TMT proteomics analysis of a pseudocereal crop, quinoa (Chenopodium quinoa Willd.), during seed maturation.

Quinoa (Chenopodium quinoa Willd.), an Andean native crop, is increasingly popular around the world due to its high nutritional content and stress tolerance. The production and the popularity of this strategic global food are greatly restricted by many limiting factors, such as seed pre-harvest sprouting, bitter saponin, etc. To solve these problems, the underlying mechanism of seed maturation in quinoa needs to be investigated. In this study, based on the investigation of morphological characteristics, a quantitative analysis of its global proteome was conducted using the combinational proteomics of tandem mass tag (TMT) labeling and parallel reaction monitoring (PRM). The proteome changes related to quinoa seed maturation conversion were monitored to aid its genetic improvement. Typical changes of morphological characteristics were discovered during seed maturation, including mean grain diameter, mean grain thickness, mean hundred-grain weight, palea, episperm color, etc. With TMT proteomics analysis, 581 differentially accumulated proteins (DAPs) were identified. Functional classification analysis and Gene Ontology enrichment analysis showed that most DAPs involved in photosynthesis were downregulated, indicating low levels of photosynthesis. DAPs that participated in glycolysis, such as glyceraldehyde-3-phosphate dehydrogenase, pyruvate decarboxylase, and alcohol dehydrogenase, were upregulated to fulfill the increasing requirement of energy consumption during maturation conversion. The storage proteins, such as globulins, legumins, vicilins, and oleosin, were also increased significantly during maturation conversion. Protein-protein interaction analysis and function annotation revealed that the upregulation of oleosin, oil body-associated proteins, and acyl-coenzyme A oxidase 2 resulted in the accumulation of oil in quinoa seeds. The downregulation of ß-amyrin 28-oxidase was observed, indicating the decreasing saponin content, during maturation, which makes the quinoa "sweet". By the PRM and qRT-PCR analysis, the expression patterns of most selected DAPs were consistent with the result of TMT proteomics. Our study enhanced the understanding of the maturation conversion in quinoa. This might be the first and most important step toward the genetic improvement of quinoa.

Lightweight deep neural networks for cholelithiasis and cholecystitis detection by point-of-care ultrasound.

BACKGROUND AND OBJECTIVE: Emergency physicians (EPs) frequently deal with abdominal pain, including that is caused by either gallstones or acute cholecystitis. Easy access and low cost justify point-of-care ultrasound (POCUS) use as a first-line test to detect these diseases; yet, the detection performance of POCUS by EPs is unreliable, causing misdiagnoses with serious impacts. This study aimed to develop a machine learning system to detect and localize gallstones and to detect acute cholecystitis by ultrasound (US) still images taken by physicians or technicians for preliminary diagnoses. METHODS: Abdominal US images (> 89,000) were collected from 2386 patients in a hospital database. We constructed training sets for gallstones with or without cholecystitis (N = 10,971) and cholecystitis with or without gallstones (N = 7348) as positives. Validation sets were also constructed for gallstones (N = 2664) and cholecystitis (N = 1919). We applied a single-shot multibox detector (SSD) and a feature pyramid network (FPN) to classify and localize objects using image features extracted by ResNet-50 for gallstones, and MobileNet V2 to classify cholecystitis. The deep learning models were pretrained using the COCO-2017 and ILSVRC-2012 datasets. RESULTS: Using the validation sets, the SSD-FPN-ResNet-50 and MobileNet V2 achieved areas under the receiver operating characteristics curve of 0.92 and 0.94, respectively. The inference speeds were 21 (47.6 frames per second, fps) and 7 ms (142.9 fps). CONCLUSIONS: A machine learning system was developed to detect and localize gallstones, and to detect cholecystitis, with acceptable discrimination and speed. This is the first study to develop this system for either gallstone or cholecystitis detection with absence or presence of each one. After clinical trials, this system may be used to assist EPs, including those in remote areas, for detecting these diseases.

Deep Learning-Based Detection of Early Renal Function Impairment Using Retinal Fundus Images: Model Development and Validation.

BACKGROUND: Retinal imaging has been applied for detecting eye diseases and cardiovascular risks using deep learning-based methods. Furthermore, retinal microvascular and structural changes were found in renal function impairments. However, a deep learning-based method using retinal images for detecting early renal function impairment has not yet been well studied. OBJECTIVE: This study aimed to develop and evaluate a deep learning model for detecting early renal function impairment using retinal fundus images. METHODS: This retrospective study enrolled patients who underwent renal function tests with color fundus images captured at any time between January 1, 2001, and August 31, 2019. A deep learning model was constructed to detect impaired renal function from the images. Early renal function impairment was defined as estimated glomerular filtration rate <90 mL/min/1.73 m2. Model performance was evaluated with respect to the receiver operating characteristic curve and area under the curve (AUC). RESULTS: In total, 25,706 retinal fundus images were obtained from 6212 patients for the study period. The images were divided at an 8:1:1 ratio. The training, validation, and testing data sets respectively contained 20,787, 2189, and 2730 images from 4970, 621, and 621 patients. There were 10,686 and 15,020 images determined to indicate normal and impaired renal function, respectively. The AUC of the model was 0.81 in the overall population. In subgroups stratified by serum hemoglobin A1c (HbA1c) level, the AUCs were 0.81, 0.84, 0.85, and 0.87 for the HbA1c levels of ≤6.5%, >6.5%, >7.5%, and >10%, respectively. CONCLUSIONS: The deep learning model in this study enables the detection of early renal function impairment using retinal fundus images. The model was more accurate for patients with elevated serum HbA1c levels.

Two new susceptibility loci 1q24.2 and 11p11.2 confer risk to severe acne.

Severe acne is a chronic inflammatory skin disorder characterized by widespread inflammatory lesions including nodules, cysts and potential scarring. Here we perform the first genome-wide association study of severe acne in a Chinese Han population comprising 1,056 cases and 1,056 controls using the Illumina HumanOmniZhongHua-8 BeadChip. In an independent cohort of 1,860 cases and 3,660 controls of Chinese Han, we replicate 101 SNPs of which 3 showed consistent association. We identify two new susceptibility loci at 11p11.2 (DDB2, rs747650, P(combined)=4.41 × 10⁻9 and rs1060573, P(combined)=1.28 × 10⁻8) and 1q24.2 (SELL, rs7531806, P(combined)=1.20 × 10⁻8) that are involved in androgen metabolism, inflammation processes and scar formation in severe acne. These results point to new genetic susceptibility factors and suggest several new biological pathways related to severe acne.

Functionalized carbon nanotubes as the pseudostationary phase for capillary EKC separation of non-steroidal anti-inflammatory drugs.

Functionalized multiwalled carbon nanotubes (f-MWCNTs) can serve as the pseudostationary phase (PSP) for the capillary EKC separation of non-steroidal anti-inflammatory drugs (NSAIDs). To increase their hydrophilicity, we treated MWCNTs, with a sonochemical process in a concentrated nitric/sulfuric acid mixture. The oxidized MWCNTs were then characterized by FT-IR, transmission electron microscopy, and X-ray photoelectron spectroscopy. We evaluated the potential of the PSP and the effects of buffer composition, pH, addition of organic modifier, and injection temperature on the NSAID separation. The PSP created a network structure of pi-pi interactions, hydrophobic forces, hydrogen bonding, and electrostatic interactions to separate NSAIDs, providing a different separation mode from SDS micelles. We achieved complete separation of six NSAIDs using a mixture of a borate buffer (75 mM, pH 10) with methanol (5%, v/v) containing 0.02 mg/mL f-MWCNTs, an applied voltage of +12 kV and detection at 214 nm. Better precision was obtained with a low injection temperature. The method was also satisfactorily applied to the analysis of NSAIDs spiked into a urine sample.