Paying the dues: black documentary film and the quest for truth

Paying the Dues: Black Documentary Film and the Quest for Truth is an examination of the partially forgotten vibrant history of Black documentary film as a form of art, cultural resistance, and self-expression in the 1960s. This renaissance in Black engagement with documentary in the 1960s, constituted a quest for truth.This dissertation explores how in their quest for truth, the wave of Black documentary renaissance films sought to reveal truth-narratives about pressing issues that confronted Black communities across the nation, bringing to the forefront discussions about Black identity, visuality, diversity, solidarity, gender, and imagination.Cities, Migration and Global Interdependence 1350-200

Interpretable 3D Multi-Modal Residual Convolutional Neural Network for Mild Traumatic Brain Injury Diagnosis

Mild Traumatic Brain Injury (mTBI) is a significant public health challenge due to its high prevalence and potential for long-term health effects. Despite Computed Tomography (CT) being the standard diagnostic tool for mTBI, it often yields normal results in mTBI patients despite symptomatic evidence. This fact underscores the complexity of accurate diagnosis. In this study, we introduce an interpretable 3D Multi-Modal Residual Convolutional Neural Network (MRCNN) for mTBI diagnostic model enhanced with Occlusion Sensitivity Maps (OSM). Our MRCNN model exhibits promising performance in mTBI diagnosis, demonstrating an average accuracy of 82.4%, sensitivity of 82.6%, and specificity of 81.6%, as validated by a five-fold cross-validation process. Notably, in comparison to the CT-based Residual Convolutional Neural Network (RCNN) model, the MRCNN shows an improvement of 4.4% in specificity and 9.0% in accuracy. We show that the OSM offers superior data-driven insights into CT images compared to the Grad-CAM approach. These results highlight the efficacy of the proposed multi-modal model in enhancing the diagnostic precision of mTBI.Comment: Accepted by the Australasian Joint Conference on Artificial Intelligence 2023 (AJCAI 2023). 12 pages and 5 Figure

Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI

Traumatic Brain Injury (TBI) poses a significant global public health challenge, contributing to high morbidity and mortality rates and placing a substantial economic burden on healthcare systems worldwide. The diagnosis of TBI relies on clinical information along with Computed Tomography (CT) scans. Addressing the multifaceted challenges posed by TBI has seen the development of innovative, data-driven approaches, for this complex condition. Particularly noteworthy is the prevalence of mild TBI (mTBI), which constitutes the majority of TBI cases where conventional methods often fall short. As such, we review the state-of-the-art Machine Learning (ML) techniques applied to clinical information and CT scans in TBI, with a particular focus on mTBI. We categorize ML applications based on their data sources, and there is a spectrum of ML techniques used to date. Most of these techniques have primarily focused on diagnosis, with relatively few attempts at predicting the prognosis. This review may serve as a source of inspiration for future research studies aimed at improving the diagnosis of TBI using data-driven approaches and standard diagnostic data.Comment: The manuscript has 34 pages, 3 figures, and 4 table

Enhancing mTBI Diagnosis with Residual Triplet Convolutional Neural Network Using 3D CT

Mild Traumatic Brain Injury (mTBI) is a common and challenging condition to diagnose accurately. Timely and precise diagnosis is essential for effective treatment and improved patient outcomes. Traditional diagnostic methods for mTBI often have limitations in terms of accuracy and sensitivity. In this study, we introduce an innovative approach to enhance mTBI diagnosis using 3D Computed Tomography (CT) images and a metric learning technique trained with triplet loss. To address these challenges, we propose a Residual Triplet Convolutional Neural Network (RTCNN) model to distinguish between mTBI cases and healthy ones by embedding 3D CT scans into a feature space. The triplet loss function maximizes the margin between similar and dissimilar image pairs, optimizing feature representations. This facilitates better context placement of individual cases, aids informed decision-making, and has the potential to improve patient outcomes. Our RTCNN model shows promising performance in mTBI diagnosis, achieving an average accuracy of 94.3%, a sensitivity of 94.1%, and a specificity of 95.2%, as confirmed through a five-fold cross-validation. Importantly, when compared to the conventional Residual Convolutional Neural Network (RCNN) model, the RTCNN exhibits a significant improvement, showcasing a remarkable 22.5% increase in specificity, a notable 16.2% boost in accuracy, and an 11.3% enhancement in sensitivity. Moreover, RTCNN requires lower memory resources, making it not only highly effective but also resource-efficient in minimizing false positives while maximizing its diagnostic accuracy in distinguishing normal CT scans from mTBI cases. The quantitative performance metrics provided and utilization of occlusion sensitivity maps to visually explain the model's decision-making process further enhance the interpretability and transparency of our approach

Advancing the fight against tuberculosis: integrating innovation and public health in diagnosis, treatment, vaccine development, and implementation science

Tuberculosis (TB) remains one of the leading causes of infectious disease mortality worldwide, increasingly complicated by the emergence of drug-resistant strains and limitations in existing diagnostic and therapeutic strategies. Despite decades of global efforts, the disease continues to impose a significant burden, particularly in low- and middle-income countries (LMICs) where health system weaknesses hinder progress. This comprehensive review explores recent advancements in TB diagnostics, antimicrobial resistance (AMR surveillance), treatment strategies, and vaccine development. It critically evaluates cutting-edge technologies including CRISPR-based diagnostics, whole-genome sequencing, and digital adherence tools, alongside therapeutic innovations such as shorter multidrug-resistant TB regimens and host-directed therapies. Special emphasis is placed on the translational gap—highlighting barriers to real-world implementation such as cost, infrastructure, and policy fragmentation. While innovations like the Xpert MTB/RIF Ultra, BPaLM regimen, and next-generation vaccines such as M72/AS01E represent pivotal progress, their deployment remains uneven. Implementation science, cost-effectiveness analyses, and health equity considerations are vital to scaling up these tools. Moreover, the expansion of the TB vaccine pipeline and integration of AI in diagnostics signal a transformative period in TB control. Eliminating TB demands more than biomedical breakthroughs—it requires a unified strategy that aligns innovation with access, equity, and sustainability. By bridging science with implementation, and integrating diagnostics, treatment, and prevention within robust health systems, the global community can accelerate the path toward ending TB

Innovative pH-responsive alginate-coated rosuvastatin-loaded chitosan nanoparticles: a targeted approach to inhibiting HMGB1-activated RAGE/TLR4-NFκB signaling in colonic inflammation in rats

This study developed and optimized an innovative oral pH-dependent drug delivery system utilizing rosuvastatin-loaded chitosan nanoparticles (RSV-CSNPs) coated with sodium alginate (ALG). The goal was to protect RSV-CSNPs from degradation in the acidic gastric environment and facilitate targeted sustained release in the colon to address inflammatory bowel disease. Nanoparticles were initially prepared by ionic gelation. A subsequent ALG coating process was optimized using a 23 factorial design. The optimal ALG-coated formulation demonstrated minimal drug loss (0.88% ± 0.09%), desirable particle size (407.2 ± 1.95 nm), and suitable zeta potential (−27.13 ± 1.36 mV). In vitro release tests highlighted the superiority of ALG-coated RSV-CSNPs, with significantly reduced RSV release in simulated gastric fluid (6.8% ± 1.06% after 2 h) compared to uncoated nanoparticles (38.45% ± 1.79%), affirming the protective effectiveness of the ALG coating. Extended-release studies at colonic pH (6.8) demonstrated sustained RSV release suitable for colon-specific targeting. In vivo assessments in a dextran sodium sulfate (DSS)-induced rat model of colitis revealed that ALG-RSV-CSNPs significantly outperformed both plain RSV and RSV-CSNPs. Treatment notably decreased colonic inflammation, disease severity scores, macroscopic damage, and oxidative stress indicators. Additionally, histopathological analyses showed remarkable restoration of colon tissue integrity, crypt preservation, and mucosal protection in animals treated with ALG-RSV-CSNPs. Mechanistically, ALG-RSV-CSNPs effectively attenuated colitis by significantly inhibiting the HMGB1-triggered RAGE/TLR4-NFκB inflammatory signaling pathway. Treatment resulted in substantial reductions in key inflammatory markers, including HMGB1, RAGE, TLR4 expression, NFκB activity, pro-inflammatory cytokines (TNF-α and IL-6), and apoptosis marker caspase-3. The anti-inflammatory actions were further supported by reduced neutrophil infiltration, lipid peroxidation, and enhanced antioxidant enzyme activities (SOD and GSH levels). The study identified HMGB1, RAGE, TLR4, and NFκB as critical biomarkers predicting disease activity. Correlation analysis highlighted strong positive associations among these markers, underscoring their collective involvement in colitis pathogenesis and emphasizing the multitarget therapeutic efficacy of ALG-RSV-CSNPs. Overall, this study demonstrates that the optimized pH-responsive ALG-coated RSV-CSNPs significantly enhance the therapeutic outcomes of RSV in colonic inflammation through targeted delivery and sustained release. These nanoparticles represent a promising strategy for effectively managing ulcerative colitis and related inflammatory bowel diseases. Future clinical studies are warranted to validate these findings and facilitate translation into human therapeutic applications

A Controversial Orthodoxy: Al-Ghazali’s Revival of the Religious Sciences

As part of NTT JTSR’s series on Key Texts, the present article discusses the magnum opus of the medieval Muslim scholar Abu Hamid al-Ghazali, Iḥyāʾ ʿUlūm al-Dīn (Revival of the Religious Sciences): its genre, the main aspects of the critique it generated and its relevance to contemporary Muslim debates. This work is still celebrated in Muslim traditionalism as a masterpiece on Islamic spiritual sublimity and self-purification, based on scriptural-traditional references and the mystic experience of the author. The Iḥyāʾ inspired many authors with commentaries, annotations, epitomes and explanations. Yet it stirred no less critique among religious scholars and conservative currents as a work indulging religious novelties as well as spreading inauthentic traditions and unorthodox practices among Muslims. The controversy about this work reflects an intra-Islamic antagonism towards the notion of orthodoxy, and what it entails for the Muslim faith and praxis

Islam, Context, Pluralism and Democracy: Classical and Modern Interpretations,

Islam, Context, Pluralism and Democracy aspires to clarify the tensions and congruences between the revelational and the rational, the text and the context, the limits and the horizons of contextualization in Islam, as these emanate from the Islamic interpretative tradition