In this paper, we propose an innovative encryption method based on the sequential labeling of cycle and star digraphs. This approach harnesses the inherent properties of digraph structures in combination with XOR operations to secure message transmission. Initially, we introduce a method to assign sequential labels to the vertices of cycle and star digraphs, ensuring a unique representation of each vertex. The message to be encrypted is converted into its binary equivalent and then segmented into 4-bit blocks. Each block is represented by a vertex in the digraph, followed by the generation of its adjacent vertices. Entropy analysis indicates a high level of randomness in the encrypted data, demonstrating the method's effectiveness and security. To support this, Welch's t-test was used to compare sequential labeling and random key data. A key component of the encryption is the XOR operation performed between vertex labels and a chosen seed value, resulting in an encrypted binary sequence. Decryption is achieved by applying the inverse XOR operation using the same seed. The proposed method offers a simple yet effective approach to encryption and can be extended to various digraph families. Illustrative examples are provided to demonstrate the construction and application of the encryption and decryption process.
Background Scoring of estrogen receptor (ER) and progesterone receptor (PR) expression in breast cancer is critical for identifying patients who would benefit with hormonal therapy. Since manual scoring of immunohistochemistry (IHC) is influenced by pathologist experience, fatigue, inter-observer variability, and subjectivity, artificial intelligence (AI)–based algorithms, trained on large datasets can aid to improve diagnostic accuracy. Methodology This study evaluated an AI-based algorithm for ER and PR IHC scoring in 297 ER and 293 PR cases of invasive breast carcinoma and compared the scores with that of pathologists (two senior and two junior) A pre-trained automated algorithm (Mimansa) identified region of interest and provided the scoreswhich was compared with the consensus score of pathologists-ground truth(GT).Concordance was evaluated using Cohen’s kappa and F1 score. Results For ER IHC, GT scores included 169 strong positive, 31 low positive, and 98 negative cases. Agreement with GT was 99% and 98% for senior pathologists, 97% for the AI algorithm, and 95% and 93% for junior pathologists. The algorithm correctly classified all strong positive cases but showed discordance in 16 low-score cases, with four false negatives and ten false positives. Notably, it identified two true positive cases missed by all pathologists. For PR IHC, agreement rates were 98% and 97% for senior pathologists, 92% for the algorithm, and 93% and 91% for junior pathologists. The algorithm achieved perfect accuracy in strong positive cases but produced 16 false negatives and eight false positives among low-score cases. Cohen’s kappa values were 0.91 (ER) and 0.84 (PR). Conclusion: The AI algorithm demonstrated high concordance with expert consensus, performing comparably to senior pathologists and outperforming junior pathologists in several metrics. It shows promise as a supportive second-reader tool, particularly in low-positive cases where diagnostic errors may significantly impact patient management.
Background Scoring of estrogen receptor (ER) and progesterone receptor (PR) expression in breast cancer is critical for identifying patients who would benefit with hormonal therapy. Since manual scoring of immunohistochemistry (IHC) is influenced by pathologist experience, fatigue, inter-observer variability, and subjectivity, artificial intelligence (AI)–based algorithms, trained on large datasets can aid to improve diagnostic accuracy. Methodology This study evaluated an AI-based algorithm for ER and PR IHC scoring in 297 ER and 293 PR cases of invasive breast carcinoma and compared the scores with that of pathologists (two senior and two junior) A pre-trained automated algorithm (Mimansa) identified region of interest and provided the scoreswhich was compared with the consensus score of pathologists-ground truth(GT).Concordance was evaluated using Cohen’s kappa and F1 score. Results For ER IHC, GT scores included 169 strong positive, 31 low positive, and 98 negative cases. Agreement with GT was 99% and 98% for senior pathologists, 97% for the AI algorithm, and 95% and 93% for junior pathologists. The algorithm correctly classified all strong positive cases but showed discordance in 16 low-score cases, with four false negatives and ten false positives. Notably, it identified two true positive cases missed by all pathologists. For PR IHC, agreement rates were 98% and 97% for senior pathologists, 92% for the algorithm, and 93% and 91% for junior pathologists. The algorithm achieved perfect accuracy in strong positive cases but produced 16 false negatives and eight false positives among low-score cases. Cohen’s kappa values were 0.91 (ER) and 0.84 (PR). Conclusion: The AI algorithm demonstrated high concordance with expert consensus, performing comparably to senior pathologists and outperforming junior pathologists in several metrics. It shows promise as a supportive second-reader tool, particularly in low-positive cases where diagnostic errors may significantly impact patient management.
A public health approach to palliative care has been developed in adult palliative care over several years. Despite the concepts of health and wellbeing, and palliation, dying and death appearing at first to be contradictory, a cogent argument has been made to understand palliative care in the context of promoting public health. However, the application to children’s palliative care has not been articulated in depth. The need for and development of children’s palliative care is well documented globally, with the public health model, and more recently the WHO conceptual model for palliative care development being key to ongoing development and progress in service delivery. Engaging communities to influence care provision is essential and important to ensure provision of appropriate and sustainable care. Positioning children’s palliative care within the public health perspective transforms care and service provision and centres around the child, their childhood and their carers, as part of the community and the wider population. Access to healthcare is vital, of course, but so is access to childhoods which guarantee children’s human rights and access to being a child living a childhood, whether that childhood is long, short or leads to an adulthood. Uncovering differing perspectives on the intersection of public health and children’s palliative care that varied between global regions, led to the development of eight statements. Our collaboration between colleagues in seven countries in different regions has allowed us to set out the context of the children’s palliative public health approach. This reflects a balancing of medical/nursing professionalised care and partnerships, co production and participation of communities. The public health approach to children’s palliative care is radical, it is transformational, and means changing how we do things in order to improve the lives of children with palliative care needs and their families around the world.
Introduction and Objective: Retina provides a window to systemic diseases, including Type 2 Diabetes(T2D). We evaluated an AI model for estimating HbA1c using retinal biomarkers. Methods: We evaluated retinal fundus camera images & HbA1c (measured on same day in lab with HPLC method) from routine evaluations (12 normal; 158 T2D per ADA criteria) at a tertiary center in India. We extracted 226 vascular features (e.g., tortuosity, branching) & pruned correlated features. Linear Discriminant Analysis with 3 fold cross validation identified top 10 features via Wilcoxan Rank-Sum test to distinguish T2D from normal. A Random Forrest Regressor was trained (St,n=85) on these features to estimate HbA1c & evaluated on test set (Sv,n=85). Model performance was tested using Pearson's Correlation Coefficient (PCC) & Bland-Altman analysis (BA) for linearity & agreement with lab-based HbA1c. Results: The venous feature-based model exhibited a strong linear correlation [PCC=0.98 (95%CI:0.67,0.99), R2=0.84 (95%CI:0.61,0.99), mean absolute error=0.8]. BA revealed minimal bias (mean difference=0.09) & acceptable agreement (-0.83 to +1.0), confirming good agreement between estimated and lab-based HbA1c (Fig). Conclusion: This proof-of-principle study shows AI-based HbA1c estimation, using retinal biomarkers on routine fundus images, is possible & correlates well with lab-based HbA1c. S. Kaup: None. S. Sil Kar: None. R. Rajalakshmi: None. R.M. Carrillo-Larco: None. R. Dhamdhere: None. A. Madabhushi: Stock/Shareholder; Picture Health. Consultant; AbbVie Inc, SimbioSys, Aiforia. Stock/Shareholder; Inspirata. Research Support; Bristol-Myers Squibb Company, AstraZeneca. Stock/Shareholder; Elucid Bioimaging. R. Anjana: None. R. Jagannathan: None. M.K. Ali: Advisory Panel; Eli Lilly and Company. V. Mohan: Speaker's Bureau; Novo Nordisk. Advisory Panel; Abbott. Research Support; Servier Laboratories. Speaker's Bureau; USV Private Limited, Sanofi, Medtronic, Eli Lilly and Company. K. Narayan: None. COALESCE program, which is a project funded by Fogarty International Center of the National Institutes of Health under Award Number D43 TW011404
