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To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC). This multicenter study incorporated 437 patients from five centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered significantly different. The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA. A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making. [ABSTRACT FROM AUTHOR]

To investigate the value of computed tomography (CT) radiomics nomogram in the preoperative prediction of perineural invasion (PNI) in oesophageal squamous cell carcinoma (ESCC) through a multicenter study. We retrospectively collected postoperative pathological data of 360 ESCC patients with definite PNI status (131 PNI-positive and 229 PNI-negative) from two centres. Radiomic features were extracted from the arterial-phase CT images, and the least absolute shrinkage and selection operator and logistic regression algorithm were used to screen valuable features for identifying the PNI status and calculating the radiomics score (Rad-score). A radiomics nomogram was established by integrating the Rad-score and clinical risk factors. A receiver operating characteristic curve was used to evaluate model performance, and decision curve analysis was used to evaluate the predictive performance of the radiomics nomogram in the training, internal validation, and external validation sets. Twenty radiomics features were extracted from a full-volume tumour region of interest to construct the model, and the radiomics nomogram combined with radiomics features and clinical risk factors was superior to the clinical and radiomics models in predicting the PNI status of ESCC patients. The area under the curve values of the radiomics nomogram in the training, internal validation, and external validation sets were 0.856 (0.794–0.918), 0.832 (0.742–0.922), and 0.803 (0.709–0.898), respectively. The radiomics nomogram based on CT has excellent predictive ability; it can non-invasively predict the preoperative PNI status of ESCC patients and provide a basis for preoperative decision-making. [ABSTRACT FROM AUTHOR]

With the extensive use of information technology, online learning has played an increasingly indispensable role in providing quality education. This study aimed to establish a learning effect model to identify the key factors in online education. Based on the system view, a conceptual model from environmental factors to learning performance was constructed from the perspective of learning interaction to study the internal mechanism of the impact of environmental factors on learning performance. An empirical study of 340 Chinese college students conducted showed that instructor supports have no significant direct impact on learning performance but indirectly impact through the intermediary role of learning interaction and self-regulated learning. Learning interaction and course design have a direct and indirect impact on learning performance. Various practical implications for educators to support their decisions are discussed and directions for further research are proposed. [ABSTRACT FROM AUTHOR]

This study aimed to determine the HIV-1 subtype distribution and HIV drug resistance (HIVDR) in patients with ART failure from 2014 to 2020 in Hainan, China. A 7-year cross-sectional study was conducted among HIV/AIDS patients with ART failure in Hainan. We used online subtyping tools and the maximum likelihood phylogenetic tree to confirm the HIV subtypes with pol sequences. Drug resistance mutations (DRMs) were analyzed using the Stanford University HIV Drug Resistance Database. A total of 307 HIV-infected patients with ART failure were included, and 241 available pol sequences were obtained. Among 241 patients, CRF01_AE accounted for 68.88%, followed by CRF07_BC (17.00%) and eight other subtypes (14.12%). The overall prevalence of HIVDR was 61.41%, and the HIVDR against non-nucleoside reverse transcriptase inhibitors (NNRTIs), nucleotide reverse transcriptase inhibitors (NRTIs), and protease inhibitors (PIs) were 59.75%, 45.64%, and 2.49%, respectively. Unemployed patients, hypoimmunity or opportunistic infections in individuals, and samples from 2017 to 2020 increased the odd ratios of HIVDR. Also, HIVDR was less likely to affect female patients. The common DRMs to NNRTIs were K103N (21.99%) and Y181C (20.33%), and M184V (28.21%) and K65R (19.09%) were the main DRMs against NRTIs. The present study highlights the HIV-1 subtype diversity in Hainan and the importance of HIVDR surveillance over a long period. [ABSTRACT FROM AUTHOR]

Prevalence of drug resistance (DR) challenges the epidemic control of human immunodeficiency virus (HIV)-1. However, little is known about DR among patients with antiretroviral therapy (ART) failure in Guangxi province, China. This cross-sectional study was aimed to investigate the prevalence of DR and the characteristics of DR sequences in the genetic transmission network among HIV-1 patients with ART failure in Guangxi. We enrolled 358 eligible patients between 2012 and 2018. Blood samples were subjected to reverse transcription polymerase chain reaction, followed by sequencing of the HIV-1 polymerase (pol) gene. An online subtyping tool and neighbor-joining phylogenetic tree were used to determine the genotype. HIV-TRACE tool was used to constructed transmission network with a pairwise genetic distance of 0.013. DR was analyzed using the Stanford University HIV Drug Resistance Database. We obtained 293 pol-sequences from participants; CRF01_AE (75.4%), CRF 08_BC (15.7%), and CRF07_BC (8.5%) were the main subtypes, and an A1 subtype was detected in Guangxi for the first time. The overall prevalence of DR was 32.4% (95/293). Among those with identified DR, 25.6% were against non-nucleoside analog reverse-transcriptase inhibitors (NNRTIs), 17.7% were against nucleoside analog reverse-transcriptase inhibitors (NRTIs), and 14.3% were against both NRTIs and NNRTIs. The common drug-resistant mutations were M184V (10.2%), K103N (10.6%) and V179D (6.1%). The patients located in the southern Guangxi [adjust odds ratio (AOR) = 10.87], or whose blood plasma were taken in 2017–2018 (AOR = 3.98) had an increased risk of DR. Of the CRF01_AE, CRF07_BC, and CRF08_BC sequences, 18.6%, 8.0%, and 13.0% fell into clusters, respectively. Nine (9.7%) sequences from patients with DR fell into three clusters. The largest cluster containing 11 individuals was the CRF01_AE subtype, 27.3% of whom were DR patients. Although the prevalence of DR among ART failure patients in Guangxi was at a low level, the continuous surveillance of DR in ART patients is necessary. [ABSTRACT FROM AUTHOR]

In this work, γ-Fe2O3/CNTs composite was facile synthesized by simple hydrothermal method and hydrazine hydrate treatment at low temperature (CNTs = carbon nanotubes). The result of VSM shows that the saturation magnetization of Fe2O3/CNTs composites increase from 0 to 1.29 emu g-1 after hydrazine hydrate treatment, indicating that the Fe2O3 in the composite transforms from a phase to γ phase. Moreover, the electrochemical properties of both a-Fe2O3/CNTs and γ-Fe2O3/CNTs was studied. Compared with a-Fe2O3/CNTs, γ-Fe2O3/CNTs shows enhanced electrochemical performance toward paracetamol determination. Under optimum condition, the γ-Fe2O3/CNTs modified electrode exhibits two linear ranges of 2.5 µM-385 µM and 593 µM-1550 µM and a low detection limit of 0.046 µM. In addition, the constructed sensor can be successfully utilized for detecting paracetamol in different water bodies. This work provides a new clue for the design and construction of excellent electrochemical sensor for paracetamol determination. [ABSTRACT FROM AUTHOR]

Rainwater chemistry plays an important role in the earth-surficial ecosystem, but studies on rainwater chemical composition of karst agro-ecosystem are rare. To explore the rainwater alkalization and the provenance of components responsible for neutralization, two-years chemical monitoring of rainwater was carried out in a karst agricultural catchment in Southwest China. The main findings suggest that SO 4 2−, NO 3 −, Ca2+, and NH 4 + are the principal ions. All the ionic contents show distinctly seasonal variation (highest in winter) in response to variations in seasonal precipitation because the rain-scour process can efficiently remove atmospheric materials. Source identification indicates that Cl− and Na+ are mainly derived from marine input whereas SO 4 2− and NO 3 − are controlled by anthropogenic emission, in particular, fixed emission sources. The source of NH 4 + is attributed to intense agricultural production, while Ca2+ and Mg2+ are mainly derived from calcite dissolution. The rainwater alkalization caused by the seasonal acid neutralization (via basic components, Ca2+ and NH 4 +) is beneficial to crop growth but also reflect agricultural overfertilization. Sulfur controlled the total wet acid deposition (68%–94%) and could be a potential agent of weathering. Image 1 • Two-year rainwater chemical data revealed the alkaline rain trend in SW China. • Alkaline trend is due to the seasonal acid neutralization in karst agriculture. • The sources of neutralization-related ions and controlling factors were explored. • This trend will benefit to ecosystem restoration but reflect overuse of fertilizer. [ABSTRACT FROM AUTHOR]

Without the auxiliary involvement of axial ligands and organic solvents, the ionic manganese porphyrin 1c immobilized in [BPy][BF4] efficiently catalyzed the oxidation of styrene and its derivatives under mild conditions, affording high activity/ oxide selectivity and good stability even after 5 recycling uses. [ABSTRACT FROM AUTHOR]