In-vitro engineering of high modulus cartilage-like constructs.

To date, the outcomes of cartilage repair have been inconsistent and have frequently yielded mechanically inferior fibro-cartilage, thereby increasing the chances of damage recurrence. Implantation of constructs with biochemical composition and mechanical properties comparable to natural cartilage could be advantageous for long term repair. This study attempted to create such constructs, in-vitro, using tissue engineering principles. Bovine synoviocytes were seeded on non-woven polyethylene terephthalate fibre scaffolds and cultured in chondrogenic medium for 4 weeks, after which uniaxial compressive loading was applied using an in-house bioreactor for 1 hour per day, at a frequency of 1 Hz, for a further 84 days. The initial loading conditions, determined from the mechanical properties of the immature constructs after 4 weeks in chondrogenic culture, were strains ranging between 13 and 23 %. After 56 days (sustained at 84 days) of loading, the constructs were stained homogenously with Alcian blue and for type-II collagen. Dynamic compressive moduli were comparable to the high end values for native cartilage and proportional to Alcian blue staining intensity. We suggest that these high moduli values were attributable to the bioreactor setup, which caused the loading regime to change as the constructs developed i.e. the applied stress and strain increased with construct thickness and stiffness, providing continued sufficient cell stimulation as further matrix was deposited. Constructs containing cartilage-like matrix with response to load similar to that of native cartilage could produce long-term effective cartilage repair when implanted

Clinical-grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning

Background and Aims: Microsatellite instability (MSI) and mismatch-repair deficiency (dMMR) in colorectal tumors are used to select treatment for patients. Deep learning can detect MSI and dMMR in tumor samples on routine histology slides faster and cheaper than molecular assays. But clinical application of this technology requires high performance and multisite validation, which have not yet been performed. Methods: We collected hematoxylin and eosin-stained slides, and findings from molecular analyses for MSI and dMMR, from 8836 colorectal tumors (of all stages) included in the MSIDETECT consortium study, from Germany, the Netherlands, the United Kingdom, and the United States. Specimens with dMMR were identified by immunohistochemistry analyses of tissue microarrays for loss of MLH1, MSH2, MSH6, and/or PMS2. Specimens with MSI were identified by genetic analyses. We trained a deep-learning detector to identify samples with MSI from these slides; performance was assessed by cross-validation (n=6406 specimens) and validated in an external cohort (n=771 specimens). Prespecified endpoints were area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC). Results: The deep-learning detector identified specimens with dMMR or MSI with a mean AUROC curve of 0.92 (lower bound 0.91, upper bound 0.93) and an AUPRC of 0.63 (range, 0.59–0.65), or 67% specificity and 95% sensitivity, in the cross-validation development cohort. In the validation cohort, the classifier identified samples with dMMR with an AUROC curve of 0.95 (range, 0.92–0.96) without image-preprocessing and an AUROC curve of 0.96 (range, 0.93–0.98) after color normalization. Conclusions: We developed a deep-learning system that detects colorectal cancer specimens with dMMR or MSI using hematoxylin and eosin-stained slides; it detected tissues with dMMR with an AUROC of 0.96 in a large, international validation cohort. This system might be used for high-throughput, low-cost evaluation of colorectal tissue specimens

Feature-Constrained Homomorphic Signal Encoding for Ransomware Detection in Encrypted Environments

Detecting ransomware within encrypted environments requires innovative methodologies that balance accuracy with data confidentiality. The Feature-Constrained Homomorphic Signal Encoding (FCHSE) method introduces a privacy-preserving detection framework that operates on encrypted data without requiring decryption, addressing challenges posed by traditional approaches that rely on access to plaintext content. Empirical evaluation demonstrated that the method maintains competitive detection accuracy across multiple ransomware families while preserving cryptographic security guarantees. Comparative assessments revealed that FCHSE achieved higher detection rates than signature-based and heuristic-based models, particularly in identifying emerging ransomware variants that exhibit obfuscation techniques. Computational efficiency analysis highlighted the increased processing time due to homomorphic encryption, underscoring the trade-off between privacy preservation and real-time detection capabilities. Feature selection experiments indicated that entropy distribution, file access behavior, and execution patterns collectively contributed to improving ransomware identification. Large-scale testing demonstrated that detection performance exhibited a slight decline as dataset volume increased, highlighting the necessity for computational optimizations. The resilience of the method against false positives and false negatives suggested a promising balance between sensitivity and specificity, though variations in detection reliability across different ransomware families remained evident. Findings indicate that homomorphic encryption offers a practical avenue for enabling ransomware detection without compromising encrypted data integrity, contributing to the development of privacy-aware cybersecurity strategies

Feature-Constrained Homomorphic Signal Encoding for Ransomware Detection in Encrypted Environments

Detecting ransomware within encrypted environments requires innovative methodologies that balance accuracy with data confidentiality. The Feature-Constrained Homomorphic Signal Encoding (FCHSE) method introduces a privacy-preserving detection framework that operates on encrypted data without requiring decryption, addressing challenges posed by traditional approaches that rely on access to plaintext content. Empirical evaluation demonstrated that the method maintains competitive detection accuracy across multiple ransomware families while preserving cryptographic security guarantees. Comparative assessments revealed that FCHSE achieved higher detection rates than signature-based and heuristic-based models, particularly in identifying emerging ransomware variants that exhibit obfuscation techniques. Computational efficiency analysis highlighted the increased processing time due to homomorphic encryption, underscoring the trade-off between privacy preservation and real-time detection capabilities. Feature selection experiments indicated that entropy distribution, file access behavior, and execution patterns collectively contributed to improving ransomware identification. Large-scale testing demonstrated that detection performance exhibited a slight decline as dataset volume increased, highlighting the necessity for computational optimizations. The resilience of the method against false positives and false negatives suggested a promising balance between sensitivity and specificity, though variations in detection reliability across different ransomware families remained evident. Findings indicate that homomorphic encryption offers a practical avenue for enabling ransomware detection without compromising encrypted data integrity, contributing to the development of privacy-aware cybersecurity strategies