Developing the biomarker panels and drugs by proteomic analysis for autoimmune uveitis and posterior scleritis.

Autoimmune uveitis and posterior scleritis are ocular diseases caused by immune dysregulation. Their pathogenesis remains elusive, and delayed diagnosis can exacerbate vision loss. Our study analyzed proteomic profiles of 190 patients with Behcet's disease uveitis, posterior scleritis, and Vogt-Koyanagi-Harada syndrome.

QL1604 plus paclitaxel-cisplatin/carboplatin in patients with recurrent or metastatic cervical cancer: an open-label, single-arm, phase II trial.

Objective: QL1604 is a highly selective, humanized monoclonal antibody against programmed death protein 1. We assessed the efficacy and safety of QL1604 plus chemotherapy as first-line treatment in patients with advanced cervical cancer.

Methods: This was a multicenter, open-label, single-arm, phase II study.

Deep Learning-Based Detection and Classification of Bone Lesions on Staging Computed Tomography in Prostate Cancer: A Development Study.

Rationale And Objectives: Efficiently detecting and characterizing metastatic bone lesions on staging CT is crucial for prostate cancer (PCa) care. However, it demands significant expert time and additional imaging such as PET/CT. We aimed to develop an ensemble of two automated deep learning AI models for 1) bone lesion detection and segmentation and 2) benign vs.

First Measurement of the Z→μ^{+}μ^{-} Angular Coefficients in the Forward Region of pp Collisions at sqrt[s]=13 TeV.

The first study of the angular distribution of μ^{+}μ^{-} pairs produced in the forward rapidity region via the Drell-Yan reaction pp→γ^{*}/Z+X→ℓ^{+}ℓ^{-}+X is presented, using data collected with the LHCb detector at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 5.1  fb^{-1}. The coefficients of the five leading terms in the angular distribution are determined as a function of the dimuon transverse momentum and rapidity.

Systematic screening reveals synergistic interactions that overcome MAPK inhibitor resistance in cancer cells.

Objective: Effective adjuvant therapeutic strategies are urgently needed to overcome MAPK inhibitor (MAPKi) resistance, which is one of the most common forms of resistance that has emerged in many types of cancers. Here, we aimed to systematically identify the genetic interactions underlying MAPKi resistance, and to further investigate the mechanisms that produce the genetic interactions that generate synergistic MAPKi resistance.

Methods: We conducted a comprehensive pair-wise sgRNA-based high-throughput screening assay to identify synergistic interactions that sensitized cancer cells to MAPKi, and validated 3 genetic combinations through competitive growth, cell viability, and spheroid formation assays.

Artificial intelligence for the detection of COVID-19 pneumonia on chest CT using multinational datasets.

Chest CT is emerging as a valuable diagnostic tool for clinical management of COVID-19 associated lung disease. Artificial intelligence (AI) has the potential to aid in rapid evaluation of CT scans for differentiation of COVID-19 findings from other clinical entities. Here we show that a series of deep learning algorithms, trained in a diverse multinational cohort of 1280 patients to localize parietal pleura/lung parenchyma followed by classification of COVID-19 pneumonia, can achieve up to 90.

Deep Gray Matter Iron Deposition and Its Relationship to Clinical Features in Cerebral Autosomal Dominant Arteriopathy With Subcortical Infarcts and Leukoencephalopathy Patients: A 7.0-T Magnetic Resonance Imaging Study.

Background and Purpose- Distribution patterns of iron deposition in deep gray matter and their association with clinical characteristics in cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) remain unclear. We aimed to evaluate iron deposition in deep gray matter in patients with CADASIL using 7.0-T susceptibility-weighted imaging and mapping and to explore its correlations with clinical characteristics.