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Abstract Background Mucosal head and neck squamous cell carcinoma (HNSCC) is often diagnosed at an advanced stage, where the prognosis is poor due to the high rates of recurrence and metastasis. With approximately one million new cases projected in 2024, worldwide mortality of HNSCC is estimated to reach 50% of detected cases the same year. Patients with early-stage tumours showed a 50–60% five-year survival rate in the US. Immune checkpoint inhibitors (ICIs) have shown promising results in prolonging survival in a subset of patients with recurrent or metastatic disease. However, challenges remain, particularly the limited efficacy of PD-1/PD-L1 blockade therapies. PD-L1 protein expression has been shown to be limited in its predictive power for ICI therapies. Emerging evidence shows that intricate characterisation of the tumour microenvironment (TME) is fundamental to understand interacting cells. This study aims to bridge the gap in understanding the tumor microenvironment by identifying distinct spatial patterns of PD-1/PD-L1 interactions and their association with immunotherapy responses in head and neck squamous cell carcinoma (HNSCC). Methods In this study, we sought to apply a more nuanced approach to understanding cellular interactions by mapping PD-1/PD-L1 interactions across whole-slide HNSCC tissue samples collected prior to ICI therapy. We used a combination of spatial proteomics (Akoya Biosciences) and an in situ proximity ligation assay (isPLA, Navinci Diagnostics) to visualise PD-1/PD-L1 interactions across cell types and cellular neighbourhoods within the tumour TME. Results Our findings indicate the existence of isPLA+ PD-1/PD-L1 interactions between macrophages/CD3 T cell-enriched neighbourhoods and tumour cells at the tumour-stroma boundaries in ICI-resistant tumours. The presence of these dense macrophage-tumour layers, which are either absent or dispersed in responders, indicates a barrier that may restrict immune cell infiltration and promote immune escape mechanisms. In contrast, responders had abundant B and T cell aggregates, predominantly around the tumour edges linked to enhanced immune responses to ICI therapy and better clinical outcomes. Conclusion This study highlights the utility of isPLA in detecting distinct tumour-immune interactions within the TME, offering new cellular interaction metrics for stratifying and optimising immunotherapy strategies.

Abstract Anti-disialoganglioside (GD2) antibody therapy has provided clinical benefit to patients with neuroblastoma however efficacy is likely impaired by the immunosuppressive tumor microenvironment. We have previously defined a link between intratumoral copper levels and immune evasion. Here, we report that adjuvant copper chelation potentiates anti-GD2 antibody therapy to confer durable tumor control in immunocompetent models of neuroblastoma. Mechanistic studies reveal copper chelation creates an immune-primed tumor microenvironment through enhanced infiltration and activity of Fc-receptor-bearing cells, specifically neutrophils which are emerging as key effectors of antibody therapy. Moreover, we report copper sequestration by neuroblastoma attenuates neutrophil function which can be successfully reversed using copper chelation to increase pro-inflammatory effector functions. Importantly, we repurpose the clinically approved copper chelating agent Cuprior as a non-toxic, efficacious immunomodulatory strategy. Collectively, our findings provide evidence for the clinical testing of Cuprior as an adjuvant to enhance the activity of anti-GD2 antibody therapy and improve outcomes for patients with neuroblastoma.

Summary: Single-cell spatial analysis of proteins is rapidly becoming increasingly important in revealing biological insights. Here, we present a protocol for automated high-plex multi-slide immunofluorescence staining and imaging of human head and neck cancer formalin-fixed paraffin-embedded (FFPE) sections using PhenoCycler-Fusion 2.0 technology. We describe steps for preparing human head and neck cancer FFPE tissues, staining with a panel of immunophenotyping markers, and Flow Cell assembly. We then detail procedures for setting up for a PhenoCycler-Fusion run, post-run Flow Cell removal, and downstream analyses.For complete details on the use and execution of this protocol, please refer to Jhaveri et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

Abstract Glycosylation is the most common post‐translational modification of proteins and regulates a myriad of fundamental biological processes under normal, and pathological conditions. Altered protein glycosylation is linked to malignant transformation, showing distinct glycopatterns that are associated with cancer initiation and progression by regulating tumor proliferation, invasion, metastasis, and therapeutic resistance. The glycopatterns of small extracellular vesicles (sEVs) released by cancer cells are promising candidates for cancer monitoring since they exhibit glycopatterns similar to their cell‐of‐origin. However, the clinical application of sEV glycans is challenging due to the limitations of current analytical technologies in tracking the trace amounts of sEVs specifically derived from tumors in circulation. Herein, a sEV GLYcan PHenotype (EV‐GLYPH) assay that utilizes a microfluidic platform integrated with surface‐enhanced Raman scattering for multiplex profiling of sEV glycans in non‐small cell lung cancer is clinically validated. For the first time, the EV‐GLYPH assay effectively identifies distinct sEV glycan signatures between non‐transformed and malignantly transformed lung cells. In a clinical study evaluated on 40 patients, the EV‐GLYPH assay successfully differentiates patients with early‐stage malignant lung nodules from benign lung nodules. These results reveal the potential to profile sEV glycans for noninvasive diagnostics and prognostics, opening up promising avenues for clinical applications and understanding the role of sEV glycosylation in lung cancer.

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model’s decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology.

Abstract COVID-19 continues to affect an unprecedented number of people with the emergence of new variants posing a serious challenge to global health. There is an expansion of knowledge in understanding the pathogenesis of Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the impact of the acute disease on multiple organs. In addition, growing evidence reports that the impact of COVID-19 on different organs persists long after the recovery phase of the disease, leading to long-term consequences of COVID-19. These long-term consequences involve pulmonary as well as extra-pulmonary sequelae of the disease. Noteably, recent research has shown a potential association between COVID-19 and change in the molecular cargo of extracellular vesicles (EVs). EVs are vesicles released by cells and play an important role in cell communication by transfer of bioactive molecules between cells. Emerging evidence shows a strong link between EVs and their molecular cargo, and regulation of metabolism in health and disease. This review focuses on current knowledge about EVs and their potential role in COVID-19 pathogenesis, their current and future implications as tools for biomarker and therapeutic development and their possible effects on long-term impact of COVID-19.

Summary: The utilization of single-cell resolved spatial transcriptomics to delineate immune responses during SARS-CoV-2 infection was able to identify M1 macrophages to have elevated expression of IFI27 in areas of infection.

Abstract Objectives Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2) virus infection in pregnancy is associated with higher incidence of placental dysfunction, referred to by a few studies as a ‘preeclampsia‐like syndrome’. However, the mechanisms underpinning SARS‐CoV‐2‐induced placental malfunction are still unclear. Here, we investigated whether the transcriptional architecture of the placenta is altered in response to SARS‐CoV‐2 infection. Methods We utilised whole‐transcriptome, digital spatial profiling, to examine gene expression patterns in placental tissues from participants who contracted SARS‐CoV‐2 in the third trimester of their pregnancy (n = 7) and those collected prior to the start of the coronavirus disease 2019 (COVID‐19) pandemic (n = 9). Results Through comprehensive spatial transcriptomic analyses of the trophoblast and villous core stromal cell subpopulations in the placenta, we identified SARS‐CoV‐2 to promote signatures associated with hypoxia and placental dysfunction. Notably, genes associated with vasodilation (NOS3), oxidative stress (GDF15, CRH) and preeclampsia (FLT1, EGFR, KISS1, PAPPA2) were enriched with SARS‐CoV‐2. Pathways related to increased nutrient uptake, vascular tension, hypertension and inflammation were also enriched in SARS‐CoV‐2 samples compared to uninfected controls. Conclusions Our findings demonstrate the utility of spatially resolved transcriptomic analysis in defining the underlying pathogenic mechanisms of SARS‐CoV‐2 in pregnancy, particularly its role in placental dysfunction. Furthermore, this study highlights the significance of digital spatial profiling in mapping the intricate crosstalk between trophoblasts and villous core stromal cells, thus shedding light on pathways associated with placental dysfunction in pregnancies with SARS‐CoV‐2 infection.

Abstract Objectives Globally, non‐small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and the leading cause of cancer‐related deaths. Tumor‐associated circulating cells in NSCLC can have a wide variety of morphological and phenotypic characteristics, including epithelial, immunological or hybrid subtypes. The distinctive characteristics and potential clinical significance of these cells in patients with NSCLC are explored in this study. Methods We utilised a spiral microfluidic device to enrich large cells and cell aggregates from the peripheral blood samples of NSCLC patients. These cells were characterised through high‐resolution immunofluorescent imaging and statistical analysis, correlating findings with clinical information from our patient cohort. Results We have identified varied populations of heterotypic circulating tumor cell clusters with differing immune cell composition that included a distinct class of atypical tumor‐associated macrophages that exhibits unique morphology and cell size. This subtype's prevalence is positively correlated with the tumor stage, progression and metastasis. Conclusions Our study reveals a heterogeneous landscape of circulating tumor cells and their clusters, underscoring the complexity of NSCLC pathobiology. The identification of a unique subtype of atypical tumor‐associatedmacrophages that simultaneously express both tumor and immune markers and whose presence correlates with late disease stages, poor clinical outcomes and metastatic risk infers the potential of these cells as biomarkers for NSCLC staging and prognosis. Future studies should focus on the role of these cells in the tumor microenvironment and their potential as therapeutic targets. Additionally, longitudinal studies tracking these cell types through disease progression could provide further insights into their roles in NSCLC evolution and response to treatment.

Abstract Objectives Non‐small‐cell lung carcinoma (NSCLC) is the most prevalent and lethal form of lung cancer. The need for biomarker‐informed stratification of targeted therapies has underpinned the need to uncover the underlying properties of the tumor microenvironment (TME) through high‐plex quantitative assays. Methods In this study, we profiled resected NSCLC tissues from 102 patients by targeted spatial proteomics of 78 proteins across tumor, immune activation, immune cell typing, immune‐oncology, drug targets, cell death and PI3K/AKT modules to identify the tumor and stromal signatures associated with overall survival (OS). Results Survival analysis revealed that stromal CD56 (HR = 0.384, P = 0.06) and tumoral TIM3 (HR = 0.703, P = 0.05) were associated with better survival in univariate Cox models. In contrast, after adjusting for stage, BCLXL (HR = 2.093, P = 0.02) and cleaved caspase 9 (HR = 1.575, P = 0.1) negatively influenced survival. Delta testing indicated the protective effect of TIM‐3 (HR = 0.614, P = 0.04) on OS. In multivariate analysis, CD56 (HR = 0.172, P = 0.001) was associated with better survival in the stroma, while B7.H3 (HR = 1.72, P = 0.008) was linked to poorer survival in the tumor. Conclusions Deciphering the TME using high‐plex spatially resolved methods is giving us new insights into compartmentalised tumor and stromal protein signatures associated with clinical endpoints in NSCLC.

Background: Axial Spondyloarthritis (AxSpA) is a chronic inflammatory condition which impacts various aspects of patients’ lives, including work productivity. This study aims to identify the impact of AxSpA on key work outcomes including sick leave rate, disease related unemployment rate, work productivity loss and its association with disease activity. Methods: 62 Adult patients aged ¿20 years who met the Assessment in Spondyloarthritis International Society (ASAS) 2009 criteria for AxSpA were enrolled. Work productivity was assessed by the Work Productivity and Activity Impairment scale (WPAI:SpA). The validated Sinhala version of WPAI questionnaire was used to obtain data and calculate absenteeism, presenteeism and work productivity loss (WPL). Higher scores for presenteeism, absenteeism, or WPL indicate greater work impairment due to illness. Disease activity was assessed by Ankylosing Spondylitis Disease Activity Score (ASDAS). Multivariate linear regression was utilized to assess the association between work productivity and disease activity. Results: Among 62 patients who were on paid employment, permanent loss of employment rate due to disease was 20.9% and disease related sick leave rate for [Formula: see text] 2 months was 58.06%. Out of them 48% engaged in heavy labor jobs and 65% had high or very high disease activity. (Figure 1) Among employed patients, mean Absenteeism, Presenteeism and WPL were 22%, 51.04% and 56% respectively. A decrease in work productivity was related to an increase in disease activity. Disease activity was strongly correlated with absenteeism (p [Formula: see text] 0.01), presenteeism (p [Formula: see text] 0.01) and work productivity loss (p [Formula: see text] 0.001). (Figure 2) Conclusion: The impact of AxSpA on work outcomes is substantial and necessitates effective intervention, like controlling disease activity. The significantly elevated levels of presenteeism, absenteeism, and WPL when compared to global data may be linked to suboptimal disease management due to limited availability of biological treatments as well as labor-intensive jobs among the population.

Introduction Sarcoidosis is a complex, multisystem disorder characterized by the formation of non-caseating granulomas in various organs, most commonly the lungs and lymph nodes. Ocular involvement is frequent, occurring in up to 78% of patients, while skin manifestations are seen in approximately 25–35% of patients. Here we present a case of a 13-year-old boy with generalized body pain that later evolved to oculocutaneous sarcoidosis. Case Report A 13-year-old Sri Lankan boy presented in 2021 with severe generalized body pain, bilateral cervical lymph node swelling, occasional fever, and elevated inflammatory markers. Initial investigations for infections, malignancies, and autoimmune diseases were inconclusive. Lymph node biopsy suggested toxoplasmosis, but serological tests were normal. He later developed left eye visual blurring and periorbital swelling, with MRI showing left optic neuritis and orbital soft tissue oedema. Treatment with IV Methylprednisolone and oral prednisolone resolved his symptoms but was lost to follow-up. A year later, he presented with transient self-resolving subcutaneous nodules over trunk and limbs. due to its transient nature. ANA, RF, ACE level, serum Calcium, serum protein electrophoresis and IgG4 levels were negative. He was started on steroids, Methotrexate, and Infliximab assuming the possibility of autoinflammatory syndrome or sarcoidosis. In 2024 he again developed bilateral submandibular and cervical lymphadenopathy with acute right side visual blurring. MRI showed right optic neuritis and lacrimal gland swelling. (Figure 1) Lymph node Biopsy confirmed non-caseating granulomas. (Figure 2). Based on the findings, a diagnosis of oculocutaneous sarcoidosis was made. Treatment with steroids, methotrexate and Adalimumab was initiated, resulting in a remarkable response. Conclusion Sarcoidosis should be considered as a differential diagnosis in cases of optic neuritis with lymphadenopathy and cutaneous manifestations. Diagnosis of oculocutaneous sarcoidosis is challenging and often requires a multidisciplinary approach involving dermatologists, ophthalmologists, and rheumatologists.

Mothers of children with autism spectrum disorder are more likely to have features of the broad autism phenotype and adverse mental health outcomes. The broad autism phenotype can contribute to poorer mental health; however, the mechanism behind this relationship remains uncertain. This study investigated both the predictors of mental health and the relationship between the broad autism phenotype and mental health symptoms in mothers of children with autism spectrum disorder. This cross-sectional survey recruited 232 Australian mothers ([greater than or equal to]18 years) of children with autism spectrum disorder (0-10 years) and assessed maternal broad autism phenotype, mental health, adult attachment style, degree of social support and parenting experience. Hierarchical multiple regressions, t-tests, correlations and mediation analyses were conducted. Psychological inflexibility was the greatest unique predictor of anxious, depressive and stress symptomatology. Challenging parenting experiences predicted depressive, anxious and stress symptomatology while the broad autism phenotype also uniquely predicted anxious symptoms. Both psychological inflexibility and challenging parenting experiences were significant mediators of the relationships between the broad autism phenotype and depressive symptoms and between the broad autism phenotype and anxiety. Psychological inflexibility alone mediated the relationship between the broad autism phenotype and stress. Targeting psychological inflexibility and supporting parenting are promising avenues for future interventions to support the mental health of mothers of young children with autism spectrum disorder.

Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient’s disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems’ operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.

Background: Mucosal head and neck squamous cell carcinoma (HNSCC) is often diagnosed at an advanced stage, where the prognosis is poor due to the high rates of recurrence and metastasis. With approximately one million new cases projected in 2024, worldwide mortality of HNSCC is estimated to reach 50% of detected cases the same year. Patients with early-stage tumours showed a 50–60% five-year survival rate in the US. Immune checkpoint inhibitors (ICIs) have shown promising results in prolonging survival in a subset of patients with recurrent or metastatic disease. However, challenges remain, particularly the limited efficacy of PD-1/PD-L1 blockade therapies. PD-L1 protein expression has been shown to be limited in its predictive power for ICI therapies. Emerging evidence shows that intricate characterisation of the tumour microenvironment (TME) is fundamental to understand interacting cells. This study aims to bridge the gap in understanding the tumor microenvironment by identifying distinct spatial patterns of PD-1/PD-L1 interactions and their association with immunotherapy responses in head and neck squamous cell carcinoma (HNSCC). Methods: In this study, we sought to apply a more nuanced approach to understanding cellular interactions by mapping PD-1/PD-L1 interactions across whole-slide HNSCC tissue samples collected prior to ICI therapy. We used a combination of spatial proteomics (Akoya Biosciences) and an in situ proximity ligation assay (isPLA, Navinci Diagnostics) to visualise PD-1/PD-L1 interactions across cell types and cellular neighbourhoods within the tumour TME. Results: Our findings indicate the existence of isPLA+ PD-1/PD-L1 interactions between macrophages/CD3 T cell-enriched neighbourhoods and tumour cells at the tumour-stroma boundaries in ICI-resistant tumours. The presence of these dense macrophage-tumour layers, which are either absent or dispersed in responders, indicates a barrier that may restrict immune cell infiltration and promote immune escape mechanisms. In contrast, responders had abundant B and T cell aggregates, predominantly around the tumour edges linked to enhanced immune responses to ICI therapy and better clinical outcomes. Conclusion: This study highlights the utility of isPLA in detecting distinct tumour-immune interactions within the TME, offering new cellular interaction metrics for stratifying and optimising immunotherapy strategies. [ABSTRACT FROM AUTHOR]

The discovery of immune checkpoint proteins such as PD-1/PDL-1 and CTLA-4 represents a significant breakthrough in the field of cancer immunotherapy. Therefore, humanized monoclonal antibodies, targeting these immune checkpoint proteins have been utilized successfully in patients with metastatic melanoma, renal cell carcinoma, head and neck cancers and non-small lung cancer. The US FDA has successfully approved three different categories of immune checkpoint inhibitors (ICIs) such as PD-1 inhibitors (Nivolumab, Pembrolizumab, and Cemiplimab), PDL-1 inhibitors (Atezolimumab, Durvalumab and Avelumab), and CTLA-4 inhibitor (Ipilimumab). Unfortunately, not all patients respond favourably to these drugs, highlighting the role of biomarkers such as Tumour mutation burden (TMB), PDL-1 expression, microbiome, hypoxia, interferon-γ, and ECM in predicting responses to ICIs-based immunotherapy. The current study aims to review the literature and updates on ICIs in cancer therapy.

Mucosal head and neck squamous cell carcinoma (HNSCC) are the seventh most common cancer, with approximately 50% of patients living beyond 5 years. Immune checkpoint inhibitors (ICIs) have shown promising results in patients with recurrent or metastatic (R/M) disease, however, only a subset of patients benefit from immunotherapy. Studies have implicated the tumor microenvironment (TME) of HNSCC as a major factor in therapy response, highlighting the need to better understand the TME, particularly by spatially resolved means to determine cellular and molecular components. Here, we employed targeted spatial profiling of proteins on a cohort of pre-treatment tissues from patients with R/M disease to identify novel biomarkers of response within the tumor and stromal margins. By grouping patient outcome categories into response or non-response, based on Response Evaluation Criteria in Solid Tumors (RECIST) we show that immune checkpoint molecules, including PD-L1, B7-H3, and VISTA, were differentially expressed. Patient responders possessed significantly higher tumor expression of PD-L1 and B7-H3, but lower expression of VISTA. Analysis of response subgroups indicated that tumor necrosis factor receptor (TNFR) superfamily members including OX40L, CD27, 4-1BB, CD40, and CD95/Fas, were associated with immunotherapy outcome. CD40 expression was higher in patient-responders than non responders, while CD95/Fas expression was lower in patients with partial response (PR) relative to those with stable disease (SD) and progressive disease (PD). Furthermore, we found that high 4-1BB expression in the tumor compartment, but not in the stroma, was associated with better overall survival (OS) (HR= 0.28, p-adjusted= 0.040). Moreover, high CD40 expression in tumor regions (HR= 0.27, p-adjusted= 0.035), and high CD27 expression in the stroma (HR= 0.2, p-adjusted=0.032) were associated with better survival outcomes. Taken together, this study supports the role of immune checkpoint molecules and implicates the TNFR superfamily as key players in immunotherapy response in our cohort of HNSCC. Validation of these findings in a prospective study is required to determine the robustness of these tissue signatures.

PurposeRobust biomarkers that predict disease outcomes amongst COVID-19 patients are necessary for both patient triage and resource prioritisation. Numerous candidate biomarkers have been proposed for COVID-19. However, at present, there is no consensus on the best diagnostic approach to predict outcomes in infected patients. Moreover, it is not clear whether such tools would apply to other potentially pandemic pathogens and therefore of use as stockpile for future pandemic preparedness.MethodsWe conducted a multi-cohort observational study to investigate the biology and the prognostic role of interferon alpha-inducible protein 27 (IFI27) in COVID-19 patients.ResultsWe show that IFI27 is expressed in the respiratory tract of COVID-19 patients and elevated IFI27 expression in the lower respiratory tract is associated with the presence of a high viral load. We further demonstrate that the systemic host response, as measured by blood IFI27 expression, is associated with COVID-19 infection. For clinical outcome prediction (e.g., respiratory failure), IFI27 expression displays a high sensitivity (0.95) and specificity (0.83), outperforming other known predictors of COVID-19 outcomes. Furthermore, IFI27 is upregulated in the blood of infected patients in response to other respiratory viruses. For example, in the pandemic H1N1/09 influenza virus infection, IFI27-like genes were highly upregulated in the blood samples of severely infected patients.ConclusionThese data suggest that prognostic biomarkers targeting the family of IFI27 genes could potentially supplement conventional diagnostic tools in future virus pandemics, independent of whether such pandemics are caused by a coronavirus, an influenza virus or another as yet-to-be discovered respiratory virus.

BackgroundCoronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Infected individuals display a wide spectrum of disease severity, as defined by the World Health Organization (WHO). One of the main factors underlying this heterogeneity is the host immune response, with severe COVID-19 often associated with a hyperinflammatory state.AimOur current study aimed to pinpoint the specific genes and pathways underlying differences in the disease spectrum and outcomes observed, through in-depth analyses of whole blood transcriptomics in a large cohort of COVID-19 participants.ResultsAll WHO severity levels were well represented and mild and severe disease displaying distinct gene expression profiles. WHO severity levels 1-4 were grouped as mild disease, and signatures from these participants were different from those with WHO severity levels 6-9 classified as severe disease. Severity level 5 (moderate cases) presented a unique transitional gene signature between severity levels 2-4 (mild/moderate) and 6-9 (severe) and hence might represent the turning point for better or worse disease outcome. Gene expression changes are very distinct when comparing mild/moderate or severe cases to healthy controls. In particular, we demonstrated the hallmark down-regulation of adaptive immune response pathways and activation of neutrophil pathways in severe compared to mild/moderate cases, as well as activation of blood coagulation pathways.ConclusionsOur data revealed discrete gene signatures associated with mild, moderate, and severe COVID-19 identifying valuable candidates for future biomarker discovery.

Abstract Risk stratification of COVID‐19 patients is essential for pandemic management. Changes in the cell fitness marker, hFwe‐Lose, can precede the host immune response to infection, potentially making such a biomarker an earlier triage tool. Here, we evaluate whether hFwe‐Lose gene expression can outperform conventional methods in predicting outcomes (e.g., death and hospitalization) in COVID‐19 patients. We performed a post‐mortem examination of infected lung tissue in deceased COVID‐19 patients to determine hFwe‐Lose’s biological role in acute lung injury. We then performed an observational study (n = 283) to evaluate whether hFwe‐Lose expression (in nasopharyngeal samples) could accurately predict hospitalization or death in COVID‐19 patients. In COVID‐19 patients with acute lung injury, hFwe‐Lose is highly expressed in the lower respiratory tract and is co‐localized to areas of cell death. In patients presenting in the early phase of COVID‐19 illness, hFwe‐Lose expression accurately predicts subsequent hospitalization or death with positive predictive values of 87.8–100% and a negative predictive value of 64.1–93.2%. hFwe‐Lose outperforms conventional inflammatory biomarkers and patient age and comorbidities, with an area under the receiver operating characteristic curve (AUROC) 0.93–0.97 in predicting hospitalization/death. Specifically, this is significantly higher than the prognostic value of combining biomarkers (serum ferritin, D‐dimer, C‐reactive protein, and neutrophil–lymphocyte ratio), patient age and comorbidities (AUROC of 0.67–0.92). The cell fitness marker, hFwe‐Lose, accurately predicts outcomes in COVID‐19 patients. This finding demonstrates how tissue fitness pathways dictate the response to infection and disease and their utility in managing the current COVID‐19 pandemic.

Brain tumor detection is crucial for effective treatment planning and improved patient outcomes. However, existing methods often face challenges, such as limited interpretability and class imbalance in medical-imaging data. This study presents a novel, custom Convolutional Neural Network (CNN) architecture, specifically designed to address these issues by incorporating interpretability techniques and strategies to mitigate class imbalance. We trained and evaluated four CNN models (proposed CNN, ResNetV2, DenseNet201, and VGG16) using a brain tumor MRI dataset, with oversampling techniques and class weighting employed during training. Our proposed CNN achieved an accuracy of 94.51%, outperforming other models in regard to precision, recall, and F1-Score. Furthermore, interpretability was enhanced through gradient-based attribution methods and saliency maps, providing valuable insights into the model's decision-making process and fostering collaboration between AI systems and clinicians. This approach contributes a highly accurate and interpretable framework for brain tumor detection, with the potential to significantly enhance diagnostic accuracy and personalized treatment planning in neuro-oncology. [ABSTRACT FROM AUTHOR]

Head and neck cancer (HNC) is the seventh most common cancer globally, resulting in 440 000 deaths per year. While there have been advancements in chemoradiotherapy and surgery, relapse occurs in more than half of HNCs, and these patients have a median survival of 10 months and a 2‐year survival of < 20%. Only a subset of patients displays durable benefits from immunotherapies in metastatic and recurrent HNC, making it critical to understand the tumor microenvironment (TME) underpinning therapy responses in HNC. To recognize biological differences within the TME that may be predictive of immunotherapy response, we applied cutting‐edge geospatial whole‐transcriptome profiling (NanoString GeoMx Digital Spatial Profiler) and spatial proteomics profiling (Akoya PhenoCycler‐Fusion) on a tumor microarray consisting of 25 cores from 12 patients that included 4 immunotherapy‐unresponsive (8 cores) and 2 immunotherapy‐responsive patients (5 cores), as well as 6 immunotherapy naïve patients (12 cores). Through high‐plex, regional‐based transcriptomic mapping of the tumor and TME, pathways involved with the complement system and hypoxia were identified to be differentially expressed in patients who went on to experience a poor immunotherapy response. Single‐cell, targeted proteomic analysis found that immune cell infiltration of the cancer cell mass and interactions of CD8 T cells with tumor and other immune cells were associated with positive immunotherapy response. The relative abundance of specific tumor phenotypes and their interactions with various immune cells was identified to be different between response groups. This study demonstrates how spatial transcriptomics and proteomics can resolve novel alterations in the TME of HNC that may contribute to therapy sensitivity and resistance. [ABSTRACT FROM AUTHOR]

The ability to study cancer-immune cell communication across the whole tumor section without tissue dissociation is needed, especially for cancer immunotherapy development, which requires understanding of molecular mechanisms and discovery of more druggable targets. In this work, we assembled and evaluated an integrated experimental framework and analytical process to enable genome-wide scale discovery of ligand-receptors potentially used for cellular crosstalks, followed by targeted validation. We assessed the complementarity of four different technologies: single-cell RNA sequencing and Spatial transcriptomic (measuring over >20,000 genes), RNA In Situ Hybridization (RNAscope, measuring 4-12 genes) and Opal Polaris multiplex protein staining (4-9 proteins). To utilize the multimodal data, we implemented existing methods and also developed STRISH (Spatial TRanscriptomic In Situ Hybridization), a computational method that can automatically scan across the whole tissue section for local expression of gene (e.g. RNAscope data) and/or protein markers (e.g. Polaris data) to recapitulate an interaction landscape across the whole tissue. We evaluated the approach to discover and validate cell-cell interaction in situ through in-depth analysis of two types of cancer, basal cell carcinoma and squamous cell carcinoma, which account for over 70% of cancer cases. We showed that inference of cell-cell interactions using scRNA-seq data can misdetect or detect false positive interactions. Spatial transcriptomics still suffers from misdetecting lowly expressed ligand-receptor interactions, but reduces false discovery. RNAscope and Polaris are sensitive methods for defining the location of potential ligand receptor interactions, and the STRISH program can determine the probability that local gene co-expression reflects true cell-cell interaction. We expect that the approach described here will be widely applied to discover and validate ligand receptor interaction in different types of solid cancer tumors.

Anthracnose caused by Colletotrichum sp. and stem-end rot (SER) caused predominantly by Lasiodiplodia sp. are common postharvest diseases affecting papaya in Sri Lanka and are currently managed by synthetic chemicals which are hazardous. Use of essential oils (EOs) is considered a non-hazardous alternative. This in vitro study investigated the antifungal potential of selected EOs against anthracnose and SER pathogens isolated from papaya var. ‘Red Lady’.A poisoned food bioassay was carried out to evaluate the antifungal effect of EOs (Cardamom, citronella, orange, mustard and lemon). Colony diameter was taken as a measure of growth. Bioassays consisted of EOs at 500, 750, 1000 μl l-1 with untreated control, three replicates and three separate trials. Cardamom (1000 μl l-1) and citronella (750 μll-1) significantly (PColletotrichum sp. and Lasiodiplodia sp. from papaya. According to GC-MS, higher percentages of antifungal geraniol, geranyl acetate are present in citronella while a-terpinyl acetate, 1, 8-cineole are present in cardamom oil.

Head and neck squamous cell carcinoma (HNSCC) often presents with locoregional or distant disease, despite multimodal therapeutic approaches, which include surgical resection, chemoradiotherapy, and more recently, immunotherapy for metastatic or recurrent HNSCC. Therapies often target the primary and nodal regional HNSCC sites, and their efficacy at controlling occult distant sites remains poor. While our understanding of the tumor microenvironment conducive to effective therapies is increasing, the biology underpinning locoregional sites remains unclear. Here, we applied targeted spatial proteomic approaches to primary and lymph node metastasis from an oropharyngeal SCC (OPSCC) cohort to understand the expression of proteins within tumors, and stromal compartments of the respective sites in samples of both matched and unmatched patients. In unmatched analyses of n = 43 primary and 11 nodal metastases, our data indicated that tumor cells in nodal metastases had higher levels of Ki-67, PARP, BAD, and cleaved caspase 9, suggesting a role for increased proliferation, DNA repair, and apoptosis within these metastatic cells. Conversely, in matched analyses (n = 7), pro-apoptotic markers BIM and BAD were enriched in the stroma of primary tumors. Univariate, overall survival (OS) analysis indicated CD25 in tumor regions of primary tumors to be associated with reduced survival (HR = 3.3, p = 0.003), while progesterone receptor (PR) was associated with an improved OS (HR = 0.33, p = 0.015). This study highlights the utility of spatial proteomics for delineating the tumor and stromal compartment composition, and utility toward understanding these properties in locoregional metastasis. These findings indicate unique biological properties of lymph node metastases that may elucidate further understanding of distant metastatic in OPSCC.