Liposomal formulations for enhanced lymphatic drug delivery

AbstractThe lymphatic system that extends throughout the whole body is one of useful targets for efficient drug delivery. The intestinal lymphatic drug delivery has been actively studied to date because administered drugs can avoid the first-pass metabolism in the liver, resulting in improvement of oral bioavailability. Drugs must be hydrophobic in order to be transported into the intestinal lymphatics because the lipid absorption mechanism in the intestine is involved in the lymphatic delivery. Therefore, various lipid-based drug carrier systems have been recently utilized to increase the transport of drug into the intestinal lymphatics. Lipidic molecules of the lipid-based drug delivery systems stimulate production of chylomicrons in the enterocytes, resulting in an increase in drug transport into lymphatic in the enterocytes. This review summarizes recently reported information on development of liposomal carriers for the intestinal lymphatic delivery and covers important determinants for successful lymphatic delivery

Grafting: Complementing RNS in CKKS

The RNS variant of the CKKS scheme (SAC 2018) is widely implemented due to its computational efficiency. However, the current optimized implementations of the RNS-CKKS scheme have a limitation when choosing the ciphertext modulus. It requires the scale factors to be approximately equal to a factor (or a product of factors) of the ciphertext modulus. This restriction causes inefficiency when the scale factor is not close to the power of the machine\u27s word size, wasting the machine\u27s computation budget. In this paper, we solve this implementation-side issue algorithmically by introducing \emph{Grafting}, a ciphertext modulus management system. In Grafting, we mitigate the link between the ciphertext modulus and the application-dependent scale factor. We efficiently enable rescaling by an arbitrary amount of bits by suggesting a method managing the ciphertext modulus with mostly word-sized factors. Thus, we can fully utilize the machine architecture with word-sized factors of the ciphertext modulus while keeping the application-dependent scale factors. This also leads to hardware-friendly RNS-CKKS implementation as a side effect. Furthermore, we apply our technique to Tuple-CKKS multiplication (CCS 2023), solving a restriction due to small scale factors. Our proof-of-concept implementation shows that the overall complexity of RNS-CKKS is almost proportional to the number of coprime factors comprising the ciphertext modulus, of size smaller than the machine\u27s word size. This results in a substantial speed-up from Grafting: $17$-$51$% faster homomorphic multiplications and $43$% faster CoeffsToSlots in bootstrapping, implemented based on the HEaaN library. We estimate that the computational gain could range up to $1.71\times$ speed-up for the current parameters used in the RNS-CKKS libraries

Clinical Application of Next-Generation Sequencing-Based Panel to BRAF Wild-Type Advanced Melanoma Identifies Key Oncogenic Alterations and Therapeutic Strategies

Molecular profiling with next-generation sequencing (NGS) has been applied in multiple solid cancers to discover potential therapeutic targets. Here, we describe the results of a clinical NGS panel in patients with advanced melanoma. Thirty-six tumor tissues from patients with BRAF wild-type melanoma at Seoul National University Hospital (SNUH; Seoul, Republic of Korea) were collected and deep-sequenced using the SNUH FIRST-Cancer NGS panel to assess single-nucleotide variants, small insertions/deletions, copy number variations, and structural variations to estimate tumor mutation burden (TMB). We discovered 106 oncogenic alterations and most of the patients (n = 33, 92%) harbored at least one oncogenic alteration, including 2 patients who were initially diagnosed as BRAF V600E-negative but were later confirmed to be positive. Altogether, 36 samples were classified into RAS/BRAF/NF1-mutant (n = 14, 39%) or triple wild-type (n = 22, 61%) melanoma subtypes. The estimated median TMB was 8.2 mutations per Mb, ranging from 0 to 146.67 mutations per Mb. Of the 36 patients, 25 (70%) had actionable alterations with currently developed drugs, and 7 (19.4%) were enrolled in dinical trials with an RAF inhibitor, multiple receptor tyrosine kinase inhibitor, and anti-programmed cell death-1 (PD-1) antibody. TMB tended to associate with progression-free survival (PFS) of treatment with anti-PD-1/PDL-1 antibody (HR, 0.96; 95% confidence interval, 0.92-1.00; P = 0.07). High-TMB (>= 13) group was associated with longer PFS than the low-TMB group (median 34.0 vs. 11.0 weeks, P = 0.04). Overall, the dinical use of a NGS panel in patients with advanced melanoma shows association with clinical outcomes and several therapeutic strategies.

Arithmetic PCA for Encrypted Data

Reducing the size of large dimensional data is a critical task in machine learning (ML) that often involves using principal component analysis (PCA). In privacy-preserving ML, data confidentiality is of utmost importance, and reducing data size is a crucial way to cut overall costs. This work focuses on minimizing the number of normalization processes in the PCA algorithm, which is a costly procedure in encrypted PCA. By modifying Krasulina\u27s algorithm, non-polynomial operations were eliminated, except for a single delayed normalization at the end. Our PCA algorithm demonstrated similar performance to conventional PCA algorithms in face recognition applications. We also implemented it using the CKKS (Cheon-Kim-Kim-Song) homomorphic encryption scheme and obtained the first 6 principal components of a 128$\times$128 real matrix in 7.85 minutes using 8 threads