Hsa_circ_0001756 drives gastric cancer glycolysis by increasing the expression and stability of PGK1 mRNA.

Introduction: Strategies for preventing high glycolysis in tumour cells are urgently needed. CircRNAs (circRNAs) play important roles in glycolysis. However, the mechanism underlying the effects of hsa_circ_0001756 in gastric cancer (GC) remains unclear.

Hsa_circ_0002301 inhibits ferroptosis in gastric cancer by encoding the de novo protein HECTD1-463aa.

Background: CircRNAs are closely related to ferroptosis in gastric cancer cells; however, the mechanism by which circRNAs regulate ferroptosis in gastric carcinogenesis remains unknown. CircRNA-encoded novel peptides are functional products translated from the open reading frames (ORFs) within circular RNAs, demonstrating that circRNAs not only serve as non-coding regulators but also have the capacity to encode biologically active peptides. Compared with noncancerous cells, cancer cells have greater iron requirements, and ferroptosis occurs in response to radiotherapy, chemotherapy, and immunotherapy; therefore, ferroptosis activation may be a potential strategy to overcome the shortcomings of conventional cancer therapy.

Role of the AKT signaling pathway in regulating tumor-associated macrophage polarization and in the tumor microenvironment: A review.

Tumor-associated macrophages (TAMs) are present in and are important components of the tumor microenvironment (TME). TAMs differentiate into 2 functionally distinct morphologies, classically activated (M1)-type TAMs and alternatively activated (M2)-type TAMs, when stimulated by different cytokines. The 2 types of TAMs exhibit distinct properties and functions.

NPR1 promotes cisplatin resistance by inhibiting PARL-mediated mitophagy-dependent ferroptosis in gastric cancer.

Cisplatin-based chemotherapy serves as the standard of care for individuals with advanced stages of gastric cancer. Nevertheless, the emergence of chemoresistance in GC has detrimental impacts on prognosis, yet the underlying mechanisms governing this phenomenon remain elusive. Level of mitophagy and ferroptosis of GC cells were detected by fluorescence, flow cytometry, GSH, MDA, Fe assays, and to explore the specific molecular mechanisms between NPR1 and cisplatin resistance by performing western blot and coimmunoprecipitation (co-IP) assays.

Predictors of visual recovery in patients with macular edema secondary to central retinal vein occlusion after treatment with Conbercept.

Background: The use of Spectral domain optical coherence tomography (SD-OCT) to evaluate the predictors of visual acuity-recovery in patients treated with conbercept for macular edema (ME) secondary to central retinal vein occlusion (CRVO) has rarely been seen. We collected 26 CRVO-ME patients with different OCT measures at 6 months follow-up to identify the factors that are most strongly correlated with the best-corrected visual acuity (BCVA) post-treatment in CRVO-ME patients treated with conbercept.

Purpose: To evaluate the effectiveness of intravitreal conbercept injections for the treatment of CRVO-ME and to determine the major predictors of best-corrected visual acuity (BCVA) post-treatment.

Intra- and interobserver concordance of a new classification system for myopic maculopathy.

Background: Myopic maculopathy (MM) is one of the major causes of visual impairment and irreversible blindness in eyes with pathologic myopia (PM). However, the classification of each type of lesion associated with MM has not been determined. Recently, a new MM classification system, known as the ATN grading and classification system, was proposed; it is based on the fundus photographs and optical coherence tomography (OCT) images and includes three variable components: atrophy (A), traction (T), and neovascularization (N).

Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images.

Purpose: To develop and validate a deep transfer learning (DTL) algorithm for detecting abnormalities in fundus images from non-mydriatic fundus photography examinations.

Methods: A total of 1295 fundus images were collected to develop and validate a DTL algorithm for detecting abnormal fundus images. After removing 366 poor images, the DTL model was developed using 929 (370 normal and 559 abnormal) fundus images.