Cancer stem cells (CSCs) represent a unique sub-population of tumor cells with the ability to initiate tumor growth and sustain self-renewal. Although CSC biomarkers have been described for various tumors, only a few markers have been identified for nasopharyngeal carcinoma (NPC). In this study, we show that CD24+ cells isolated from human NPC cell lines express stem cell genes (Sox2, Oct4, Nanog, Bmi-1, and Rex-1), and show activation of the Wnt/β-catenin signaling pathway. CD24+ cells possess typical CSC characteristics that include enhanced cell proliferation, increased colony and sphere formation, maintenance of cell differentiation potential in prolonged culture, and enhanced resistance to chemotherapeutic drugs. Notably, CD24+ cells produce tumors following inoculation of as few as 500 cells in immunodeficient NOD/SCID mice. CD24+ cells further show increased invasion ability in vitro, which correlates with enhanced expression of matrix metalloproteinase 2 and 9. In summary, our results suggest that CD24 represents a novel CSC biomarker in NPC.

Elevate(™) a offered lower incidence of mesh erosion and comparable results on anatomical POP correction; however, incidence of de novo SUI was high. There is an apparent lengthening of implanted Elevate® mesh sonographically.

Bacterial keratitis (BK), a painful and fulminant bacterial infection of the cornea, is the most common type of vision-threatening infectious keratitis (IK). A rapid clinical diagnosis by an ophthalmologist may often help prevent BK patients from progression to corneal melting or even perforation, but many rural areas cannot afford an ophthalmologist. Thanks to the rapid development of deep learning (DL) algorithms, artificial intelligence via image could provide an immediate screening and recommendation for patients with red and painful eyes. Therefore, this study aims to elucidate the potentials of different DL algorithms for diagnosing BK via external eye photos. External eye photos of clinically suspected IK were consecutively collected from five referral centers. The candidate DL frameworks, including ResNet50, ResNeXt50, DenseNet121, SE-ResNet50, EfficientNets B0, B1, B2, and B3, were trained to recognize BK from the photo toward the target with the greatest area under the receiver operating characteristic curve (AUROC). Via five-cross validation, EfficientNet B3 showed the most excellent average AUROC, in which the average percentage of sensitivity, specificity, positive predictive value, and negative predictive value was 74, 64, 77, and 61. There was no statistical difference in diagnostic accuracy and AUROC between any two of these DL frameworks. The diagnostic accuracy of these models (ranged from 69 to 72%) is comparable to that of the ophthalmologist (66% to 74%). Therefore, all these models are promising tools for diagnosing BK in first-line medical care units without ophthalmologists.

A total of 145,293 serum samples were collected from volunteer blood donors in 13 provinces of China and tested for anti-HTLV antibody using an ELISA licensed in China. Thirty were positive for anti-HTLV by ELISA and 19 of those 30 samples were confirmed positive using a supplemental immunoblot assay. Anti-HTLV-I was detected only in samples from Fujian province (a Southern province) where the positivity rate was approximately 0.05%. The region encoding gp46 from 5 of the 19 antibody positive samples was amplified and sequenced. Sequence analysis indicated that these isolates belong to HTLV-I genotype A. These data suggest that HTLV-I is not endemic throughout China and the virus may be restricted to a particular region.

Age ≥66 year, neurological factors like CVA and Parkinsonian disease, pre-operative MUCP ≥60 cmH O, MFR < 15 mL, Dmax ≥ 20 cmH O, and PVR ≥ 200 mL are independent risk factors for developing post-operative DO following vaginal PRS for advanced POP.

This study demonstrated an association between sP-selectin levels and ABO groups in a Chinese Han population, implicating its generalizability to other ethnic groups. This finding will improve the understanding of the mechanism of ABO blood group-associated diseases.

BACKGROUND:The Paris System for Reporting Urinary Cytology (TPS) has been shown to improve bladder cancer diagnosis. Advances in artificial intelligence (AI) may assist and improve the clinical workflow by applying TPS in routine diagnostic services. METHODS: A deep-learning-based algorithm was developed to identify urothelial cancer candidate cells using whole-slide images (WSIs). In the testing cohort, 131 urine cytology slides were retrospectively retrieved and analyzed using this AI algorithm. The authors compared the performance of one cytopathologist and two cytotechnologists using AIassisted digital urine cytology. Then, the AI-assisted WSIs were evaluated in the clinical workflow. The cytopathologist first made a diagnosis by reviewing the AI-inferred WSIs and quantitative data (nuclear-to-cytoplasmic ratio and nuclear size) for each sample. After a washout period, the same cytopathologist made a diagnosis for the same samples using direct microscopy. All diagnosis results were compared with the expert panel consensus. RESULTS: The AI-assisted diagnosis by the two cytotechnologists and the one cytopathologist demonstrated performance results that were comparable to the expert panel consensus (sensitivity, 79.5% and 82.1% vs. 92.3%, respectively; specificity, 100% and 98.9% vs. 100%, respectively).Furthermore, the performance of the AI-assisted WSIs compared with the microscopic diagnosis by the cytopathologist demonstrated superior sensitivity (92.3% vs. 87.2%) and negative predictive value (96.8% vs. 94.8%). In addition, the AIassisted reporting demonstrated near perfect agreement with the expert panel consensus (κ = 0.944) and the microscopic diagnosis (κ = 0.862).

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