Machine learning-enabled high-throughput industry screening of edible oils.

Long-term consumption of mixed fraudulent edible oils increases the risk of developing of chronic diseases which has been a threat to the public health globally. The complicated global supply-chain is making the industry malpractices had often gone undetected. In order to restore the confidence of consumers, traceability (and accountability) of every level in the supply chain is vital. In this work, we shown that machine learning (ML) assisted windowed spectroscopy (e.g., visible-band, infra-red band) produces high-throughput, non-destructive, and label-free authentication of edible oils (e.g., olive oils, sunflower oils), offers the feasibility for rapid analysis of large-scale industrial screening. We report achieving high-level of discriminant (AUC > 0.96) in the large-scale (n ≈ 11,500) of adulteration in olive oils. Notably, high clustering fidelity of 'spectral fingerprints' achieved created opportunity for (hypothesis-free) self-sustaining large database compilation which was never possible without machine learning. (137 words).

Development and validation of nomograms to predict clinical outcomes of preeclampsia.

Background: Preeclampsia (PE) is one of the most severe pregnancy-related diseases; however, there is still a lack of reliable biomarkers. In this study, we aimed to develop models for predicting early-onset PE, severe PE, and the gestation duration of patients with PE. Methods: Eligible patients with PE were enrolled and divided into a training (n = 253) and a validation (n = 108) cohort. Multivariate logistic and Cox models were used to identify factors associated with early-onset PE, severe PE, and the gestation duration of patients with PE. Based on significant factors, nomograms were developed and evaluated using the area under the curve (AUC) and a calibration curve. Results: In the training cohort, multiple gravidity experience (p = 0.005), lower albumin (ALB; p < 0.001), and higher lactate dehydrogenase (LDH; p < 0.001) were significantly associated with early-onset PE. Abortion history (p = 0.017), prolonged thrombin time (TT; p < 0.001), and higher aspartate aminotransferase (p = 0.002) and LDH (p = 0.003) were significantly associated with severe PE. Abortion history (p < 0.001), gemellary pregnancy (p < 0.001), prolonged TT (p < 0.001), higher mean platelet volume (p = 0.014) and LDH (p < 0.001), and lower ALB (p < 0.001) were significantly associated with shorter gestation duration. Three nomograms were developed and validated to predict the probability of early-onset PE, severe PE, and delivery time for each patient with PE. The AUC showed good predictive performance, and the calibration curve and decision curve analysis demonstrated clinical practicability. Conclusion: Based on the clinical features and peripheral blood laboratory indicators, we identified significant factors and developed models to predict early-onset PE, severe PE, and the gestation duration of pregnant women with PE, which could help clinicians assess the clinical outcomes early and design appropriate strategies for patients.

Golgi-protein 73 facilitates vimentin polymerization in hepatocellular carcinoma.

Golgi-protein 73 (GP73) is highly expressed in hepatocellular carcinoma (HCC) and, as a secretory protein, it has been proposed as a serum biomarker indicating progression of HCC. The underlying mechanism by which GP73 may promote HCC metastasis is still poorly understood. In this study, we discovered that GP73 interacted with vimentin to facilitate Serine/Threonine-protein phosphatase PP1-alpha (PP1A)-mediated dephosphorylation of vimentin at S56 and facilitated vimentin polymerization, which blocked vimentin degradation via TRIM56-mediated ubiquitin/proteasome-dependent pathway. Strikingly, Clomipramine, a 5-hydroxytryptamine receptor (5-HTR) agonist approved for the treatment of depression, impaired GP73-mediated vimentin polymerization to effectively inhibit metastasis of HCC with high GP73 expression, which provided a new strategy against HCC metastasis. Lastly, it was found that serum GP73 (sGP73) correlated positively with vimentin in primary tissues of HCC, suggesting that sGP73 might serve as a potential serum biomarker for companion diagnosis of HCC with highly expressed vimentin. In summary, this study reveals the process of GP73-mediated vimentin polymerization and proves that Clomipramine serves as a potential drug targeting vimentin for metastatic HCC patients with high sGP73 level.

DNA methylation regulator-mediated modification pattern defines tumor microenvironment immune infiltration landscape in colon cancer.

Emerging evidence implies a non-negligible role of DNA methylation in tumor immunity, however, its comprehensive impact on tumor microenvironment (TME) formation and immune activation remains unclear. In this study, we integrated 24 DNA methylation regulators among 754 colon cancer patients to distinguish different modification patterns via an unsupervised clustering method, and explore their TME immune characteristics. Three DNA methylation modification patterns with distinct prognosis and biological behaviors were identified, consistent with three known phenotypes of immune-inflamed, immune-excluded, and immune-desert. We then determined a DNA methylation gene signature and constructed a DNA methylation score (DMS) to quantify modification patterns individually through principal component analysis algorithms. DMS-low group had characteristics of specific molecular subtypes, including microsatellite instability, CpG island methylator phenotype positive, and mutant BRAF, presented by increased mutation burden, activation of DNA damage repair and immune-related pathways, highly TME immune cells infiltration, and hence, a preferable prognosis. Further, low DMS was also demonstrated to be correlated to better response and prolonged survival of anti-PD-L1 antibody, indicating that DMS could be considered as an effective predictive tool for immunotherapy. In conclusion, our work presented a landscape of different DNA methylation modification patterns, and their vital role in the formation of TME diversity and complexity, which could help to enhance understanding of TME immune infiltration characteristics and more importantly, guide immunotherapy strategies more effectively and personalized.

Arsenic trioxide induces proteasome dependent TBLR1-RARα degradation to improve leukemia eradication through cell differentiation enhancement.

BACKGROUND: Acute promyelocytic leukemia (APL) mainly harbors PML-RARα fusion gene, which is sensitive to all-trans retinoic acid (ATRA) and arsenic trioxide (ATO) treatment. However, APL harboring other RARα fusion genes exhibit different drug sensitivity. Here, we investigated the role and mechanism of TBLR1-RARα, a rare RARα fusion gene, on ATO treatment in leukemia cells. METHODS: By constructing two cell models of leukemia cell line HL-60 and U937 with overexpressed TBLR1-RARα, we detected the cell differentiation in the two cell models after ATO treatment by flow cytometry and Wright staining. Meanwhile, cell viability, colony formation and apoptosis were also determined after ATO treatment. RESULTS: We found that TBLR1-RARα enhanced ATO-induced apoptosis and cell proliferation inhibition. Besides, TBLR1-RARα also promoted ATO-induced cell differentiation. Furthermore, we found that the mitochondrial caspase pathway was involved in the apoptosis induced by ATO treatment in TBLR1-RARα positive leukemia cells. Moreover, ATO mediated TBLR1-RARα protein degradation via proteasome pathway, which accounts for the transcriptional activation of RARα target gene and is further involved in cell differentiation of TBLR1-RARα positive leukemia cells. CONCLUSIONS: Our study provides evidence that TBLR1-RARα positive APL patients may benefit from ATO treatment, thereby improving the appropriate management in TBLR1-RARα positive APL.

A novel joint index based on peripheral blood CD4+/CD8+ T cell ratio, albumin level, and monocyte count to determine the severity of major depressive disorder.

BACKGROUND: Inflammation and immune status are correlated with the severity of major depressive disorder (MDD).The purpose of this study was to establish an optimization model of peripheral blood parameters to predict the severity of MDD. METHODS: MDD severity in the training and validation cohorts (n = 99 and 97) was classified using the Hamilton Depression Scale, Thirty-eight healthy individuals as controls. Significant severity-associated factors were identified using a multivariate logistic model and combined to develop a joint index through binary logistic regression analysis. The area under the receiver operating characteristic curve (AUC) was used to identify the optimal model and evaluate the discriminative performance of the index. RESULTS: In the training cohort, lower CD4+/CD8+ T cell ratio, albumin level, and a higher monocyte percentage (M%) were significant as operating sociated with severe disease (P < 0.05 for all). The index was developed using these factors and calculated as CD4+/CD8+ T cell ratio, albumin level, and M%, with a sensitivity and specificity of 90 and 70%, respectively. The AUC values for the index in the training and validation cohorts were 0.85 and 0.75, respectively, indicating good discriminative performance. CONCLUSION: We identified disease severity-associated joint index that could be easily evaluated: CD4+/CD8+ T cell ratio, albumin level, and M%.

The NAO Variability Prediction and Forecasting with Multiple Time Scales Driven by ENSO Using Machine Learning Approaches.

Machine learning methods have now become an optional technique in Earth science research, and such data-driven solutions have also made tremendous progress in weather forecasting and climate prediction in recent years. Since climate data are typically time series, the neural network layers, which can identify the intrinsic connections between the points of the sequence and features in two-dimensional data, perform particularly well for climate prediction. The North Atlantic Oscillation (NAO) is a prominent atmospherical mode in the northern hemisphere, with the frequency change characteristic of sea level pressure (SLP) in the North Atlantic sector. One of the reasons why NAO prediction is still challenging is that NAO is also proven to be influenced by other climate circulations, the most significant of which is the interaction between El Niño-Southern Oscillation (ENSO) and NAO. Therefore, sea surface temperature (SST) in the Pacific Ocean used to characterize ENSO is also one of the factors that contribute to the evolution of NAO and can be used as an input factor to predict the NAO. In this paper, the seasonal lag correlation between ENSO and NAO is explored and analyzed. The interaction has been considered in both short-term forecasting and midterm prediction of the NAO variability. The monthly NAO index (NAOI) fluctuation is predicted using the Niño indices based on the RF-Var model, and the accuracy achieves 68% when the lead time is about three months. In addition, integrating multiple physical variables directly related to the NAO and Pacific SST, the short-term NAO forecasting is conducted using a multi-channel neural network named AccNet with trajectory gated recursive unit (TrajGRU) layer. AccNet has the ability to identify the mechanism of the high-frequency variation in several days, and the NAO variability is indicated by SLP. The loss function of AccNet is set to anomaly correlation coefficient (ACC), which is the indicator that verifies spatial correlation in geoscience. Forecasting extreme events of NAO between 2010 and 2021, AccNet presents higher flexibility compared against other structures that can capture spatial-temporal features.

Overexpression of Nicotinamide N-methyltransferase mainly covers stroma of colorectal cancer and correlates with unfavorable survival by its product 1-MNA.

Background: Accumulating evidence indicates that Nicotinamide N-methyltransferase (NNMT) is abnormally expressed in tumor tissues of several cancers including colorectal cancer (CRC) and associated with cancer progression. However, the distribution characteristics and the clinical value of each part of NNMT expression in CRC are still not fully understood. The purpose of this study is to determine the distribution of NNMT expression and its association with survival in CRC. Methods: By using the cancer genome atlas (TCGA) and clinical proteomic tumor analysis consortium (CPTAC), we firstly analyzed the difference of gene and protein levels of NNMT between CRC and normal colorectal tissue. Then, NNMT protein expressions were detected in 18 intraepithelial neoplastic samples and 177 CRC tumor samples through immunohistochemistry in our study cohort. Furthermore, the relationship between NNMT expression and clinicopathological characteristics, overall survival (OS) and disease-free survival (DFS) of CRC patients were analyzed by Pearson χ2 test and log-rank test, respectively, in public datasets and our study cohort. Lastly, the function of NNMT and its product 1-methyl-nicotinamide (1-MNA) on migration and invasion in colorectal cancer cells was analyzed by wound healing assay and transwell assay. Results: We determined that higher NNMT expression in CRC tissues than normal tissues in both gene and protein level in TCGA and CPTAC datasets (all p < 0.05). In addition, the strong relationships of NNMT expression with stromal cells were found in the TCGA cohort. Fortunately, our cohort could validate that the expression of NNMT in tumor stroma cell was significantly higher than that in tumor cell (p < 0.0001), and both of them were significantly higher than that in adjacent normal tissue (ANT) (p < 0.0001 and p < 0.0001, respectively). Furthermore, the positive NNMT expression in tumor cell and stromal cell were associated with series of unfavorable clinical characteristics, including advanced TNM stage, lymph node metastasis, distant metastasis (all p < 0.05). Also, higher NNMT was associated with unfavorable survival both in our study and public datasets, including TCGA and two Gene Expression Omnibus (GEO) datasets (GSE33113 and GSE17538). Moreover, the functional experiments showed that stromal cells with high NNMT expression can secret 1-MAN to promote migration and invasion of CRC cells in vitro. Conclusions: In CRC, NNMT is overexpressed in tumor cells and stroma cells, and then mainly expressed in tumor stroma cells. Overexpression of NNMT in tumor cell and stroma cell both are associated with metastasis and unfavorable survival. Besides, stromal cells with high NNMT expression secrets 1-MAN to promote migration and invasion of CRC cells. Therefore, NNMT may be a potential prognostic indicator in CRC patients.

Integrated analyses of m1A regulator-mediated modification patterns in tumor microenvironment-infiltrating immune cells in colon cancer.

Emerging evidence has revealed the crucial role of transcriptional RNA methyladenosine modification in immune response. However, the potential role of RNA N1-methyladenosine (m1A) modification of immune cells in the tumor microenvironment (TME) still remains unclear. In this study, we identified three distinct m1A modification patterns based on the integrated analyses of nine m1A regulators, which are significantly related to Relapse-free survival (RFS), Overall survival (OS), and TME infiltration cells in colon cancer patients. Furthermore, the m1AScore was generated by using principal components analysis (PCA) of expression of the 71 m1A-related genes to further demonstrate the characteristics of m1A patterns in colon cancer. In summary, a low m1AScore could be characterized by lower EMT, pan-F TBRS, and TNM stages, as well as less presence of lymphatic invasion, and, hence, good prognosis. At the same time, a low m1AScore could also be linked to CD8 + T effector proliferation, in addition to high microsatellite instability (MSI), neoantigen burden and PD-L1 expression, showing prolonged survival and better response after undergoing an anti-PD-L1 immunotherapy regimen in the public immunotherapy cohort. Our work reveals that m1A modification patterns play a key role in the formation of TME complexity and diversity in the context of immune cell infiltration. Accordingly, this m1AScore system provides an efficient method by which to identify and characterize TME immune cell infiltration, thereby allowing for more personalized and effective antitumor immunotherapy strategies.

Development and validation of an individualized nomogram for early prediction of the duration of SARS-CoV-2 shedding in COVID-19 patients with non-severe disease.

With the number of cases of coronavirus disease-2019 (COVID-19) increasing rapidly, the World Health Organization (WHO) has recommended that patients with mild or moderate symptoms could be released from quarantine without nucleic acid retesting, and self-isolate in the community. This may pose a potential virus transmission risk. We aimed to develop a nomogram to predict the duration of viral shedding for individual COVID-19 patients. This retrospective multicentric study enrolled 135 patients as a training cohort and 102 patients as a validation cohort. Significant factors associated with the duration of viral shedding were identified by multivariate Cox modeling in the training cohort and combined to develop a nomogram to predict the probability of viral shedding at 9, 13, 17, and 21 d after admission. The nomogram was validated in the validation cohort and evaluated by concordance index (C-index), area under the curve (AUC), and calibration curve. A higher absolute lymphocyte count (P=0.001) and lymphocyte-to-monocyte ratio (P=0.013) were correlated with a shorter duration of viral shedding, while a longer activated partial thromboplastin time (P=0.007) prolonged the viral shedding duration. The C-indices of the nomogram were 0.732 (95% confidence interval (CI): 0.685‒0.777) in the training cohort and 0.703 (95% CI: 0.642‒0.764) in the validation cohort. The AUC showed a good discriminative ability (training cohort: 0.879, 0.762, 0.738, and 0.715 for 9, 13, 17, and 21 d; validation cohort: 0.855, 0.758, 0.728, and 0.706 for 9, 13, 17, and 21 d), and calibration curves were consistent between outcomes and predictions in both cohorts. A predictive nomogram for viral shedding duration based on three easily accessible factors was developed to help estimate appropriate self-isolation time for patients with mild or moderate symptoms, and to control virus transmission.

Distinguishing between COVID-19 and influenza during the early stages by measurement of peripheral blood parameters.

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 infection. This study aims to examine the changes in peripheral blood parameters during the early stages of COVID-19 and influenza. We analyzed the peripheral blood parameters of 169 COVID-19 patients and 131 influenza patients during the early-onset stage. Results from the patients with COVID-19 were compared with those from healthy controls and influenza patients. In addition, results from patients with common and severe COVID-19 were further compared. There were significant differences between COVID-19 and influenza patients in terms of age, white blood cell count, platelet count, percentage of neutrophils, percentage of lymphocytes, percentage of monocytes, percentage of eosinophils, percentage of basophils, neutrophil, count and monocyte count. Two parameters (monocyte count and percentage of basophils) were combined to clarify the diagnostic efficacy of COVID-19 and influenza and the area under the curve was found to be 0.772. Comparison of peripheral blood parameters from common COVID-19, severe COVID-19, and influenza patients revealed many differences during the early disease stages. The diagnostic formula developed by this study will be of benefit for physicians in the differentiation of COVID-19 and influenza.

Development of nomograms to predict therapeutic response and prognosis of non-small cell lung cancer patients treated with anti-PD-1 antibody.

BACKGROUND: Anti-programmed death-1 (PD-1) antibody changed the treatment of non-small cell lung cancer (NSCLC), however, reliable predictive markers were lacking. We aimed to explore factors associated with response and survival, and develop predictive models. METHODS: This multicenter retrospective study included a training cohort (n = 92) and validation cohort (n = 111) with NSCLC patients received anti-PD-1 antibody monotherapy in eight Chinese hospitals, and a control cohort (n = 124) with NSCLC patients received chemotherapy. Logistic and Cox models were used to identify factors associated with response and survival respectively. Nomograms were developed based on significant factors, and evaluated by Concordance-index (C-index), area under the curve (AUC) and calibration curve. RESULT: In training cohort, smoking history (P = 0.027) and higher absolute lymphocyte count (P = 0.038) were associated with response. Female (P < 0.001), age ≥ 65 years (P = 0.004) and higher lactate dehydrogenase (LDH, P < 0.001) were associated with shorter progression-free survival (PFS). Higher LDH (P < 0.001) and derived neutrophil-to-lymphocyte ratio (P = 0.035) were associated with poorer overall survival (OS). While these factors were nonsignificant in chemotherapy cohort. Three nomograms to predict response at 6-week after treatment, PFS and OS at 6-, 12- and 18-months were developed, and validated in validation cohort. The C-indices of each nomogram in both cohorts were as follow (training vs validation): 0.706 vs 0.701; 0.728 vs 0.701; 0.741 vs 0.709; respectively. AUC showed a good discriminative ability. Calibration curves demonstrated a consistence between actual results and predictions. CONCLUSION: We developed predictive nomograms based on easily available factors to help clinicians early assess response and prognosis for NSCLC patients received anti-PD-1 antibody.

A nomogram to early predict isolation length for non-severe COVID-19 patients based on laboratory investigation: A multicenter retrospective study in Zhejiang Province, China.

BACKGROUND: Majority coronavirus disease 2019 (COVID-19) patients are classified as mild and moderate (non-severe) diseases. We aim to develop a model to predict isolation length for non-severe patients. METHODS: Among 188 non-severe patients, 96 patients were enrolled as training cohort to identify factors associated with isolation length via Cox regression model and develop a nomogram. Other 92 patients formed as validation cohort to validate nomogram. Concordance index (C-index), area under the curve (AUC) and calibration curves were used to evaluated nomogram. RESULTS: Increasing absolute eosinophil count (AEC) after admission was correlated with shorter isolation length (P = 0.02). Baseline activated partial thromboplastin time (APTT) > 30 s was correlated with longer isolation length (P = 0.03). A nomogram to predict isolation probability at 11-, 16- and 21-day was developed and validated. The C-indices of training and validation cohort were 0.604 and 0.682 respectively. Both cohorts showed a good discriminative ability (AUC, 11-day: 0.646 vs 0.730; 16-day: 0.663 vs 0.750; 21-day: 0.711 vs 0.783; respectively) and calibration power. CONCLUSIONS: Baseline APTT and dynamic change of AEC were two significant factors associated with isolation length of non-severe patients. Nomogram could predict isolation probability for each patient to estimate appropriate quarantine length.

Regulation of L-type Ca2+ Channel Activity and Insulin Secretion by Huntingtin-associated Protein 1.

Huntingtin-associated protein 1 (Hap1) was originally identified as a protein that binds to the Huntington disease protein, huntingtin. Growing evidence has shown that Hap1 participates in intracellular trafficking via its association with various microtubule-dependent transporters and organelles. Recent studies also revealed that Hap1 is involved in exocytosis such as insulin release from pancreatic ß-cells. However, the mechanism underlying the action of Hap1 on insulin release remains to be investigated. We found that Hap1 knock-out mice had a lower plasma basal insulin level than control mice. Using cultured pancreatic ß-cell lines, INS-1 cells, we confirmed that decreasing Hap1 reduces the number of secreted vesicles and inhibits vesicle exocytosis. Electrophysiology and imaging of intracellular Ca2+ measurements demonstrated that Hap1 depletion significantly reduces the influx of Ca2+ mediated by L-type Ca2+ channels (Cav). This decrease is not due to reduced expression of Cav1.2 channel mRNA but results from the decreased distribution of Cav1.2 on the plasma membrane of INS-1 cells. Fluorescence recovery after photobleaching showed a defective movement of Cav1.2 in Hap1 silencing INS-1 cells. Our findings suggest that Hap1 is important for insulin secretion of pancreatic ß-cells via regulating the intracellular trafficking and plasma membrane localization of Cav1.2, providing new insight into the mechanisms that regulate insulin release from pancreatic ß-cells.

1,2,3,4,6-penta-O-galloyl-ß-D-glucose protects PC12 Cells from MPP(+)-mediated cell death by inducing heme oxygenase-1 in an ERK- and Akt-dependent manner.

This study examined the ability of 1,2,3,4,6-penta-O-galloyl-ß-D-glucose (ß-PGG) to induce the expression of heme oxygenase-1 (HO-1) in the PC12 cells and its regulation in the PC12 cells. One week before treatment with the drug, nerve growth factor (NGF) was added to the cultures at a final concentration of 50 ng/mL to induce neuronal differentiation. After drug treatment, HO-1 gene transcription was analyzed by reverse transcription polymerase chain reaction (RT-PCR). Expression of HO-1 and NF-E2-related factor2 (Nrf2) and activation of extracellular signal-regulated kinase (ERK) and Akt were detected by Western blotting. The viability of the PC12 cells treated with different medicines was examined by MTT assay. The oxidative stress in the PC12 cells was evaluated qualitatively and quantitatively by DCFH-DA. The results showed that ß-PGG up-regulated HO-1 expression and this increased expression provided neuroprotection against MPP(+)-induced oxidative injury. Moreover, ß-PGG induced Nrf2 nuclear translocation, which was found to be upstream of ß-PGG-induced HO-1 expression, and the activation of ERK and Akt, a pathway that is involved in ß-PGG-induced Nrf2 nuclear translocation, HO-1 expression and neuroprotection. In conclusion, ß-PGG up-regulates HO-1 expression by stimulating Nrf2 nuclear translocation in an ERK- and Akt-dependent manner, and HO-1 expression by ß-PGG may provide the PC12 cells with an acquired antioxidant defense capacity to survive the oxidative stress.