AAV2 vector optimization for retinal ganglion cell-targeted delivery of therapeutic genes.

Recombinant adeno-associated virus (AAV)-2 has significant potential as a delivery vehicle of therapeutic genes to retinal ganglion cells (RGCs), which are key interventional targets in optic neuropathies. Here we show that when injected intravitreally, AAV2 engineered with a reporter gene driven by cytomegalovirus (CMV) enhancer and chicken ß-actin (CBA) promoters, displays ubiquitous and high RGC expression, similar to its synthetic derivative AAV8BP2. A novel AAV2 vector combining the promoter of the human RGC-selective γ-synuclein (hSNCG) gene and woodchuck hepatitis post-transcriptional regulatory element (WPRE) inserted upstream and downstream of a reporter gene, respectively, induces widespread transduction and strong transgene expression in RGCs. High transduction efficiency and selectivity to RGCs is further achieved by incorporating in the vector backbone a leading CMV enhancer and an SV40 intron at the 5' and 3' ends, respectively, of the reporter gene. As a delivery vehicle of hSIRT1, a 2.2-kb therapeutic gene with anti-apoptotic, anti-inflammatory and anti-oxidative stress properties, this recombinant vector displayed improved transduction efficiency, a strong, widespread and selective RGC expression of hSIRT1, and increased RGC survival following optic nerve crush. Thus, AAV2 vector carrying hSNCG promoter with additional regulatory sequences may offer strong potential for enhanced effects of candidate gene therapies targeting RGCs.

"These Places are easy to get into but Impossible to get out of": Women's Pathways to Psychiatric Institutions and Barriers to Community Reentry in India.

In India, where institutional-based mental health care is common, gender and other intersecting marginalized identities along with absent familial support contribute to women's admission and prolonged confinement to psychiatric institutions. However, an intersectional analysis of factors that prevent women with limited familial support from returning to their communities is lacking. This article is based on narratives of eleven women residing at a halfway home in an urban city in India, awaiting return to their communities. We include descriptions and an intersectional analysis of women's pathways to psychiatric institutions, their experiences receiving institutional-based mental health care, and the challenges they face as they contemplate returning to their communities. This study adds to the minimal research examining women's gendered pathways to psychiatric institutions in India. Women's narratives highlight that gender and illness-related disadvantages coupled with economic adversity that led to the initial admission also serve as deterrents to reentering the community.

Retrospective validation of parkland grading scale in a Latin-American high-volume center.

BACKGROUND: Increased complication rates following laparoscopic cholecystectomies have been described, likely related to surgical difficulty, anatomical variations, and gallbladder inflammation severity. Parkland Grading Scale (PGS) stratifies the severity of intraoperative findings to predict operative difficulty and complications. This study aims to validate PGS as a postoperative-outcome predictive tool, comparing its performance with Tokyo Guidelines Grading System (TGGS). METHODS: This is a single-center retrospective cohort study where PGS and TGGS performances were evaluated regarding intraoperative and postoperative outcomes. Both univariate and bivariate analyses were performed on each severity grading scale using STATA-SE 16.0 software. Additionally, we proposed a Logistic Regression Model for each scale. Their association with outcomes was compared between both scales by their Receiver Operating Characteristic Curve. RESULTS: 400 Patients were included. Grade 1 predominance was observed for both PGS and TGGS (47.36% and 25.3%, respectively). A positive association was observed between higher PGS grades and inpatient postoperative care, length of stay, ICU care, and antibiotic requirement. Based on the area under the ROC curve, better performance was observed for PGS over TGGS in the evaluated outcomes. CONCLUSION: PGS performed better than TGGS as a predictive tool for inpatient postoperative care, length of stay, ICU, and antibiotic requirement, especially in severe cases.

Emerging Gene Therapy Technologies for Retinal Ganglion Cell Neuroprotection.

ABSTRACT: Optic neuropathies encompass a breadth of diseases that ultimately result in dysfunction and/or loss of retinal ganglion cells (RGCs). Although visual impairment from optic neuropathies is common, there is a lack of effective clinical treatments. Addressing a critical need for novel interventions, preclinical studies have been generating a growing body of evidence that identify promising new drug-based and cell-based therapies. Gene therapy is another emerging therapeutic field that offers the potential of specifically and robustly increasing long-term RGC survival in optic neuropathies. Gene therapy offers additional benefits of driving improvements following a single treatment administration, and it can be designed to target a variety of pathways that may be involved in individual optic neuropathies or across multiple etiologies. This review explores the history of gene therapy, the fundamentals of its application, and the emerging development of gene therapy technology as it relates to treatment of optic neuropathies.

Two functionally distinct E2/E3 pairs coordinate sequential ubiquitination of a common substrate in Caenorhabditis elegans development.

Ubiquitination, the crucial posttranslational modification that regulates the eukaryotic proteome, is carried out by a trio of enzymes, known as E1 [ubiquitin (Ub)-activating enzyme], E2 (Ub-conjugating enzyme), and E3 (Ub ligase). Although most E2s can work with any of the three mechanistically distinct classes of E3s, the E2 UBCH7 is unable to function with really interesting new gene (RING)-type E3s, thereby restricting it to homologous to E6AP C-terminus (HECT) and RING-in-between-RING (RBR) E3s. The Caenorhabditis elegans UBCH7 homolog, UBC-18, plays a critical role in developmental processes through its cooperation with the RBR E3 ARI-1 (HHARI in humans). We discovered that another E2, ubc-3, interacts genetically with ubc-18 in an unbiased genome-wide RNAi screen in C. elegans These two E2s have nonoverlapping biochemical activities, and each is dedicated to distinct classes of E3s. UBC-3 is the ortholog of CDC34 that functions specifically with Cullin-RING E3 ligases, such as SCF (Skp1-Cullin-F-box). Our genetic and biochemical studies show that UBCH7 (UBC-18) and the RBR E3 HHARI (ARI-1) coordinate with CDC34 (UBC-3) and an SCF E3 complex to ubiquitinate a common substrate, a SKP1-related protein. We show that UBCH7/HHARI primes the substrate with a single Ub in the presence of CUL-1, and that CDC34 is required to build chains onto the Ub-primed substrate. Our study reveals that the association and coordination of two distinct E2/E3 pairs play essential roles in a developmental pathway and suggests that cooperative action among E3s is a conserved feature from worms to humans.

Women's Experiences and Perceptions of Depression in India: A Metaethnography.

In India, social determinants of health, including poverty, domestic violence, and inadequate social support disproportionately affect women, leaving them more vulnerable to depression than men. We conducted a metaethnography to synthesize qualitative data from 13 studies (1987-2017) that explored women's experiences and perceptions of depression in India. We used a feminist standpoint to critically examine how gender shapes these experiences and perceptions. Indian women's experiences of depression were embedded in their social worlds. Women perceived interpersonal conflict, caregiving burden, domestic violence, financial insecurity, adverse reproductive events and widowhood as causes of depression. Women used cultural expressions to describe physical, emotional, and cognitive distress. The detrimental impact of discriminatory social conditions, gender inequalities, and traditional gender roles on Indian women's mental health highlights the need for gender-sensitive mental health research and practice that can attend to women's sociocultural context and promote values of gender equality and social justice.

High Life Satisfaction: Exploring the Role of Health, Social Integration and Perceived Safety among Mexican Midlife and Older Adults.

We sought to investigate the relationship of high life satisfaction with important physical health, mental health, social integration and perceived safety factors among midlife and older Mexican adults. We examined 2,200 midlife and older adults (aged 50-101 years) from the Mexican arm of the Study on global AGEing and adult health (SAGE) and used binary logistic regression models to identify key factors associated with high LSA. Our final logistic regression model revealed self-rated health, affect, interpersonal activities and perceived safety on street to be significantly associated with high life satisfaction. Results from this study add to the nascent literature on subjective well-being of midlife and older Mexicans. Although social work with older adults is not well established in Mexico, researchers and practitioners should collaborate on the development and implementation of social worker-led strategies for prevention and intervention to enhance well-being among midlife and older Mexicans.

Descriptions of depression and depression treatment in older Hispanic immigrants in a geriatric collaborative care program.

OBJECTIVES: To explore experiences with depression and depression treatment among older Hispanic immigrants participating in a collaborative care program of psychotherapy and antidepressant medication. METHOD: Semi-structured, in-depth interviews were conducted with 14 older Spanish-speaking Hispanic immigrants with major depression who participated in a collaborative care program within a public sector specialty geriatric clinic in Los Angeles, CA. RESULTS: Findings revealed that participants used various idioms to describe their experiences with depression, and that depression had a strong impact on functioning. Other findings indicated that depression was caused by various psychosocial problems, antidepressants were helpful in reducing depression, and that bilingual psychotherapists provided a welcoming and safe environment to express emotions and find solutions to problems. CONCLUSION: Results revealed participants' experiences with depression and the impact of participating in a collaborative care intervention for depression. Findings from this project should be used to inform future geriatric interventions for older Hispanic immigrants in the USA.

Extending QGroundControl for Automated Mission Planning of UAVs.

Unmanned Aerial Vehicles (UAVs) have become very popular in the last decade due to some advantages such as strong terrain adaptation, low cost, zero casualties, and so on. One of the most interesting advances in this field is the automation of mission planning (task allocation) and real-time replanning, which are highly useful to increase the autonomy of the vehicle and reduce the operator workload. These automated mission planning and replanning systems require a Human Computer Interface (HCI) that facilitates the visualization and selection of plans that will be executed by the vehicles. In addition, most missions should be assessed before their real-life execution. This paper extends QGroundControl, an open-source simulation environment for flight control of multiple vehicles, by adding a mission designer that permits the operator to build complex missions with tasks and other scenario items; an interface for automated mission planning and replanning, which works as a test bed for different algorithms, and a Decision Support System (DSS) that helps the operator in the selection of the plan. In this work, a complete guide of these systems and some practical use cases are provided.

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems.

Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.

Bridges to Better Health and Wellness: An Adapted Health Care Manager Intervention for Hispanics with Serious Mental Illness.

This study examined the feasibility, acceptability, and initial impact of bridges to better health and wellness (B2BHW), a culturally-adapted health care manager intervention for Hispanics with serious mental illness (SMI). Thirty-four Hispanics with SMI and at risk for cardiovascular disease were enrolled. Mixed-linear models were used to examine changes over 12-months on patient activation, self-efficacy, patient-rated quality of care, receipt of preventive primary care services, and quality of life. The majority of participants completed the intervention (85%) with high satisfaction. Significant improvements were found for patient activation, self-efficacy, patients' ratings of quality of care, and receipt of preventive primary care.

Social big data: Recent achievements and new challenges.

Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.

Maximizing throughput in NOMA-enable industrial IoT networks using digital twin and reinforcement learning.

INTRODUCTION: Increased deployment of heterogeneous and complex Industrial Internet of Things (IIoT) applications such as predictive maintenance and asset tracking places a substantial strain on the limited computational and communication resources. To cater to the rigorous demands of these applications, it is imperative to devise an adaptive online resource allocation method to enhance the efficiency of the current network operations. Multiaccess edge computing (MEC) and digital twins (DTs) are promising solutions that facilitate the realization of edge intelligence and find applications in various industrial applications. Yet, little is known about the advantage the two technologies offer to IIoT networks. OBJECTIVE: This study presents a joint optimization of offloading and resource allocation approach where MEC-server DT is created at the edge, and nonorthogonal multiple access (NOMA) communication is considered between IIoT devices and the industrial gateways (IGWs) for spectral efficiency. Our proposed framework is tailored to reduce mean task completion latency and enhance overall IIoT network throughput. METHOD: To achieve our objective, we jointly optimize the computation resource allocation (RA), subchannel assignment (SA), and offloading decisions (OD). Given the inherent complexity of the problem, we further divide it into RA and SA/OD sub-problems. Employing Deep Reinforcement Learning (DRL), we have formulated a solution delineating the most efficient RA strategy and leveraged DT for optimal SA/OD strategies. RESULTS: Simulation results demonstrate the superior efficiency of our framework, realizing up to 92 % of the efficiency of the exhaustive search method while reducing computation and action decision time. CONCLUSION: In light of system dynamics considered for our work, the proposed framework perfomance showcase its robustness and potential application in real-world IIoT networks.

Loneliness and global cognitive functioning in racially and ethnically diverse US midlife and older adults.

Introduction: Few studies have examined the association of loneliness and cognitive functioning in the US. We used two common measures of loneliness and examined their association in a large sample of US Black, Latino, and White adults (ages ≥ 50). Methods: We analyzed Wave 3 of the National Social Life, Health, and Aging Project (N = 2,757). We examined loneliness using one item from the CES-D and the Felt Loneliness Measure (NFLM); cognitive functioning was assessed using the Montreal Cognitive Assessment (MoCA) tool, where higher scores indicated better functioning. We used weighted ordinary least squares regressions to examine the effects of loneliness (CES-D loneliness and NFLM in separate models) on MoCA scores. In exploratory analyses, we examined if these relationships varied by race and ethnicity. We adjusted all models for sociodemographic and other salient factors (e.g., chronic disease, depressive symptoms, living alone). Results: Mean age was 63.49 years, 52% were female, and 9% were Black and 6% Latino persons. Approximately 54% endorsed feeling lonely on at least one measure; 31% (CES-D) and 46% (NFLM). The relationship between loneliness measures was positive and significant, X 2 (1, N = 2,757) = 435.493 p < 0.001. However, only 40% of lonely individuals were identified as lonely on both assessments. CES-D loneliness was inversely (߈ = -0.274, p = 0.032) associated with MoCA scores and this association did not vary by race and ethnicity. Greater NFLM loneliness was positively associated (߈ = 0.445, p < 0.001) with higher MoCA scores for Latino participants only. Discussion: Loneliness appears to be an important predictor of cognitive functioning. However, the association of loneliness and cognitive functioning varied when using the CES-D loneliness item or the NFLM. Future work is needed to understand how loneliness and its clinically relevant dimensions (social, emotional, existential, chronicity) relate to global and individual cognitive domains. Research is needed with racially and ethnically diverse midlife and older adults, particularly to understand our counterintuitive finding for Latino participants. Finally, findings also support the need for research on interventions to prevent cognitive decline targeting loneliness.

Changes In Serum CXCL13 Levels Are Associated With Outcomes of Colorectal Cancer Patients Undergoing First-Line Oxaliplatin-Based Treatment.

Metastatic colorectal cancer (mCRC) currently lacks reliable biomarkers for precision medicine, particularly for chemotherapy-based treatments. This study examines the behavior of 11 CXC chemokines in the blood of 104 mCRC patients undergoing first-line oxaliplatin-based treatment to pinpoint predictive and prognostic markers. Serum samples were collected before treatment, at response evaluation (EVAR), and at disease progression or last follow-up. Chemokines were assessed in all samples using a Luminex® custom panel. CXCL13 levels increased at EVAR in responders, while in non-responders it decreased. Increasing levels of CXCL13 at EVAR, independently correlated with improved progression-free survival (PFS) and overall survival (OS). Nanostring® analysis in primary tumor samples showed CXCL13 gene expression's positive correlation not only with gene profiles related to an immunogenic tumor microenvironment, increased B cells and T cells (mainly CD8+) but also with extended OS. In silico analysis using RNAseq data from liver metastases treated or not with neoadjuvant oxaliplatin-based combinations, and deconvolution analysis using the MCP-counter algorithm, confirmed CXCL13 gene expression's association with increased immune infiltration, improved OS, and Tertiary Lymphoid Structures (TLSs) gene signatures, especially in neoadjuvant-treated patients. CXCL13 analysis in serum from 36 oxaliplatin-treated patients from the METIMMOX study control arm, reported similar findings. In conclusion, the increase of CXCL13 levels in peripheral blood and its association with the formation of TLSs within the metastatic lesions, emerges as a potential biomarker indicative of the therapeutic efficacy in mCRC patients undergoing oxaliplatin-based treatment.

Exploring Dimensionality Reduction Techniques in Multilingual Transformers.

In scientific literature and industry, semantic and context-aware Natural Language Processing-based solutions have been gaining importance in recent years. The possibilities and performance shown by these models when dealing with complex Human Language Understanding tasks are unquestionable, from conversational agents to the fight against disinformation in social networks. In addition, considerable attention is also being paid to developing multilingual models to tackle the language bottleneck. An increase in size has accompanied the growing need to provide more complex models implementing all these features without being conservative in the number of dimensions required. This paper aims to provide a comprehensive account of the impact of a wide variety of dimensional reduction techniques on the performance of different state-of-the-art multilingual siamese transformers, including unsupervised dimensional reduction techniques such as linear and nonlinear feature extraction, feature selection, and manifold techniques. In order to evaluate the effects of these techniques, we considered the multilingual extended version of Semantic Textual Similarity Benchmark (mSTSb) and two different baseline approaches, one using the embeddings from the pre-trained version of five models and another using their fine-tuned STS version. The results evidence that it is possible to achieve an average reduction of 91.58 % ± 2.59 % in the number of dimensions of embeddings from pre-trained models requiring a fitting time 96.68 % ± 0.68 % faster than the fine-tuning process. Besides, we achieve 54.65 % ± 32.20 % dimensionality reduction in embeddings from fine-tuned models. The results of this study will significantly contribute to the understanding of how different tuning approaches affect performance on semantic-aware tasks and how dimensional reduction techniques deal with the high-dimensional embeddings computed for the STS task and their potential for other highly demanding NLP tasks.

Employment of trauma informed principles in the Palabras Fuertes project: Implications for narrative research with older Latinx communities.

In the US, there is a growing number of older Latinx communities. Qualitative approaches such as narrative inquiry may be fruitful endeavors to elucidate their lived experiences. However, older Latinx communities, including sexual minorities, are disproportionately exposed to social, health, and historical challenges that may result in exposure to potentially traumatic events (e.g. discrimination, illness, grief, etc.). The recognition of high rates of exposure to potentially traumatic events among participants has led to the recommended adoption of Trauma Informed (TI) principles for use in non-trauma specific research. At present, there are limited examples and discussions about the implementation of TI principles in qualitative research and our literature review yielded no discussion of the use of TI principles in narrative inquiry or with older Latinx communities. In this manuscript, we advocate for the adoption of TI principles when engaging in narrative inquiry with older Latinx adults. Second, we discuss examples of TI guided practices we employed while conducting the Palabras Fuertes study of life history narratives with older Latino immigrant gay men living in New York City. Finally, based on these experiences, we provide recommendations for incorporating TI into future narrative research with older Latinx communities.

A Mixed Approach for Aggressive Political Discourse Analysis on Twitter.

Political tensions have grown throughout Europe since the beginning of the new century. The consecutive crises led to the rise of different social movements in several countries, in which the political status quo changed. These changes included an increment of the different tensions underlying politics, as has been reported after many other political and economical crises during the twentieth century. This article proposes the study of the political discourse, and its underlying tension, during Madrid's elections (Spain) in May 2021 by using a mixed approach. To demonstrate if an aggressive tone is used during the campaign, a mixed methodology approach is applied: quantitative computational techniques, related to natural language processing, are used to conduct a first general analysis of the information screened; then, these methods are used for detecting specific trends that can be later filtered and analyzed using a qualitative approach (content analysis), which is also conducted to extract insights about the information found. The main outcomes of this study show that the electoral campaign is not as negative as perceived by the citizens and that there was no relationship between the tone of the discourse and its dissemination. The analysis confirms that the most ideologically extreme parties tend to have a more aggressive language than the moderate ones. The content analysis carried out using our methodology showed that Twitter is used as a sentiment thermometer more than as a way of communicating concrete politics.

Analysis of the contribution of sport events to sustainable development: Impacts, support and resident's perception.

The current study intends to evaluate the link between the affects observed by citizens on athletic events, the perception of their contribution to municipalities' sustainable development, and support for their celebration. A total of 2049 inhabitants from the Valencian Community's three provincial capitals (Spain) took part in this study, which used a stratified random sample with proportionate allocation. The causal association model fit well, with RMSEA = 0.044, NNFI = 0.94, CFI = 0.95, and IFI = 0.95. The study's findings demonstrate that residents' perceptions of the positive and negative impacts caused by sporting events in their communities are an antecedent that explains how these events contribute to sustainable urban development. A positive relationship between perceived sustainable development and support for the hosting of sport events in these localities was also found. These results highlight the need to promote sustainable management development of any sporting event to increase local support from the residents.

Testing the performance, adequacy, and applicability of an artificial intelligence model for pediatric pneumonia diagnosis.

BACKGROUND: Community-acquired Pneumonia (CAP) is a common childhood infectious disease. Deep learning models show promise in X-ray interpretation and diagnosis, but their validation should be extended due to limitations in the current validation workflow. To extend the standard validation workflow we propose doing a pilot test with the next characteristics. First, the assumption of perfect ground truth (100% sensitive and specific) is unrealistic, as high intra and inter-observer variability have been reported. To address this, we propose using Bayesian latent class models (BLCA) to estimate accuracy during the pilot. Additionally, assessing only the performance of a model without considering its applicability and acceptance by physicians is insufficient if we hope to integrate AI systems into day-to-day clinical practice. Therefore, we propose employing explainable artificial intelligence (XAI) methods during the pilot test to involve physicians and evaluate how well a Deep Learning model is accepted and how helpful it is for routine decisions as well as analyze its limitations by assessing the etiology. This study aims to apply the proposed pilot to test a deep Convolutional Neural Network (CNN)-based model for identifying consolidation in pediatric chest-X-ray (CXR) images already validated using the standard workflow. METHODS: For the standard validation workflow, a total of 5856 public CXRs and 950 private CXRs were used to train and validate the performance of the CNN model. The performance of the model was estimated assuming a perfect ground truth. For the pilot test proposed in this article, a total of 190 pediatric chest-X-ray (CXRs) images were used to test the CNN model support decision tool (SDT). The performance of the model on the pilot test was estimated using extensions of the two-test Bayesian Latent-Class model (BLCA). The sensitivity, specificity, and accuracy of the model were also assessed. The clinical characteristics of the patients were compared according to the model performance. The adequacy and applicability of the SDT was tested using XAI techniques. The adequacy of the SDT was assessed by asking two senior physicians the agreement rate with the SDT. The applicability was tested by asking three medical residents before and after using the SDT and the agreement between experts was calculated using the kappa index. RESULTS: The CRXs of the pilot test were labeled by the panel of experts into consolidation (124/176, 70.4%) and no-consolidation/other infiltrates (52/176, 29.5%). A total of 31/176 (17.6%) discrepancies were found between the model and the panel of experts with a kappa index of 0.6. The sensitivity and specificity reached a median of 90.9 (95% Credible Interval (CrI), 81.2-99.9) and 77.7 (95% CrI, 63.3-98.1), respectively. The senior physicians reported a high agreement rate (70%) with the system in identifying logical consolidation patterns. The three medical residents reached a higher agreement using SDT than alone with experts (0.66±0.1 vs. 0.75±0.2). CONCLUSIONS: Through the pilot test, we have successfully verified that the deep learning model was underestimated when a perfect ground truth was considered. Furthermore, by conducting adequacy and applicability tests, we can ensure that the model is able to identify logical patterns within the CXRs and that augmenting clinicians with automated preliminary read assistants could accelerate their workflows and enhance accuracy in identifying consolidation in pediatric CXR images.