TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.

The intricate interaction between major histocompatibility complexes (MHCs) and antigen peptides with diverse amino acid sequences plays a pivotal role in immune responses and T cell activity. In recent years, deep learning (DL)-based models have emerged as promising tools for accelerating antigen peptide screening. However, most of these models solely rely on one-dimensional amino acid sequences, overlooking crucial information required for the three-dimensional (3-D) space binding process.

Cytoprotective Role of Heme Oxygenase-1 in Cancer Chemoresistance: Focus on Antioxidant, Antiapoptotic, and Pro-Autophagy Properties.

Chemoresistance remains the foremost challenge in cancer therapy. Targeting reactive oxygen species (ROS) manipulation is a promising strategy in cancer treatment since tumor cells present high levels of intracellular ROS, which makes them more vulnerable to further ROS elevation than normal cells. Nevertheless, dynamic redox evolution and adaptation of tumor cells are capable of counteracting therapy-induced oxidative stress, which leads to chemoresistance.

A modified CVD method for the synthesis of monolayer MoSand photoelectric improvement by HfOpassivation.

Molybdenum disulfide (MoS) is an emerging class of new materials with a wide range of potential practical applications. However, the uncontrollability of monolayer MoSsynthesized by traditional chemical vapor deposition method and the low responsivity of MoSphotodetectors limit its further development in the field of photoelectric detection. To achieve controlled growth of monolayer MoSand construct MoSphotodetectors with a high responsivity, we propose a novel single crystal growth strategy of high-quality MoSby controlling the Mo to S vapor ratio near the substrate, and deposit a layer of hafnium oxide (HfO) on the surface of MoSto enhance the performance of the pristine metal-semiconductor-metal structure photodetector.

Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach.

Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy.

Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors.

Machine learning including modern deep learning models has been extensively used in drug design and screening. However, reliable prediction of molecular properties is still challenging when exploring out-of-domain regimes, even for deep neural networks. Therefore, it is important to understand the uncertainty of model predictions, especially when the predictions are used to guide further experiments.

Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer.

Ovarian cancer remains the most lethal gynecological malignancy. Ferroptosis, a specialized form of iron-dependent, nonapoptotic cell death, plays a crucial role in various cancers. However, the contribution of ferroptosis to ovarian cancer is poorly understood.

Erastin synergizes with cisplatin via ferroptosis to inhibit ovarian cancer growth in vitro and in vivo.

Aim: Cisplatin-based chemotherapy is the first-line treatment for ovarian cancer. However, acquired resistance to cisplatin treatment or serious side effects often occurs in ovarian cancer, and thus, there is an urgent need for effective and combined therapies to overcome such obstacles. In the present study, we aimed to uncover synergistic effects between erastin and cisplatin (CDDP) in inhibiting ovarian cancer cell growth by inducing ferroptosis in vitro and in vivo.

Unexpected primary fallopian tube carcinoma during gynecological operations: Clinicopathological and prognostic factors analyses of 67 cases.

Objective: The aim of the study was to analyze the clinicopathologic features, the survival rate, and the prognostic factors of women with unexpected primary fallopian tube carcinoma diagnosed during gynecological operations.

Materials And Methods: We reviewed medical records of patients with unexpected primary fallopian tube carcinoma at the Obstetrics and Gynecology Hospital of Fudan University between January 2004 to December 2017. The survival analysis was based on the Kaplan-Meier method, and the results were compared using the log-rank test.

Nrf2 induces cisplatin resistance via suppressing the iron export related gene SLC40A1 in ovarian cancer cells.

Induction of Nuclear factor erythroid 2 (NF-E2)-related factor 2 (Nrf2) has been demonstrated to be involved in cisplatin resistance in ovarian cancer. Solute carrier family 40 member 1 (SLC40A1) is an iron exporter, which possesses many putative Nrf2 binding sites. Here we hypothesize that it may be a possible downstream gene of Nrf2.

ABCF2, an Nrf2 target gene, contributes to cisplatin resistance in ovarian cancer cells.

Previously, we have demonstrated that NRF2 plays a key role in mediating cisplatin resistance in ovarian cancer. To further explore the mechanism underlying NRF2-dependent cisplatin resistance, we stably overexpressed or knocked down NRF2 in parental and cisplatin-resistant human ovarian cancer cells, respectively. These two pairs of stable cell lines were then subjected to microarray analysis, where we identified 18 putative NRF2 target genes.

Primary Fallopian Tube Carcinoma: A Single-Institution Experience of 101 Cases: A Retrospective Study.

Objective: This study aimed to identify the prognostic factors for primary fallopian tube carcinoma.

Methods: A retrospective analysis was conducted of the patients treated with primary surgery and adjuvant chemotherapy at the Obstetrics and Gynecology Hospital of Fudan University from February 2003 to December 2010. Cox proportional hazards model was used for univariate and multivariate survival analysis.

Retrospective analysis of the clinicopathologic and prognostic characteristics of stage I placental site trophoblastic tumor in China.

Objective: To investigate clinicopathologic features and identify prognostic factors of placental site trophoblastic tumor (PSTT).

Methods: In a retrospective study, data were analyzed from patients with stage I PSTT treated at a tertiary hospital in Shanghai, China, from January 2007 to May 2013. Univariate log-rank tests were used to examine the association between clinicopathologic characteristics and overall survival and disease-free survival (DFS).

Induction of autophagy contributes to cisplatin resistance in human ovarian cancer cells.

Cisplatin resistance is a major challenge in the clinical treatment of ovarian cancer, of which the underlying mechanisms remain unknown. The aim of the present study was to explore the role of autophagy in cisplatin resistance in ovarian cancer cells. A2780cp cisplatin-resistant ovarian carcinoma cells and the A2780 parental cell line, were used as a model throughout the present study.

Nrf2 induces cisplatin resistance through activation of autophagy in ovarian carcinoma.

Unlabelled: Cisplatin resistance is a major problem affecting ovarian carcinoma treatment. NF-E2-related factor 2 (Nrf2), a nuclear transcription factor, plays an important role in chemotherapy resistance. However, the underlying mechanism by which Nrf2 mediates cisplatin chemoresistance is unclear.