Relation between change of effective lens position and toric IOL rotation after toric IOL implantation.

OBJECTIVE: To evaluate the relationship between change of effective lens position (ELP) and toric intraocular lens (IOL) rotation in patients with increasing postoperative refractive astigmatism after successful toric IOL implantation. METHODS: The subjects include 61 people (61 eyes) with increasing residual astigmatism >0.5 D 3 months after successful toric IOL implantation. Clinical measurements included preoperative, 1-week, and 1-, 2-, and 3-month postoperative visual acuity; refraction; keratometer; anterior and posterior corneal astigmatism; ELP by Scheimpflug camera imaging; and IOL axis by slit-lamp biomicroscopic photograph with pupil dilation. RESULTS: Residual astigmatism in postoperative month 3 (0.81 ± 0.50 D) is higher than that at postoperative week 1 (0.41 ± 0.38 D; p < 0.05). ELP decreased by 264.44 ± 163.25 µm, and the IOL rotated by 2.91 ± 1.44 degrees from week 1 to month 3 (p < 0.05). The ELP change had a positive correlation with IOL rotation (R2 = 0.383; p = 0.006), and the postoperative refractive astigmatic change had a positive correlation with ELP change (R2 = 0.272; p = 0.027) and IOL rotation (R2 = 0.272; p = 0.0001) from week 1 to month 3. CONCLUSION: ELP change can influence toric IOL rotation and increase residual astigmatism after toric IOL implantation.

Systematic dissection of tumor-normal single-cell ecosystems across a thousand tumors of 30 cancer types.

The complexity of the tumor microenvironment poses significant challenges in cancer therapy. Here, to comprehensively investigate the tumor-normal ecosystems, we perform an integrative analysis of 4.9 million single-cell transcriptomes from 1070 tumor and 493 normal samples in combination with pan-cancer 137 spatial transcriptomics, 8887 TCGA, and 1261 checkpoint inhibitor-treated bulk tumors. We define a myriad of cell states constituting the tumor-normal ecosystems and also identify hallmark gene signatures across different cell types and organs. Our atlas characterizes distinctions between inflammatory fibroblasts marked by AKR1C1 or WNT5A in terms of cellular interactions and spatial co-localization patterns. Co-occurrence analysis reveals interferon-enriched community states including tertiary lymphoid structure (TLS) components, which exhibit differential rewiring between tumor, adjacent normal, and healthy normal tissues. The favorable response of interferon-enriched community states to immunotherapy is validated using immunotherapy-treated cancers (n = 1261) including our lung cancer cohort (n = 497). Deconvolution of spatial transcriptomes discriminates TLS-enriched from non-enriched cell types among immunotherapy-favorable components. Our systematic dissection of tumor-normal ecosystems provides a deeper understanding of inter- and intra-tumoral heterogeneity.

Single-cell mapping of combinatorial target antigens for CAR switches using logic gates.

Identification of optimal target antigens that distinguish cancer cells from normal surrounding tissue cells remains a key challenge in chimeric antigen receptor (CAR) cell therapy for tumors with intratumoral heterogeneity. In this study, we dissected tissue complexity to the level of individual cells through the construction of a single-cell expression atlas that integrates ~1.4 million tumor, tumor-infiltrating normal and reference normal cells from 412 tumors and 12 normal organs. We used a two-step screening method using random forest and convolutional neural networks to select gene pairs that contribute most to discrimination between individual malignant and normal cells. Tumor coverage and specificity are evaluated for the AND, OR and NOT logic gates based on the combinatorial expression pattern of the pairing genes across individual single cells. Single-cell transcriptome-coupled epitope profiling validates the AND, OR and NOT switch targets identified in ovarian cancer and colorectal cancer.

Hyperglycemic memory in metabolism and cancer.

Hyperglycemia is a hallmark of both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Recent evidence strongly suggests that prolonged exposure to hyperglycemia can epigenetically modify gene expression profiles in human cells and that this effect is sustained even after hyperglycemic control is therapeutically achieved; this phenomenon is called hyperglycemic memory. This metabolic memory effect contributes substantially to the pathology of various diabetic complications, such as diabetic retinopathy, hypertension, and diabetic nephropathy. Due to the metabolic memory in cells, diabetic patients suffer from various complications, even after hyperglycemia is controlled. With regard to this strong association between diabetes and cancer risk, cancer cells have emerged as key target cells of hyperglycemic memory in diabetic cancer patients. In this review, we will discuss the recent understandings of the molecular mechanisms underlying hyperglycemic memory in metabolism and cancer.