Comparison of visual efficacy and patient's satisfaction between two toric IOLs, enhanced for intermediate vision and monofocal.

BACKGROUND: To compare the visual efficacy and patient satisfaction between 2 toric IOLs (TIOL), enhanced for intermediate vision or monofocal. METHODS: A retrospective chart review was performed of 100 eyes from 68 astigmatic patients who underwent cataract surgery with implantation of a Tecnis Eyhance Toric II IOL (Johnson & Johnson Vision, Irvine, CA, USA) (Group 1, 50 eyes) or Tecnis TIOL (Group 2, 50 eyes). The uncorrected distant (UDVA), intermediate (UIVA), and near (UNVA) visual acuities; residual refractive astigmatism; defocus curve; and IOL axis rotation were evaluated at 1 week, 1 month, 2 months, and 3 months postoperatively. RESULTS: The postoperative UDVA and UNVA refractive astigmatism values of the two groups were better than the preoperative measurements (P < 0.05), but there was no statistical difference between the two groups (P > 0.05). The postoperative UIVA of group 1 (0.18 ± 0.15 logMAR) was significantly better than that of group 2 (0.30 ± 0.25) (P < 0.05). The mean postoperative IOL rotation of group 1 (2.51 ± 0.79°) was lower than that of group 2 (3.02 ± 0.84°) (P < 0.05). Overall satisfaction of group 1 (1.27 ± 0.47) was better than that of group 2 (2.02 ± 0.53) (P < 0.05). CONCLUSIONS: The Tecnis Eyhance Toric II IOL demonstrated less postoperative IOL rotation and excellent uncorrected intermediate vision compared with the Tecnis TIOL. Near visual acuity and overall satisfaction with the Tecnis Eyhance Toric II IOL were also higher than those with the Tecnis TIOL.

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.