ni de muerte.

A perioperative layered autologous tissue expansion graft for hollow organ repair.

Tissue engineering has not been widely adopted in clinical settings for several reasons, including technical challenges, high costs, and regulatory complexity. Here, we introduce the Perioperative Layered Autologous Tissue Expansion graft (PLATE graft), a composite biomaterial and collagen-reinforced construct with autologous epithelium on one side and smooth muscle tissue on the other. Designed to mimic the structure and function of natural hollow organs, the PLATE graft is unique in that it can be produced in a standard operating theatre and is cost-effective. In this proof-of-principle study, we test its regenerative performance in eight different organs, present biomechanical and permeability tests, and finally explore its in vivo performance in live rabbits.

On retrospective k-space subsampling schemes for deep MRI reconstruction.

Acquiring fully-sampled MRI k-space data is time-consuming, and collecting accelerated data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling schemes is a conventional approach for accelerated acquisitions; however, this often results in imprecise reconstructions, even with the use of Deep Learning (DL), especially at high acceleration factors. Non-rectilinear or non-Cartesian trajectories can be implemented in MRI scanners as alternative subsampling options. This work investigates the impact of the k-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models. The Recurrent Variational Network (RecurrentVarNet) was used as the DL-based MRI-reconstruction architecture. Cartesian, fully-sampled multi-coil k-space measurements from three datasets were retrospectively subsampled with different accelerations using eight distinct subsampling schemes: four Cartesian-rectilinear, two Cartesian non-rectilinear, and two non-Cartesian. Experiments were conducted in two frameworks: scheme-specific, where a distinct model was trained and evaluated for each dataset-subsampling scheme pair, and multi-scheme, where for each dataset a single model was trained on data randomly subsampled by any of the eight schemes and evaluated on data subsampled by all schemes. In both frameworks, RecurrentVarNets trained and evaluated on non-rectilinearly subsampled data demonstrated superior performance, particularly for high accelerations. In the multi-scheme setting, reconstruction performance on rectilinearly subsampled data improved when compared to the scheme-specific experiments. Our findings demonstrate the potential for using DL-based methods, trained on non-rectilinearly subsampled measurements, to optimize scan time and image quality.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.