Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

MiR-125a has been characterized as a tumor suppressor in several cancers. However, the role of miR-125a in cervical cancer is unknown. In this study, we found the expression of miR-125a was downregulated in cervical cancer patients, and negatively correlated with the tumor size, FIGO stage, and preoperative metastasis. Kaplan-Meier analysis showed that miR-125a expression predicted favorable outcome for cervical cancer patients. Dual luciferase assays identified the STAT3 gene as a novel direct target of miR-125a. Functional studies showed that miR-125a overexpression significantly suppressed the growth, invasion and epithelial-mesenchymal transition (EMT) of cervical cancer cells both in vitro and in vivo via decreasing STAT3 expression. Moreover, miR-125a conferred to G2/M cell cycle arrest, accompanied by inhibition of several G2/M checkpoint proteins. Mechanistically, inactivation of miR-125a during cervical carcinogenesis was caused by HPV suppression of p53 expression. Clinically, STAT3, the expression of which, predicted poorer outcome, was inversely correlated with miR-125a in cervical cancer. These data highlight the importance of miR-125a in the cell proliferation and progression of cervical cancer, and indicate that miR-125a may be a useful therapeutic target for cervical cancer.

Background The purpose of this study was to explore the diagnostic value of soluble triggering receptor expressed on myeloid cells 1 (sTREM-1), procalcitonin (PCT), and C-reactive protein (CRP) serum levels for differentiating sepsis from SIRS, identifying new fever caused by bacteremia, and assessing prognosis when new fever occurred. Methods We enrolled 144 intensive care unit (ICU) patients: 60 with systemic inflammatory response syndrome (SIRS) and 84 with sepsis complicated by new fever at more than 48 h after ICU admission. Serum sTREM-1, PCT, and CRP levels were measured on the day of admission and at the occurrence of new fever (>38.3°C) during hospitalization. Based on the blood culture results, the patients were divided into a blood culture-positive bacteremia group (33 patients) and blood culture-negative group (51 patients). Based on 28-day survival, all patients, both blood culture-positive and -negative, were further divided into survivor and nonsurvivor groups. Results On ICU day 1, the sepsis group had higher serum sTREM-1, PCT, and CRP levels compared with the SIRS group ( P <0.05). The areas under the curve (AUC) for these indicators were 0.868 (95% CI, 0.798–0.938), 0.729 (95% CI, 0.637–0.821), and 0.679 (95% CI, 0.578–0.771), respectively. With 108.9 pg/ml as the cut-off point for serum sTREM-1, sensitivity was 0.83 and specificity was 0.81. There was no statistically significant difference in serum sTREM-1 or PCT levels between the blood culture-positive and -negative bacteremia groups with ICU-acquired new fever. However, the nonsurvivors in the blood culture-positive bacteremia group had higher levels of serum sTREM-1 and PCT ( P <0.05), with a prognostic AUC for serum sTREM-1 of 0.868 (95% CI, 0.740–0.997). Conclusions Serum sTREM-1, PCT, and CRP levels each have a role in the early diagnosis of sepsis. Serum sTREM-1, with the highest sensitivity and specificity of all indicators studied, is especially notable. sTREM-1, PCT, and CRP levels are of no use in determining new fever caused by bacteremia in ICU patients, but sTREM-1 levels reflect the prognosis of bacteremia. Trial registration ClinicalTrial.gov identifier NCT01410578

Mammalian target of rapamycin (mTOR) signaling pathways have been shown to be activated in thyroid cancer. Recent evidences have demonstrated that the antidiabetic agent metformin, an activator of 5'-AMP-activated protein kinase, can impair the proliferation and migration of cancer cells via inhibition of mTOR. However, the underlying mechanisms remain unclear. In this study, we show that metformin can inhibit mTOR pathway to impair growth and migration of the thyroid cancer cell lines. Cyclin D1 and c-Myc are important regulators of cancer cell growth, and we observed that treatment of thyroid cancer cells with metformin reduced c-Myc and cyclin D1 expression through suppression of mTOR and subsequent inhibition of P70S6K1 and 4E-BP1 phosphorylation. Metformin reduced epithelial to mesenchymal transition (EMT) in thyroid carcinoma cells. Moreover, metformin regulated expression of the EMT-related markers E-cadherin, N-cadherin, and Snail. Additionally, knockdown of TSC2, the upstream regulatory molecule of mTOR pathway, or treatment of rapamycin, the mTOR inhibitor, could abolish the effects of metformin to regulate thyroid cancer cell proliferation, migration, EMT, and mTOR pathway molecules. These results indicate that metformin can suppress the proliferation, migration, and EMT of thyroid cancer cell lines by inhibiting mTOR signaling. These findings suggest that metformin and its molecular targets may be useful in thyroid carcinoma therapy.

Cervical cancer (CC) is one of the most common malignancies in women. Paclitaxel is the front-line chemotherapeutic agent for treating CC. However, its therapeutic efficacy is limited because of chemoresistance, the mechanism of which remains poorly understood. Here, we used microRNA (miRNA) arrays to compare miRNA expression levels in the CC cell lines, HeLa and CaSki, with their paclitaxel resistance counterparts, HeLa/PR and CaSki/PR. We demonstrate that miR-125a was one of most significantly downregulated miRNAs in paclitaxel-resistant cells, which also acquired cisplatin resistance. And that the upregulation of miR-125a sensitized HeLa/PR and CaSki/PR cells to paclitaxel both in vitro and in vivo and to cisplatin in vitro. Moreover, we determined that miR-125a increased paclitaxel and cisplatin sensitivity by downregulating STAT3. MiR-125a enhanced paclitaxel and cisplatin sensitivity by promoting chemotherapy-induced apoptosis. Clinically, miR-125a expression was associated with an increased responsiveness to paclitaxel combined with cisplatin and a more favorable outcome. These data indicate that miR-125a may be a useful method to enable treatment of chemoresistant CC and may also provide a biomarker for predicting paclitaxel and cisplatin responsiveness in CC.

Pituitary stalk interruption syndrome (PSIS) is a rare congenital defect manifesting with varying degrees of pituitary hormone deficiency. The signs and symptoms of PSIS during the neonatal period and infancy are often overlooked and therefore diagnosis is delayed. The typical manifestations of PSIS can be detected by magnetic resonance imaging. Several genes in the Wnt, Notch and Shh signalling pathways related to hypothalamic-pituitary development, such as PIT1, PROP1, LHX3/LHX4, PROKR2, OTX2, TGIF and HESX1, have been found to be associated with PSIS. Nevertheless, the aetiology in the majority of cases still remains unknown. In the present review, we provide an overview of clinical features of PSIS and summarise our current understanding of the underlying pathogenic mechanisms for this rare syndrome. Furthermore, we propose future research directions that may help our understanding of the aetiology of PSIS.

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Abstract. Metastasis is a common event in cancer pathology, and represents the primary cause of cancer-associated mortality. Metastasis, which is the process in which cancer cells at the primary tumor site spread to a different location in the body and form a new tumor, is regulated by multiple factors and includes a number of steps and stages. In our previous study, it was demonstrated miR-339-5p inhibits cell migration and invasion in vitro and is associated with the tumor-node-metastasis stage and the lymph node metastasis status of non-small cell lung cancer. In the present study, expression of miR-339-5p was first determined in the tissues and peripheral blood of patients with non-small cell lung cancer (NSCLC) and in NSCLC cell lines. It was then demonstrated that miR-339-5p inhibits A549 and H1299 cell invasion. The underlying molecular events of miR-339-5p action in NSCLC were also explored. By luciferase assay and western blot analysis, B-cell CLL/lymphoma 6 (BCL6) was verified as the direct target gene of miR-339-5p. miR-339-5p may inhibit lung cancer cell invasion and migration by regulating the epithelial-to-mesenchymal transition via BCL6 in vitro. It was also demonstrated that the relative expression of miR-339-5p in the peripheral blood is associated with cancer metastasis in patients with non-small cell lung cancer.