Artificial intelligence for gastric cancer in endoscopy: From diagnostic reasoning to market.

Recognition of gastric conditions during endoscopy exams, including gastric cancer, usually requires specialized training and a long learning curve. Besides that, the interobserver variability is frequently high due to the different morphological characteristics of the lesions and grades of mucosal inflammation. In this sense, artificial intelligence tools based on deep learning models have been developed to support physicians to detect, classify, and predict gastric lesions more efficiently. Even though a growing number of studies exists in the literature, there are multiple challenges to bring a model to practice in this field, such as the need for more robust validation studies and regulatory hurdles. Therefore, the aim of this review is to provide a comprehensive assessment of the current use of artificial intelligence applied to endoscopic imaging to evaluate gastric precancerous and cancerous lesions and the barriers to widespread implementation of this technology in clinical routine.

Morus alba L. Leaves - Integration of Their Transcriptome and Metabolomics Dataset: Investigating Potential Genes Involved in Flavonoid Biosynthesis at Different Harvest Times.

The mulberry leaf is a classic herb commonly used in traditional Chinese medicine. It has also been used as animal feed for livestock and its fruits have been made into a variety of food products. Traditionally, mulberry (Morus alba L.) leaf harvesting after frost is thought to have better medicinal properties, but the underlying mechanism remains largely unsolved. To elucidate the biological basis of mulberry leaves after frost, we first explored the content changes of various compounds in mulberry leaves at different harvest times. Significant enrichment of flavonoids was observed with a total of 224 differential metabolites after frost. Subsequently, we analyzed the transcriptomic data of mulberry leaves collected at different harvest times and successfully annotated 22,939 unigenes containing 1,695 new genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed 26, 20, and 59 unigenes related to flavonoids synthesis in three different groups harvested at different times. We found that the expression levels of flavonoid biosynthesis-related unigenes also increased when harvested at a delayed time, which was consistent with the flavonoid accumulation discovered by the metabolomic analysis. The results indicated that low temperature may be a key trigger in flavonoid biosynthesis of mulberry leaves by increasing the expression of flavonoid biosynthesis-related genes. This study also provided a theoretical basis for the optimal harvest time of mulberry leaves.

3DLRA: An RFID 3D Indoor Localization Method Based on Deep Learning.

As the core supporting technology of the Internet of Things, Radio Frequency Identification (RFID) technology is rapidly popularized in the fields of intelligent transportation, logistics management, industrial automation, and the like, and has great development potential due to its fast and efficient data collection ability. RFID technology is widely used in the field of indoor localization, in which three-dimensional location can obtain more real and specific target location information. Aiming at the existing three-dimensional location scheme based on RFID, this paper proposes a new three-dimensional localization method based on deep learning: combining RFID absolute location with relative location, analyzing the variation characteristics of the received signal strength (RSSI) and Phase, further mining data characteristics by deep learning, and applying the method to the smart library scene. The experimental results show that the method has a higher location accuracy and better system stability.

The Association between Metabolic Syndrome and Morbid Events in Type 2 Diabetes after a 7-Year Community Management: Beijing Community Diabetes Study 17.

BACKGROUND: To examine the association between morbid events and metabolic syndrome (MS) in patients with type 2 diabetes mellitus (T2DM). METHODS: A prospective, longitudinal, multicenter study was conducted at 13 community health centers associated with Beijing Tongren Hospital. From 2008 to 2015, there have been 3,525 T2DM patients being managed based on the Chinese guideline for T2DM. The morbid events included macrovascular events, diabetic kidney disease, ophthalmologic events, cancer, and all-cause death. RESULTS: At baseline, there were 2,708 people with MS and 817 without MS. After a seven-year management, there were 351 (12.96%) events in MS people and 74 (9.06%) events in people without MS (p = 0.003). The prevalence of macrovascular events (6.06%) was much higher in MS people than in people without MS (3.79%, p = 0.013). Cox regression analysis showed an association between MS and morbid events even after adjusting for confounding variables (adjusted hazard ratio = 1.44). MS was also associated with macrovascular events (adjusted hazard ratio = 1.96). The occurrence of morbid events and macrovascular events was increased when the numbers of metabolic abnormalities were 1, 2, 3, and 4 (p < 0.001). There was no continuously statistically significant difference in the cumulative prevalence of morbid events between patients with MS and patients without MS during the first five years. However, after six or seven years, the cumulative prevalence of morbid events in patients with MS was continuously significantly higher than that in patients without MS (11.00% vs. 8.20%, 12.96% vs. 9.06%, p < 0.05). CONCLUSIONS: T2DM with MS had higher incidence of morbid events, especially cardiovascular events, even after integrated management. The occurrence of morbid and macrovascular events increased as the number of metabolic abnormalities increased. MS was associated with increased risk of morbid events by 44% and macrovascular events by 96%. It would take at least six years to observe the association between MS and morbid events in T2DM.

The effects of AER and eGFR on outcomes of CVD in patients with T2DM in an urban community over 8 years of multifactorial treatment: the Beijing Communities Diabetes Study 18.

OBJECTIVE: It is well known that diabetic kidney disease is a risk factor for cardiovascular diseases (CVD) in patients with type 2 diabetes mellitus (T2DM). In this study, the effects of urine albumin excretion rate (AER) and estimated glomerular filtration rate (eGFR) on CVD outcomes were analyzed in a population of T2DM. METHODS: The study was carried out using recorded information of a cohort study. A total of 1,914 patients with T2DM with no prevalent CVD were enrolled in an 8 years prospective study and received multifactorial intervention. The risk of CVD outcomes was assessed according to chronic kidney disease staging, which was categorized using AER (mg/d) and eGFR (mL/min/1.73 m2). The effects of AER and eGFR on risk of CVD onset were also analyzed. RESULTS: During the follow-up period (median 6.8 years), 71 CVD events occurred. At baseline, those with AER ≥300 mg/d and coexisting eGFR 60-89 mL/min/1.73 m2 or <60 mL/min/1.73 m2 showed increased risk for CVD outcomes when compared with "no chronic kidney disease" (AER <30 mg/d and eGFR ≥90 mL/min/1.73 m2). The increased CVD risk was observed in patients who progressed to AER ≥30 mg/d during the follow-up period, whereas patients who progressed to eGFR <90 mL/min/1.73 m2 alone showed no increased CVD risk. During the follow-up period, after multifactorial intervention, 8.7% patients with microalbuminuria and 1.8% patients with overt nephropathy reversed to normoalbuminuria or microalbuminuria. CONCLUSION: AER is a more sensitive predictor than eGFR for CVD outcomes in T2DM patients. Overt nephropathy can be reversed after multifactorial intervention.

Sensitive detection of small molecules by competitive immunomagnetic-proximity ligation assay.

A novel detection method of small molecules, competitive immunomagnetic-proximity ligation assay (CIPLA), was developed and described in this report. Through the proximity effect caused by special proximity probes we prepared, small molecules can be detected using only one monoclonal antibody. CIPLA overcomes the obstacle that the proximity ligation assay (PLA) cannot be used in small molecular detection, as two antibodies are unable to combine to one small molecule due to its small molecular structure. Two small molecular compounds, clenbuterol (CLE) and ractopamine (RAC), were selected as targets for CIPLA. The limit of detection (LOD) reached 0.01 ng mL(-1), which was 10-50-fold lower than ELISA. With 5 orders of magnitude of the dynamic range achieved, the excellent sensitivity and broad dynamic range of CIPLA are noted. It can be applied widely in the sensitive detection of many other small molecular materials such as pesticides, additives in food, drugs, and biological samples, which have great significance in both theoretical and practical aspects.

Comparison of oligonucleotide-labeled antibody probe assays for prostate-specific antigen detection.

As a specific tumor marker, prostate-specific antigen (PSA) is widely used for the early diagnosis of prostate cancer. Sensitive and specific methods are required to improve the diagnostic accuracy of PSA detection. In the current study, we compared the immuno-polymerase chain reaction (immuno-PCR) method with the solid-phase proximity ligation assay (SP-PLA) with respect to the detection of PSA. Using oligonucleotide-labeled antibody probes, we used both immuno-PCR and SP-PLA to detect trace levels of PSA. The nucleic acid sequences can be monitored using real-time PCR. SP-PLA, however, was found to be superior in terms of both the detection limit and the dynamic range. To detect even lower levels of PSA, we used the loop-mediated isothermal amplification (LAMP) method to measure the levels of reporter DNA molecules in SP-PLA. The sensitivity of the LAMP method is 0.001 pM, which is approximately 100-fold higher than the sensitivities of the other assays. The results suggest that an SP-PLA- and LAMP-based protocol with oligonucleotide-labeled antibody probes may have great application in detecting PSA or other proteins present at trace levels.