Functional diversity of bacterial microbiota associated with the toxigenic benthic dinoflagellate Prorocentrum.

Interactions between bacterial microbiota and epibenthic species of the dinoflagellate Prorocentrum may define the onset and persistence of benthic harmful algal blooms (bHABs). Chemical ecological interactions within the dinoflagellate phycosphere potentially involve a complex variety of organic molecules, metabolites, and toxins, including undefined bioactive compounds. In this study, the bacterial diversity and core members of the dinoflagellate-associated microbiota were defined from 11 strains of three epibenthic Prorocentrum species, representing three geographically disjunct locations within Mexican coastal waters. Microbiota profiles in stable monoclonal Prorocentrum cultures were obtained by sequencing amplicons of the V3-V4 region of the 16S rRNA gene. Thirteen classes of bacteria were identified among dinoflagellate clones, where Alphaproteobacteria, Gammaproteobacteria, and Bacteroidia were consistently dominant. The bacterial community structure exhibited significantly different grouping by the location of origin of dinoflagellate clones. No significant diversity difference was found among free-living or unattached bacteria in the dinoflagellate culture medium (M) compared with those in closer association with the dinoflagellate host cells (H). Twelve taxa were defined as core members of the bacterial assemblage, representing the genera Algiphilus, Cohaesibacter, Labrenzia, Mameliella, Marinobacter, Marivita, Massilia, Muricauda, Roseitalea, and an unclassified member of the Rhodobacteraceae. The core members are inferred to significantly contribute to primary and secondary metabolic functions, but no direct correlation with dinoflagellate toxigenicity was apparent. Overall the bacterial profile and implied gene functionality indicated a suite of positive interactions, suggesting either mutualism or commensalism with the dinoflagellate. The further characterization and interpretation of specific gene functions and interactions between bacteria and dinoflagellates, such as epibenthic members of genus Prorocentrum, are key to understanding their role in toxigenesis and bHAB development.

Vascular Anastomoses and Dissection: A Six-Part Simulation Curriculum for Surgical Residents.

Introduction: As surgical technologies grow, so too do demands on surgical trainees to master increasing numbers of skill sets. With the rise of endovascular surgery, trainees have fewer opportunities to practice open vascular techniques in the operating room. Simulation can bridge this gap. However, existing published open vascular simulation curricula are basic or based on expensive models. Methods: We iteratively developed an open vascular skills curriculum for second-year surgery residents comprising six 2-hour sessions. We refined the curriculum based on feedback from learners and faculty. The curriculum required skilled facilitators, vascular instruments, and tissue models. We evaluated the latest iteration with a survey and by assessing participants' technical skills using the Objective Structured Assessment of Technical Skills (OSATS) form. Results: Over the past 10 years, 101 residents have participated in the curriculum. Nine of 13 residents who participated in the latest curricular iteration completed the survey. All respondents rated the sessions as excellent and strongly agreed that they had improved their abilities to perform anastomoses with tissue and prosthetic. Facilitators completed 18 OSATS forms for residents in the fifth and sixth sessions of the latest iteration. Residents scored well overall, with a median 26.5 (interquartile range: 24-29) out of a possible score of 35, with highest scores on knowledge of instruments. Discussion: This simulation-based curriculum facilitates open vascular surgical skill acquisition among surgery residents. The curriculum allows residents to acquire critical vascular skills that are challenging to learn in an increasingly demanding operative setting.

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.

The hard life of an octopus embryo is seen through gene expression, energy metabolism, and its ability to neutralize radical oxygen species.

The reproductive process in Octopus maya was analyzed to establish the amount of reactive oxygen species that the embryos inherit from females, during yolk synthesis. At the same time, respiratory metabolism, ROS production, and the expression of some genes of the antioxidant system were monitored to understand the ability of embryos to neutralize maternal ROS and those produced during development. The results indicate that carbonylated proteins and peroxidized lipids (LPO) were transferred from females to the embryos, presumably derived from the metabolic processes carried out during yolk synthesis in the ovary. Along with ROS, females also transferred to embryos glutathione (GSH), a key element of the antioxidant defense system, thus facilitating the neutralization of inherited ROS and those produced during development. Embryos are capable of neutralizing ROS thanks to the early expression of genes such as catalase (CAT) and superoxide dismutase (SOD), which give rise to the synthesis of enzymes when the circulatory system is activated. Also, it was observed that the levels of the routine metabolic rate of embryos are almost as high as those of the maximum activity metabolism, which leads, on the one hand, to the elevated production of ROS and suggests that, at this stage of the life cycle in octopuses, energy production is maximum and is physically limited by the biological properties inherent to the structure of embryonic life (oxygen transfer through the chorion, gill surface, pumping capacity, etc.). Due to its role in regulating vascularization, a high expression of HIf-1A during organogenesis suggests that circulatory system development has begun in this phase of embryo development. The results indicate that the routine metabolic rate and the ability of O. maya embryos to neutralize the ROS are probably the maximum possible. Under such circumstances, embryos cannot generate more energy to combat the free radicals produced by their metabolism, even when environmental factors such as high temperatures or contaminants could demand excess energy.

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.

Structure and Spectroscopic Insights for CH3PCO Isomers: A High-Level Quantum Chemical Study.

The exploration of phosphorus-bearing species stands as a prolific field in current astrochemical research, particularly within the context of prebiotic chemistry. Herein, we have employed high-level quantum chemistry methodologies to predict the structure and spectroscopic properties of isomers composed of a methyl group and three P, C, and O atoms. We have computed relative and dissociation energies, as well as rotational, rovibrational, and torsional parameters using the B2PLYPD3 functional and the explicitly correlated coupled cluster CCSD(T)-F12b method. Based upon our study, all the isomers exhibit a bent heavy atom skeleton with CH3PCO being the most stable structure, regardless of the level theory employed. Following in energy, we found four high-energy isomers, namely, CH3OCP, CH3CPO, CH3COP, and CH3OPC. The computed adiabatic dissociation energies support the stability of all [CH3, P, C, O] isomers against fragmentation into CH3 and [P, C, O]. Torsional barrier heights associated with the methyl internal rotation for each structure have been computed to evaluate the occurrence of possible A-E splittings in the rotational spectra. For the most stable isomer, CH3PCO, we found a V3 barrier of 82 cm-1, which is slightly larger than that obtained experimentally for the N-counterpart, CH3NCO, yet still very low. Therefore, the analysis of its rotational spectrum can be anticipated as a challenging task owing to the effect of the CH3 internal rotation. The complete set of spectroscopic constants and transition frequencies reported here for the most stable isomer, CH3PCO, is intended to facilitate eventual laboratory searches.

The Utility of Prevena Negative Pressure Wound Therapy on Groin Incisions for Critical Limb-Threatening Ischemia: A Single Institution Experience.

BACKGROUND: Incisional negative pressure wound therapy (iNPWT) is an adjunctive treatment that uses constant negative pressure suction to facilitate healing. The utility of this treatment modality on vascular operations for critical limb-threatening ischemia (CLTI) has yet to be elucidated. This study compares the incidence of postoperative wound complications between the Prevena Incision Management System, a type of iNPWT, and standard wound dressings for vascular patients who also underwent plastic surgery closure of groin incisions for CLTI. METHOD: We performed a retrospective cohort study of 40 patients with CLTI who underwent 53 open vascular surgeries with subsequent sartorius muscle flap closure. Patient demographics, intraoperative details, and wound complications were measured from 2015 to 2018 at the University of California San Francisco. Two cohorts were generated based on the modality of postoperative wound management and compared on wound healing outcomes. RESULTS: Of the 53 groin incisions, 29 were managed with standard dressings, and 24 received iNPWT. Patient demographics, comorbidities, and operative characteristics were similar between the 2 groups. Patients who received iNPWT had a significantly lower rate of infection (8.33% vs 31.0%, P = 0.04) and dehiscence (0% vs 41.3%, P < 0.01). Furthermore, the iNPWT group had a significantly lower rate of reoperation (0% vs 17.2%, P = 0.03) for wound complications within 30 days compared with the control group and a moderately reduced rate of readmission (4.17% vs 20.7%, P = 0.08). CONCLUSIONS: Rates of infection, reoperation, and dehiscence were significantly reduced in patients whose groin incisions were managed with iNPWT compared with standard wound care. Readmission rates were also decreased, but this difference was not statistically significant. Our results suggest that implementing iNPWT for the management of groin incisions, particularly in patients undergoing vascular operations for CLTI, may significantly improve clinical outcomes.

Guía de práctica clínica para el tratamiento y seguimiento de personas con cáncer diferenciado de tiroides. Parte I: Vigilancia activa

Introducción: El cáncer de tiroides es una enfermedad frecuente en el mundo, con mayor prevalencia del tipo diferenciado. El diagnóstico temprano y manejo pertinente, individualizado y adaptable puede mejorar su pronóstico. Objetivo: Generar recomendaciones basadas en evidencia sobre el tratamiento y seguimiento de personas adultas con cáncer diferenciado de tiroides (CDT). Metodología: Guía de práctica clínica (GPC) a partir de revisión sistemática de literatura (RSL) y consenso de expertos clínicos. El grupo desarrollador definió el alcance y cuatro preguntas que se resolvieron a través de revisión de evidencia de GPC existentes, RSL, estudios primarios publicadas en español o inglés en diferentes fuentes de información desde 2013. Las preguntas de investigación fueron: 1. ¿Cuáles son las indicaciones de la vigilancia activa?, ¿cómo realizarla?, ¿cuándo y con que periodicidad realizarla? 2. ¿Cuál es el tratamiento y su indicación en pacientes con nódulos tiroideos sospechosos de cáncer? 3. ¿Cómo y cuándo realizar seguimiento de pacientes con CDT de acuerdo con el riesgo dinámico? 4. ¿Cuál es el manejo actual de los pacientes iodo refractarios? Se propusieron recomendaciones basadas en la evidencia, y analizadas y discutidas por el colectivo experto en sesiones asincrónicas. Se evalúo la calidad de la evidencia y las recomendaciones fueron gradadas en fuerte o condicional y a favor o en contra a partir del análisis de la calidad de la evidencia, contexto de implementación (disponibilidad e implementación) y la experticia clínica. En el presente documento se desarrollada la primera pregunta, referente a vigilancia activa. Resultados: 86 recomendaciones fueron propuestas y acordadas por el grupo desarrollador, categorizadas en tratamiento y seguimiento para resolver las preguntas planteadas. 10 de las recomendaciones corresponden a vigilancia activa y se incluyen en el presente documento. Recomendaciones claves incluyen, brindar información completa y oportuna a pacientes, conformación de equipos multidisciplinarios, análisis individualizado del paciente para la decisión de tratamiento, estadificación rutinaria de riesgo dinámico para evaluar la respuesta al tratamiento y ajustarlo, minimización de procedimientos fútiles o que aportan poco a la supervivencia y calidad de vida de los pacientes. Conclusión: Se presentan recomendaciones que esperan incidir en la estandarización de la práctica clínica cotidiana de pacientes con CDT y mejores resultados en salud.

Introduction: Thyroid cancer is a common disease in the world, with a higher prevalence of the differentiated type. Early diagnosis individualized and adaptive management can improve prognosis. Objective: Generate evidence-based recommendations on the treatment and follow-up of adults with differentiated thyroid carcinoma (DTC). Methodology: Clinical practice guideline (CPG) based on systematic literature review (RSL) and consensus of clinical experts. The development group defined the range and four questions that were resolved through a review of evidence from existing CPGs, RSLs, primary studies published in Spanish or English in various sources of information since 2013. The research questions were: 1. What are the indications for active surveillance? How to carry it out? When and how often to carry it out? 2. What is the treatment and its indication in patients with thyroid nodules suspicious for cancer? 3. How and when to follow up patients with CDT according to dynamic risk? 4. What is the current management of iodine refractory patients? Evidence-based recommendations analyzed and discussed by the expert group in asynchronous sessions were proposed. The quality of the evidence was evaluated, and the recommendations were graded as strong or conditional and in favor or against based on the analysis of the quality of the evidence, implementation context (availability and implementation) and clinical expertise. In this document, is developed the first question, referring to active surveillance. Results: 86 recommendations were proposed and agreed upon by the development group, categorized into treatment and follow-up to solve the questions raised. 10 of the recommendations correspond to active surveillance and are included in this document. Key recommendations include providing complete and timely information to patients, develop of multidisciplinary teams, individualized patient analysis for treatment decisions, routine dynamic risk staging to evaluate response to treatment and adjust it, minimization of futile procedures or that contribute little to the survival and quality of life of patients. Conclusion: Recommendations are presented that longs to influence the standardization of the daily clinical practice of patients with DTC and better health outcomes.

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.

Study on Miscibility, Thermomechanical Behavior, and Thermoregulation Performance of Paraffin Wax/Bituminous Blends for Solar Thermal Energy Storage Applications.

The goal of this work was to study the miscibility, thermal stability, thermomechanical properties, and temperature regulation performance of paraffin wax/bitumen blends for their potential use in solar thermal energy storage applications. Results indicated that these blends present a suitable thermal stability, and their thermomechanical properties are strongly dependent on composition, developed microstructure, and temperature. Among all paraffin wax concentrations studied, the blend containing 40 wt % paraffin wax displays enhanced binder elastic properties together with lower thermal susceptibility compared to base bitumen. In addition, this binder also presents improved thermal properties (thermal conductivity and specific heat capacity) and still maintains a high crystallinity, thereby retaining a large enough latent heat to be used for thermal energy storage. Thus, results from the temperature regulation test, which was conducted by subjecting the sample to simulated solar irradiation at a constant radiant flux density, provide a higher latent heat thermoregulation index value than other microencapsulated phase change materials systems. Therefore, it can be stated that paraffin wax/bitumen blends are promising base materials to formulate form-stable products for thermal energy storage applications for thermoregulation purposes.

Dietary fatty acid composition drives neuroinflammation and impaired behavior in obesity.

Nutrient composition in obesogenic diets may influence the severity of disorders associated with obesity such as insulin-resistance and chronic inflammation. Here we hypothesized that obesogenic diets rich in fat and varying in fatty acid composition, particularly in omega 6 (ω6) to omega 3 (ω3) ratio, have various effects on energy metabolism, neuroinflammation and behavior. Mice were fed either a control diet or a high fat diet (HFD) containing either low (LO), medium (ME) or high (HI) ω6/ω3 ratio. Mice from the HFD-LO group consumed less calories and exhibited less body weight gain compared to other HFD groups. Both HFD-ME and HFD-HI impaired glucose metabolism while HFD-LO partly prevented insulin intolerance and was associated with normal leptin levels despite higher subcutaneous and perigonadal adiposity. Only HFD-HI increased anxiety and impaired spatial memory, together with increased inflammation in the hypothalamus and hippocampus. Our results show that impaired glucose metabolism and neuroinflammation are uncoupled, and support that diets with a high ω6/ω3 ratio are associated with neuroinflammation and the behavioral deterioration coupled with the consumption of diets rich in fat.

Neurobehavioral Precursors of Compulsive Cocaine Seeking in Dual Frontostriatal Circuits.

Background: Only some individuals who use drugs recreationally eventually develop a substance use disorder, characterized in part by the rigid engagement in drug foraging behavior (drug seeking), which is often maintained in the face of adverse consequences (i.e., is compulsive). The neurobehavioral determinants of this individual vulnerability have not been fully elucidated. Methods: Using a prospective longitudinal study involving 39 male rats, we combined multidimensional characterization of behavioral traits of vulnerability to stimulant use disorder (impulsivity and stickiness) and resilience (sign tracking and sensation seeking/locomotor reactivity to novelty) with magnetic resonance imaging to identify the structural and functional brain correlates of the later emergence of compulsive drug seeking in drug-naïve subjects. We developed a novel behavioral procedure to investigate the individual tendency to persist in drug-seeking behavior in the face of punishment in a drug-free state in subjects with a prolonged history of cocaine seeking under the control of the conditioned reinforcing properties of a drug-paired Pavlovian conditioned stimulus. Results: In drug-naïve rats, the tendency to develop compulsive cocaine seeking was characterized by behavioral stickiness-related functional hypoconnectivity between the prefrontal cortex and posterior dorsomedial striatum in combination with impulsivity-related structural alterations in the infralimbic cortex, anterior insula, and nucleus accumbens. Conclusions: These findings show that the vulnerability to developing compulsive cocaine-seeking behavior stems from preexisting structural or functional changes in two distinct corticostriatal systems that underlie deficits in impulse control and goal-directed behavior.

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.

Acid-sensing ion channel 3 mediates pain hypersensitivity associated with high-fat diet consumption in mice.

ABSTRACT: Lipid-rich diet is the major cause of obesity, affecting 13% of the worldwide adult population. Obesity is a major risk factor for metabolic syndrome that includes hyperlipidemia and diabetes mellitus. The early phases of metabolic syndrome are often associated with hyperexcitability of peripheral small diameter sensory fibers and painful diabetic neuropathy. Here, we investigated the effect of high-fat diet-induced obesity on the activity of dorsal root ganglion (DRG) sensory neurons and pain perception. We deciphered the underlying cellular mechanisms involving the acid-sensing ion channel 3 (ASIC3). We show that mice made obese through consuming high-fat diet developed the metabolic syndrome and prediabetes that was associated with heat pain hypersensitivity, whereas mechanical sensitivity was not affected. Concurrently, the slow conducting C fibers in the skin of obese mice showed increased activity on heating, whereas their mechanosensitivity was not altered. Although ASIC3 knockout mice fed with high-fat diet became obese, and showed signs of metabolic syndrome and prediabetes, genetic deletion, and in vivo pharmacological inhibition of ASIC3, protected mice from obesity-induced thermal hypersensitivity. We then deciphered the mechanisms involved in the heat hypersensitivity of mice and found that serum from high-fat diet-fed mice was enriched in lysophosphatidylcholine (LPC16:0, LPC18:0, and LPC18:1). These enriched lipid species directly increased the activity of DRG neurons through activating the lipid sensitive ASIC3 channel. Our results identify ASIC3 channel in DRG neurons and circulating lipid species as a mechanism contributing to the hyperexcitability of nociceptive neurons that can cause pain associated with lipid-rich diet consumption and obesity.

Defects in AMPAR trafficking and microglia activation underlie socio-cognitive deficits associated to decreased expression of phosphodiesterase 2 a.

Phosphodiesterase 2 A (PDE2A) is an enzyme involved in the homeostasis of cAMP and cGMP and is the most highly expressed PDE in human brain regions critical for socio-cognitive behavior. In cerebral cortex and hippocampus, PDE2A expression level is upregulated in Fmr1-KO mice, a model of the Fragile X Syndrome (FXS), the most common form of inherited intellectual disability (ID) and autism spectrum disorder (ASD). Indeed, PDE2A translation is negatively modulated by FMRP, whose functional absence causes FXS. While the pharmacological inhibition of PDE2A has been associated to its pro-cognitive role in normal animals and in models of ID and ASD, homozygous PDE2A mutations have been identified in patients affected by ID, ASD and epilepsy. To clarify this apparent paradox about the role of PDE2A in brain development, we characterized here Pde2a+/- mice (homozygote animals being not viable) at the behavioral, cellular, molecular and electrophysiological levels. Pde2a+/- females display a milder form of the disorder with reduced cognitive performance in adulthood, conversely males show severe socio-cognitive deficits throughout their life. In males, these phenotypes are associated with microglia activation, elevated glutathione levels and increased externalization of Glutamate receptor (GluR1) in CA1, producing reduced mGluR-dependent Long-term Depression. Overall, our results reveal molecular targets of the PDE2A-dependent pathway underlying socio-cognitive performance. These results clarify the mechanism of action of pro-cognitive drugs based on PDE2A inactivation, which have been shown to be promising therapeutic approaches for Alzheimer's disease, schizophrenia, FXS as well as other forms of ASD.

BabyNet＋＋: Fetal birth weight prediction using biometry multimodal data acquired less than 24 hours before delivery.

Accurate prediction of fetal weight at birth is essential for effective perinatal care, particularly in the context of antenatal management, which involves determining the timing and mode of delivery. The current standard of care involves performing a prenatal ultrasound 24 hours prior to delivery. However, this task presents challenges as it requires acquiring high-quality images, which becomes difficult during advanced pregnancy due to the lack of amniotic fluid. In this paper, we present a novel method that automatically predicts fetal birth weight by using fetal ultrasound video scans and clinical data. Our proposed method is based on a Transformer-based approach that combines a Residual Transformer Module with a Dynamic Affine Feature Map Transform. This method leverages tabular clinical data to evaluate 2D+t spatio-temporal features in fetal ultrasound video scans. Development and evaluation were carried out on a clinical set comprising 582 2D fetal ultrasound videos and clinical records of pregnancies from 194 patients performed less than 24 hours before delivery. Our results show that our method outperforms several state-of-the-art automatic methods and estimates fetal birth weight with an accuracy comparable to human experts. Hence, automatic measurements obtained by our method can reduce the risk of errors inherent in manual measurements. Observer studies suggest that our approach may be used as an aid for less experienced clinicians to predict fetal birth weight before delivery, optimizing perinatal care regardless of the available expertise.

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

BACKGROUND: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraobserver variability because of factors, such as the experience of the operator, image quality, maternal characteristics, or fetal movements. Our study tested the hypothesis that a deep learning method can estimate fetal weight based on a video scan of the fetal abdomen and gestational age with similar performance to the full biometry-based estimations provided by clinical experts. OBJECTIVE: This study aimed to develop and test a deep learning method to automatically estimate fetal weight from fetal abdominal ultrasound video scans. STUDY DESIGN: A dataset of 900 routine fetal ultrasound examinations was used. Among those examinations, 800 retrospective ultrasound video scans of the fetal abdomen from 700 pregnant women between 15 6/7 and 41 0/7 weeks of gestation were used to train the deep learning model. After the training phase, the model was evaluated on an external prospectively acquired test set of 100 scans from 100 pregnant women between 16 2/7 and 38 0/7 weeks of gestation. The deep learning model was trained to directly estimate fetal weight from ultrasound video scans of the fetal abdomen. The deep learning estimations were compared with manual measurements on the test set made by 6 human readers with varying levels of expertise. Human readers used standard 3 measurements made on the standard planes of the head, abdomen, and femur and heuristic formula to estimate fetal weight. The Bland-Altman analysis, mean absolute percentage error, and intraclass correlation coefficient were used to evaluate the performance and robustness of the deep learning method and were compared with human readers. RESULTS: Bland-Altman analysis did not show systematic deviations between readers and deep learning. The mean and standard deviation of the mean absolute percentage error between 6 human readers and the deep learning approach was 3.75%±2.00%. Excluding junior readers (residents), the mean absolute percentage error between 4 experts and the deep learning approach was 2.59%±1.11%. The intraclass correlation coefficients reflected excellent reliability and varied between 0.9761 and 0.9865. CONCLUSION: This study reports the use of deep learning to estimate fetal weight using only ultrasound video of the fetal abdomen from fetal biometry scans. Our experiments demonstrated similar performance of human measurements and deep learning on prospectively acquired test data. Deep learning is a promising approach to directly estimate fetal weight using ultrasound video scans of the fetal abdomen.

Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios.

Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND). Deep learning models can be trained to segment the retinal layers for biomarker extraction. However, the onset of ND can have an impact on the neural tissue, which can lead to the degraded performance of models not exposed to images displaying signs of disease during training. We present a fully automatic approach for the retinal layer segmentation in multiple neurodegenerative disorder scenarios, using an annotated dataset of patients of the most prevalent NDs: Alzheimer's disease, Parkinson's disease, multiple sclerosis and essential tremor, along with healthy control patients. Furthermore, we present a two-part, comprehensive study on the effects of ND on the performance of these models. The results show that images of healthy patients may not be sufficient for the robust training of automated segmentation models intended for the analysis of ND patients, and that using images representative of different NDs can increase the model performance. These results indicate that the presence or absence of patients of ND in datasets should be taken into account when training deep learning models for retinal layer segmentation, and that the proposed approach can provide a valuable tool for the robust and reliable diagnosis in multiple scenarios of ND.

Thermal optimality and physiological parameters inferred from experimental studies scale latitudinally with marine species occurrences.

Ocean warming is expected to occur due to anthropogenic climate change bringing a spatial shift of marine communities. Experimental data that characterize the aerobic power budget via an aerobic scope, thermal metabolic scope, or thermal preferences have been proposed as tools that can describe species distribution since they characterize species fitness or performance under different temperatures. This study tested the potential relationship between observed occurrences and different physiological studies in the Americas for 11 commercially important species in Mexico. Projections were also developed for Mexico's exclusive economic zone under different climate warming scenarios. The physiological data were fitted from optimum up to pejus temperatures and projected to sea surface temperatures for present (2003-2014) and Representative Concentration Pathway (RCP) scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) for the period 2040-2050 and 2090-2100. For species with wide distributions in the Americas, the number of occurrences reported decreases at higher latitudes related to the decrease in species performance calculated from laboratory experiments. In addition, higher species occurrences are usually reported around optimum temperatures. Overall, the results suggest that pejus temperatures likely restrict latitudinal distribution, at least for widely distributed taxons. Regarding Mexican projections, the results varied widely by species. For example, in the Atlantic Ocean, Octopus maya and Panulirus argus are vulnerable to warming scenarios, while Centropomus undecimalis is not. Interestingly, northern Campeche Bank, the Gulf of California, and Western Baja California may act as thermal refugia for marine species indicating they could be assigned as protected areas to support fisheries throughout the Mexican exclusive economic zone. This research adds to the increasing evidence of the relationship between thermal niche and wild population distribution.