Presentation_1_Esophageal cancer detection via non-contrast CT and deep learning.PPTX

BackgroundEsophageal cancer is the seventh most frequently diagnosed cancer with a high mortality rate and the sixth leading cause of cancer deaths in the world. Early detection of esophageal cancer is very vital for the patients. Traditionally, contrast computed tomography (CT) was used to detect esophageal carcinomas, but with the development of deep learning (DL) technology, it may now be possible for non-contrast CT to detect esophageal carcinomas. In this study, we aimed to establish a DL-based diagnostic system to stage esophageal cancer from non-contrast chest CT images.MethodsIn this retrospective dual-center study, we included 397 primary esophageal cancer patients with pathologically confirmed non-contrast chest CT images, as well as 250 healthy individuals without esophageal tumors, confirmed through endoscopic examination. The images of these participants were treated as the training data. Additionally, images from 100 esophageal cancer patients and 100 healthy individuals were enrolled for model validation. The esophagus segmentation was performed using the no-new-Net (nnU-Net) model; based on the segmentation result and feature extraction, a decision tree was employed to classify whether cancer is present or not. We compared the diagnostic efficacy of the DL-based method with the performance of radiologists with various levels of experience. Meanwhile, a diagnostic performance comparison of radiologists with and without the aid of the DL-based method was also conducted.ResultsIn this study, the DL-based method demonstrated a high level of diagnostic efficacy in the detection of esophageal cancer, with a performance of AUC of 0.890, sensitivity of 0.900, specificity of 0.880, accuracy of 0.882, and F-score of 0.891. Furthermore, the incorporation of the DL-based method resulted in a significant improvement of the AUC values w.r.t. of three radiologists from 0.855/0.820/0.930 to 0.910/0.955/0.965 (p = 0.0004/ConclusionThe DL-based method shows a satisfactory performance of sensitivity and specificity for detecting esophageal cancers from non-contrast chest CT images. With the aid of the DL-based method, radiologists can attain better diagnostic workup for esophageal cancer and minimize the chance of missing esophageal cancers in reading the CT scans acquired for health check-up purposes.</p

The clinical application of single-sperm-based SNP haplotyping for PGD of osteogenesis imperfecta

<p>Osteogenesis imperfecta (OI) is a genetically heterogeneous disorder, presenting either autosomal dominant, autosomal recessive or X-linked inheritance patterns. The majority of OI cases are autosomal dominant and are caused by heterozygous mutations in either the <i>COL1A1</i> or <i>COL1A2</i> gene. In these dominant disorders, allele dropout (ADO) can lead to misdiagnosis in preimplantation genetic diagnosis (PGD). Polymorphic markers linked to the mutated genes have been used to establish haplotypes for identifying ADO and ensuring the accuracy of PGD. However, the haplotype of male patients cannot be determined without data from affected relatives. Here, we developed a method for single-sperm-based single-nucleotide polymorphism (SNP) haplotyping via next-generation sequencing (NGS) for the PGD of OI. After NGS, 10 informative polymorphic SNP markers located upstream and downstream of the <i>COL1A1</i> gene and its pathogenic mutation site were linked to individual alleles in a single sperm from an affected male. After haplotyping, a normal blastocyst was transferred to the uterus for a subsequent frozen embryo transfer cycle. The accuracy of PGD was confirmed by amniocentesis at 19 weeks of gestation. A healthy infant weighing 4,250 g was born via vaginal delivery at the 40th week of gestation. Single-sperm-based SNP haplotyping can be applied for PGD of any monogenic disorders or <i>de novo</i> mutations in males in whom the haplotype of paternal mutations cannot be determined due to a lack of affected relatives.</p> <p><b>Abbreviations:</b> ADO: allele dropout; DI: dentinogenesis imperfect; ESHRE: European Society of Human Reproduction and Embryology; FET: frozen embryo transfer; gDNA: genomic DNA; ICSI: intracytoplasmic sperm injection; IVF: in vitro fertilization; MDA: multiple displacement amplification; NGS: next-generation sequencing; OI: osteogenesis imperfect; PBS: phosphate buffer saline; PCR: polymerase chain reaction; PGD: preimplantation genetic diagnosis; SNP: single-nucleotide polymorphism; STR: short tandem repeat; TE: trophectoderm; WGA: whole-genome amplification</p

Examples of aging and broken MCs covers made of plastic composite.

<p>Examples of aging and broken MCs covers made of plastic composite.</p

Mosquito species composition of MCs with different features.

<p>Mosquito species composition of MCs with different features.</p

Examples of sewage MCs with different sizes and covers.

<p>Sewage MCs include those covers made of (M-7,9) cast iron, and (M-8,10) plastic composite. (m-7,8) the insides of MCs.</p

Physical parameters and mosquito production of CBs with different features.

<p>Physical parameters and mosquito production of CBs with different features.</p

DNA Molecular Beacon-Based Plastic Biochip: A Versatile and Sensitive Scanometric Detection Platform

In this paper, we report a novel DNA molecular beacon (MB)-based plastic biochip platform for scanometric detection of a range of analytical targets. Hairpin DNA strands, which are dually modified with amino and biotin groups at their two ends are immobilized on a disposable plastic (polycarbonate) substrate as recognition element and gold nanoparticle-assisted silver-staining as signal reading protocol. Initially, the immobilized DNA probes are in their folded forms; upon target binding the hairpin secondary structure of the probe strand is “forced” open (i.e., converted to the unfolded state). Nanogold-streptavidin conjugates can then bind the terminal biotin groups and promote the deposition of rather large silver particles which can be either directly visualized or quantified with a standard flatbed scanner. We demonstrate that with properly designed probe sequences and optimized preparation conditions, a range of molecular targets, such as DNA strands, proteins (thrombin) and heavy metal ions (Hg²⁺), can be detected with high sensitivity and excellent selectivity. The detection can be done in both standard physiological buffers and real world samples. This constitutes a platform technology for performing rapid, sensitive, cost-effective, and point-of-care (POC) chemical analysis and medical diagnosis

<i>Aedes albopictus</i> production in urban stormwater catch basins and manhole chambers of downtown Shanghai, China - Fig 3

<p><b>Examples of CBs with flat grates</b>, including those (A-1) connected with surface gutters, and those (A-2) that are independent; (a-1,2) the insides of CB chambers.</p

Physical parameters and mosquito production of MCs with different features.

<p>Physical parameters and mosquito production of MCs with different features.</p

<i>Aedes albopictus</i> production in urban stormwater catch basins and manhole chambers of downtown Shanghai, China - Fig 2

<p>A cross-sectional diagram of CBs with a flat grate (A) and a vertical grate (B).</p