Pancreatic ductal adenocarcinoma (PDAC), generally known as pancreatic cancer (PC), ranks the fourth leading cause of cancer-related deaths in the western world. While the incidence of pancreatic cancer is displaying a rising tendency every year, the mortality rate has not decreased significantly because of late diagnosis, early metastasis, and limited reaction to chemotherapy or radiotherapy. Adjuvant chemotherapy after surgical resection is typically the preferred option to treat early pancreatic cancer. Although 5-fluorouracil/leucovorin with irinotecan and oxaliplatin (FOLFIRINOX) and gemcitabine/nab-paclitaxel can profoundly improve the prognosis of advanced pancreatic cancer, the development of chemoresistance still leads to poor clinical outcomes. Chemoresistance is multifactorial as a result of the interaction among pancreatic cancer cells, cancer stem cells, and the tumor microenvironment. Nevertheless, more pancreatic cancer patients will benefit from precision treatment and targeted drugs. Therefore, we outline new perspectives for enhancing the efficacy of gemcitabine after reviewing the related factors of gemcitabine metabolism, mechanism of action, and chemoresistance.

Pancreatic cancer remains one of the deadliest cancers in the world, as a consequence of late diagnosis, early metastasis and limited response to chemotherapy, under which conditions the potential mechanism of pancreatic cancer progression requires further study. Exosomes are membrane vesicles which are important in the progression, metastasis and chemoresistance in pancreatic cancer. Additionally, they have been verified to be potential as biomarkers, targets and drug carriers for pancreatic cancer treatment. Thus, studying the role of exosomes in pancreatic cancer is significant. This paper focuses on the role of exosomes in the proliferation, metastasis and chemoresistance, as well as their potential applications for pancreatic cancer.

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Although radiation therapy has recently made great advances in cancer treatment, the majority of patients diagnosed with pancreatic cancer (PC) cannot achieve satisfactory outcomes due to intrinsic and acquired radioresistance. Identifying the molecular mechanisms that impair the efficacy of radiotherapy and targeting these pathways are essential to improve the radiation response of PC patients. Our goal is to identify sensitive targets for pancreatic cancer radiotherapy (RT) using the kinome-wide CRISPR-Cas9 loss-of-function screen and enhance the therapeutic effect through the development and application of targeted inhibitors combined with radiotherapy. We transduced pancreatic cancer cells with a protein kinase library; 2D and 3D library cells were irradiated daily with a single dose of up to 2 Gy for 4 weeks for a total of 40 Gy using an X-ray generator. Sufficient DNA was collected for next-generation deep sequencing to identify candidate genes. In this study, we identified several cell cycle checkpoint kinases and DNA damage related kinases in 2D- and 3D-cultivated cells, including DYRK1A, whose loss of function sensitizes cells to radiotherapy. Additionally, we demonstrated that the harmine-targeted suppression of DYRK1A used in conjunction with radiotherapy increases DNA double-strand breaks (DSBs) and impairs homologous repair (HR), resulting in more cancer cell death. Our results support the use of CRISPR-Cas9 screening to identify new therapeutic targets, develop radiosensitizers, and provide novel strategies for overcoming the tolerance of pancreatic cancer to radiotherapy.

Compared to type C2 hysterectomies, type C1 hysterectomies, using the DUV and its branches as an anatomical landmark, resulted in shorter hospitalization time, less blood loss, reduced bladder dysfunction, and uncompromised radicality.

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BackgroundReliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD.Materials and methodsA prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers.ResultsThe gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients.ConclusionBrain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.

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