Precision measurements of A1N in the deep inelastic regime

We have performed precision measurements of the double-spin virtual-photon asymmetry A1A1 on the neutron in the deep inelastic scattering regime, using an open-geometry, large-acceptance spectrometer and a longitudinally and transversely polarized 3He target. Our data cover a wide kinematic range 0.277≤x≤0.5480.277≤x≤0.548 at an average Q2Q2 value of 3.078 (GeV/c)2, doubling the available high-precision neutron data in this x range. We have combined our results with world data on proton targets to make a leading-order extraction of the ratio of polarized-to-unpolarized parton distribution functions for up quarks and for down quarks in the same kinematic range. Our data are consistent with a previous observation of anA1n zero crossing near x=0.5x=0.5. We find no evidence of a transition to a positive slope in(Δd+Δd¯)/(d+d¯) up to x=0.548x=0.548

The IDENTIFY study: the investigation and detection of urological neoplasia in patients referred with suspected urinary tract cancer - a multicentre observational study

Objective To evaluate the contemporary prevalence of urinary tract cancer (bladder cancer, upper tract urothelial cancer [UTUC] and renal cancer) in patients referred to secondary care with haematuria, adjusted for established patient risk markers and geographical variation. Patients and Methods This was an international multicentre prospective observational study. We included patients aged ≥16 years, referred to secondary care with suspected urinary tract cancer. Patients with a known or previous urological malignancy were excluded. We estimated the prevalence of bladder cancer, UTUC, renal cancer and prostate cancer; stratified by age, type of haematuria, sex, and smoking. We used a multivariable mixed-effects logistic regression to adjust cancer prevalence for age, type of haematuria, sex, smoking, hospitals, and countries. Results Of the 11 059 patients assessed for eligibility, 10 896 were included from 110 hospitals across 26 countries. The overall adjusted cancer prevalence (n = 2257) was 28.2% (95% confidence interval [CI] 22.3–34.1), bladder cancer (n = 1951) 24.7% (95% CI 19.1–30.2), UTUC (n = 128) 1.14% (95% CI 0.77–1.52), renal cancer (n = 107) 1.05% (95% CI 0.80–1.29), and prostate cancer (n = 124) 1.75% (95% CI 1.32–2.18). The odds ratios for patient risk markers in the model for all cancers were: age 1.04 (95% CI 1.03–1.05; P < 0.001), visible haematuria 3.47 (95% CI 2.90–4.15; P < 0.001), male sex 1.30 (95% CI 1.14–1.50; P < 0.001), and smoking 2.70 (95% CI 2.30–3.18; P < 0.001). Conclusions A better understanding of cancer prevalence across an international population is required to inform clinical guidelines. We are the first to report urinary tract cancer prevalence across an international population in patients referred to secondary care, adjusted for patient risk markers and geographical variation. Bladder cancer was the most prevalent disease. Visible haematuria was the strongest predictor for urinary tract cancer

Post-transplant small cell carcinoma arising in the native kidney of renal transplant recipient

Small cell carcinoma (SCC) originating from kidney is extremely rare. To date, there has been no reported case of primary SCC of renal transplant recipients' (RTRs)' own kidney. Here, we report the first case of primary SCC of RTRs' own kidney. Resection of bilateral native kidneys, possessing whole length of ureters and small cuffs of bladder along with a neoplasm located in her right kidney, was performed on a 68-year-old female patient, five years after renal transplantation. The immuno-histochemical result confirmed mixed SCC of the right kidney (SCC combined with little transitional cell carcinoma). Postoperatively, platinum-based adjuvant chemotherapy (Cisplatin combined with Gemcitabine) was given, and the patient is still alive with well-functioning transplanted kidney

Multi-omic comparison of Alzheimer's variants in human ESC-derived microglia reveals convergence at APOE.

Variations in many genes linked to sporadic Alzheimer's disease (AD) show abundant expression in microglia, but relationships among these genes remain largely elusive. Here, we establish isogenic human ESC-derived microglia-like cell lines (hMGLs) harboring AD variants in CD33, INPP5D, SORL1, and TREM2 loci and curate a comprehensive atlas comprising ATAC-seq, ChIP-seq, RNA-seq, and proteomics datasets. AD-like expression signatures are observed in AD mutant SORL1 and TREM2 hMGLs, while integrative multi-omic analysis of combined epigenetic and expression datasets indicates up-regulation of APOE as a convergent pathogenic node. We also observe cross-regulatory relationships between SORL1 and TREM2, in which SORL1R744X hMGLs induce TREM2 expression to enhance APOE expression. AD-associated SORL1 and TREM2 mutations also impaired hMGL Aβ uptake in an APOE-dependent manner in vitro and attenuated Aβ uptake/clearance in mouse AD brain xenotransplants. Using this modeling and analysis platform for human microglia, we provide new insight into epistatic interactions in AD genes and demonstrate convergence of microglial AD genes at the APOE locus

Deep Learning with Quantitative Features of Magnetic Resonance Images to Predict Biochemical Recurrence of Radical Prostatectomy: A Multi-Center Study

Biochemical recurrence (BCR) occurs in up to 27% of patients after radical prostatectomy (RP) and often compromises oncologic survival. To determine whether imaging signatures on clinical prostate magnetic resonance imaging (MRI) could noninvasively characterize biochemical recurrence and optimize treatment. We retrospectively enrolled 485 patients underwent RP from 2010 to 2017 in three institutions. Quantitative and interpretable features were extracted from T2 delineated tumors. Deep learning-based survival analysis was then applied to develop the deep-radiomic signature (DRS-BCR). The model’s performance was further evaluated, in comparison with conventional clinical models. The model achieved C-index of 0.802 in both primary and validating cohorts, outweighed the CAPRA-S score (0.677), NCCN model (0.586) and Gleason grade group systems (0.583). With application analysis, DRS-BCR model can significantly reduce false-positive predictions, so that nearly one-third of patients could benefit from the model by avoiding overtreatments. The deep learning-based survival analysis assisted quantitative image features from MRI performed well in prediction for BCR and has significant potential in optimizing systemic neoadjuvant or adjuvant therapies for prostate cancer patients