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Upper gastrointestinal cancers (including oesophageal cancer and gastric cancer) are the most common cancers worldwide. Artificial intelligence platforms using deep learning algorithms have made remarkable progress in medical imaging but their application in upper gastrointestinal cancers has been limited. We aimed to develop and validate the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies. This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method. 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952–0·957) in the internal validation set, 0·927 (0·925–0·929) in the prospective set, and ranged from 0·915 (0·913–0·917) to 0·977 (0·977–0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 [95% CI 0·924–0·957] vs 0·945 [0·927–0·959]; p=0·692) and superior sensitivity compared with competent (0·858 [0·832–0·880], p<0·0001) and trainee (0·722 [0·691–0·752], p<0·0001) endoscopists. The positive predictive value was 0·814 (95% CI 0·788–0·838) for GRAIDS, 0·932 (0·913–0·948) for the expert endoscopist, 0·974 (0·960–0·984) for the competent endoscopist, and 0·824 (0·795–0·850) for the trainee endoscopist. The negative predictive value was 0·978 (95% CI 0·971–0·984) for GRAIDS, 0·980 (0·974–0·985) for the expert endoscopist, 0·951 (0·942–0·959) for the competent endoscopist, and 0·904 (0·893–0·916) for the trainee endoscopist. GRAIDS achieved high diagnostic accuracy in detecting upper gastrointestinal cancers, with sensitivity similar to that of expert endoscopists and was superior to that of non-expert endoscopists. This system could assist community-based hospitals in improving their effectiveness in upper gastrointestinal cancer diagnoses. The National Key R&D Program of China, the Natural Science Foundation of Guangdong Province, the Science and Technology Program of Guangdong, the Science and Technology Program of Guangzhou, and the Fundamental Research Funds for the Central Universities.

Dear Editor, True hermaphroditism is a condition in which the gonads, genital morphology, and sexuality simultaneously show both male and female characteristics （ovaries and testes） or in which both types of gonadal tissue exist in a single gonad （known as an ovariotestis）. True hermaphroditism associated with a chimeric or 46, XX/46, XY karyotype is extremely rare, and the genitalia of those affected can be characterized as female, male, or mixed.1-3 The cause of true hermaphroditism has not been determined because the condition is relatively rare and has a diverse phenotype.4

Purpose - Kano's model is extensively applied in industry and by researchers. However, the model has a shortcoming in that enterprises cannot use it to evaluate the influences of quality attributes on product precisely; the lack of consideration of the different attribute strengths among 25 possible outcomes affects judgment of the categories in Kano's evaluation sheet. The aim of this study is to measure the quality attribute strength of 25 possible outcomes in the evaluation sheet to develop a new Kano's evaluation sheet to improve the accuracy of the classification of the quality attributes.Design methodology approach - This study develops a new Kano's evaluation sheet, and defines the canonical and non-canonical judgment of the evaluation sheet based on a novel \"similarity\" calculation which calculates the response frequency and the distance between canonical judgment and non-canonical judgment.Findings - Quality attribute strength is probed and compared with the traditional Kano's evaluation sheet. The new Kano's evaluation sheet is more practical because it supports a precise judgment of the category of quality attributes. Empirical results also demonstrate that the new Kano's evaluation sheet is practical.Originality value - The new evaluation sheet presents not only a different logic of classification and statistical method for analyzing quality attributes, but also reviewed judgments of the category of quality attributes from Kano's traditional evaluation sheet.

This study demonstrates how a decision-making trial and evaluation laboratory (DEMATEL) threshold value can be quickly and reasonably determined in the process of combining DEMATEL and decomposed theory of planned behavior (DTPB) models. Models are combined to identify the key factors of a complex problem. This paper presents a case study of a food and beverage information system as an example. The analysis of the example indicates that, given direct and indirect relationships among variables, if a traditional DTPB model only simulates the effects of the variables without considering that the variables will affect the original cause-and-effect relationships among the variables, then the original DTPB model variables cannot represent a complete relationship. For the food and beverage example, a DEMATEL method was employed to reconstruct a DTPB model and, more importantly, to calculate reasonable DEMATEL threshold value for determining additional relationships of variables in the original DTPB model. This study is method-oriented, and the depth of investigation into any individual case is limited. Therefore, the methods proposed in various fields of study should ideally be used to identify deeper and more practical implications.

Product design is an inter-disciplinary project focused on strengthening product design capabilities given limitations in both time and costs. How to lower costs and improve the overall performance of the product supply chain is an important factor to ensure a company's ability to maintain its competitive advantage. In the study of product design issue, the exploration of time and cost are closely related to the enhancement of a company's product design capabilities. This study focuses in-depth on the issues of time and costs during product design using the conjoint analysis method to integrate consumer and designer partiality with the product in order to build two models for optimal product design based on normal and rush completion times. This is to provide reference points for corporations in terms of time and cost controls during product design. The objectives of the study are: (1) a review of related literature; (2) an overall planning of normal time and production design model framework; (3) an overall planning of rush completion time and product design model framework; (4) an application on a actual case; and (5) the conclusion. [PUBLICATION ABSTRACT]

The purpose of this study is to examine the interrelationships among the stock indexes of the upper, middle and lower stream of IC industry in Taiwan by using time series method. The results found that there is no long-term equilibrium relationship among the upper, middle and lower stream of IC industry in Taiwan. In the short-term, IC design and IC packaging industries have been influenced by all industries except manufacturing industry. From the causality test, there are no lead-lag relationship among the IC design, packaging and manufacturing industries. The empirical results of impulse response functions and variance decompositions point out that, comparing those IC industries, there is stronger sudden impulse to the exogenous in IC manufacturing industry. On the other hand, there are less and shorter impact of the exogenous disturbance in IC design and packaging industries.

Abstract Exosomes have been suggested as promising targets for the diagnosis and treatment of neurological diseases, including schizophrenia (SCZ), but the potential role of exosome-derived metabolites in these diseases was rarely studied. Using ultra-performance liquid chromatography-tandem mass spectrometry, we performed the first metabolomic study of serum-derived exosomes from patients with SCZ. Our sample comprised 385 patients and 332 healthy controls recruited from 3 clinical centers and 4 independent cohorts. We identified 25 perturbed metabolites in patients that can be used to classify samples from patients and control participants with 95.7% accuracy (95% CI: 92.6%–98.9%) in the training samples (78 patients and 66 controls). These metabolites also showed good to excellent performance in differentiating between patients and controls in the 3 test sets of participants, with accuracies 91.0% (95% CI: 85.7%–96.3%; 107 patients and 62 controls), 82.7% (95% CI: 77.6%–87.9%; 104 patients and 142 controls), and 99.0% (95% CI: 97.7%–100%; 96 patients and 62 controls), respectively. Bioinformatic analysis suggested that these metabolites were enriched in pathways implicated in SCZ, such as glycerophospholipid metabolism. Taken together, our findings support a role for exosomal metabolite dysregulation in the pathophysiology of SCZ and indicate a strong potential for exosome-derived metabolites to inform the diagnosis of SCZ.

The Hippo pathway plays critical roles in cell growth, differentiation, organ development and tissue homeostasis, whereas its dysregulation can lead to tumorigenesis. YAP and TAZ are transcription co-activators and represent the main downstream effectors of the Hippo pathway. Here, we show that heat stress induces a strong and rapid YAP dephosphorylation and activation. The effect of heat shock on YAP is dominant to other signals known to modulate the Hippo pathway. Heat shock inhibits LATS kinase by promoting HSP90-dependent LATS interaction with and inactivation by protein phosphatase 5. Heat shock also induces LATS ubiquitination and degradation. YAP and TAZ are crucial for cellular heat shock responses, including the heat shock transcriptome and cell viability. This study uncovers previously unknown mechanisms of Hippo regulation by heat shock, as well as physiological functions of YAP, in the heat stress response. Our observations also reveal a potential combinational therapy involving hyperthermia and targeting of the Hippo pathway.Luo et al. report that heat stress activates YAP to launch the heat shock transcriptome through inducing dephosphorylation and degradation of LATS independent of the upstream kinases MST and MAP4Ks.

Although a massive research has been devoted on the exploration of noble metal–based nanozyme, less progress has been made in the investigation of palladium (Pd) nanozyme and the interaction between ions and Pd nanozyme. In this study, a new type of Pd nanozyme was prepared by a facile one-pot approach by using carboxylated chitosan as the stabilizer. Owing to the synergistic effect of carboxylated chitosan stabilized Pd nanoparticles (CC-PdNPs) can effectively catalyze the H2O2-mediated oxidation of 3,3′,5,5′-tetramethylbenzidine sulfate (TMB) accompanied by a blue color change (oxidized TMB), indicating the peroxidase-like activity of CC-PdNPs. Furthermore, the Michaelis-Menten constants and catalytic stability of CC-PdNPs render them suitable for environmental analysis and bio-detection. Here, we found that while introducing the iodine ions (I−) into the reaction medium, the peroxidase-like activity of CC-PdNPs has been rapidly and effectively inhibited through the formation of Pd-I bond; thus, the active sites of PdNPs can be blocked by I−. Based on this specific inhibition by I−, a facile colorimetric assay has been performed for the detection of I− with an extremely low limit of detection (0.19 nM). Furthermore, the practicality of the proposed sensor also has been demonstrated in tap water, and the satisfactory recoveries were obtained. Our study not only demonstrated a novel Pd–based nanozyme but also provided guidance for I− sensing for environmental analysis, food inspection, and bio-detection.

Acute kidney injury (AKI) is commonly present in critically ill patients with sepsis. Early prediction of short-term reversibility of AKI is beneficial to risk stratification and clinical treatment decision. The study sought to use machine learning methods to discriminate between transient and persistent sepsis-associated AKI. Septic patients who developed AKI within the first 48 h after ICU admission were identified from the Medical Information Mart for Intensive Care III database. AKI was classified as transient or persistent according to the Acute Disease Quality Initiative workgroup consensus. Five prediction models using logistic regression, random forest, support vector machine, artificial neural network and extreme gradient boosting were constructed, and their performance was evaluated by out-of-sample testing. A simplified risk prediction model was also derived based on logistic regression and features selected by machine learning algorithms. A total of 5984 septic patients with AKI were included, 3805 (63.6%) of whom developed persistent AKI. The artificial neural network and logistic regression models achieved the highest area under the receiver operating characteristic curve (AUC) among the five machine learning models (0.76, 95% confidence interval [CI] 0.74–0.78). The simplified 14-variable model showed adequate discrimination, with the AUC being 0.76 (95% CI 0.73–0.78). At the optimal cutoff of 0.63, the sensitivity and specificity of the simplified model were 63% and 76% respectively. In conclusion, a machine learning-based simplified prediction model including routine clinical variables could be used to differentiate between transient and persistent AKI in critically ill septic patients. An easy-to-use risk calculator can promote its widespread application in daily clinical practice.