Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study.

PURPOSE: To assess the clinical potential of a set of new diffusion parameters (D, ß, and µ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm2). The FROC model parameters including diffusion coefficient (D), fractional order parameter (ß), a microstructural quantity (µ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the ß and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: Among all the studied diffusion parameters, significant differences were found in D, ß, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher ß values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 µm2/ms vs. 1.03 ± 0.13 µm2/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 µm2/ms vs. 2.09 ± 0.70 µm2/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and ß demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; ß: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs. CONCLUSIONS: The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs.

CRISPR-based assessment of genomic structure in the conserved SQUAMOSA promoter-binding-like gene clusters in rice.

Although SQUAMOSA promoter-binding-like (SPL) transcription factors are important regulators of development in rice (Oryza sativa), prior assessments of the SPL family have been limited to single genes. A functional comparison across the full gene family in standardized genetic backgrounds has not been reported previously. Here, we demonstrate that the SPL gene family in rice is enriched due to the most recent whole genome duplication (WGD). Notably, 10 of 19 rice SPL genes (52%) cluster in four units that have persisted for at least 50 million years. We show that SPL gene grouping and retention following WGD is widespread in angiosperms, suggesting the conservatism and importance of this gene arrangement. We used Cas9 editing to generate transformation lines for all 19 SPL genes in a common set of backgrounds, and found that knockouts of 14 SPL genes exhibited defects in plant height, 10 exhibited defects in panicle size, and nine had altered grain lengths. We observed subfunctionalization of genes in the paleoduplicated pairs, but little evidence of neofunctionalization. Expression of OsSPL3 was negatively correlated with that of its closest neighbor in its synteny group, OsSPL4, and its sister paired gene, OsSPL12, in the opposing group. Nucleotide diversity was lower in eight of the nine singleton genes in domesticated rice, relative to wild rice, whereas the reverse was true for the paired genes. Together, these results provide functional information on eight previously unexamined OsSPL family members and suggest that paleoduplicate pair redundancy benefits plant survival and innovation.

Mapping the Mouse Cell Atlas by Microwell-Seq.

Single-cell RNA sequencing (scRNA-seq) technologies are poised to reshape the current cell-type classification system. However, a transcriptome-based single-cell atlas has not been achieved for complex mammalian systems. Here, we developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices. Using Microwell-seq, we analyzed more than 400,000 single cells covering all of the major mouse organs and constructed a basic scheme for a mouse cell atlas (MCA). We reveal a single-cell hierarchy for many tissues that have not been well characterized previously. We built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression. Our study demonstrates the wide applicability of the Microwell-seq technology and MCA resource.