Table_1_A novel nomogram model combining CT texture features and urine energy metabolism to differentiate single benign from malignant pulmonary nodule.docx

ObjectiveTo investigate a novel diagnostic model for benign and malignant pulmonary nodule diagnosis based on radiomic and clinical features, including urine energy metabolism index.MethodsA total of 107 pulmonary nodules were prospectively recruited and pathologically confirmed as malignant in 86 cases and benign in 21 cases. A chest CT scan and urine energy metabolism test were performed in all cases. A nomogram model was established in combination with radiomic and clinical features, including urine energy metabolism levels. The nomogram model was compared with the radiomic model and the clinical feature model alone to test its diagnostic validity, and receiver operating characteristic (ROC) curves were plotted to assess diagnostic validity.ResultsThe nomogram was established using a logistic regression algorithm to combine radiomic features and clinical characteristics including urine energy metabolism results. The predictive performance of the nomogram was evaluated using the area under the ROC and calibration curve, which showed the best performance, area under the curve (AUC) = 0.982, 95% CI = 0.940–1.000, compared to clinical and radiomic models in the testing cohort. The clinical benefit of the model was assessed using the decision curve analysis (DCA) and using the nomogram for benign and malignant pulmonary nodules, and preoperative prediction of benign and malignant pulmonary nodules using nomograms showed better clinical benefit.ConclusionThis study shows that a coupled model combining CT imaging features and clinical features (including urine energy metabolism) in combination with the nomogram model has higher diagnostic performance than the radiomic and clinical models alone, suggesting that the combination of both methods is more advantageous in identifying benign and malignant pulmonary nodules.</p

The flow chart of experimental process: (A, the four cue conditions; B, three target conditions; C, an example of the procedure).

<p>RT = reaction time.</p

Scatter-plots demonstrating correlations for MSLT versus overall RT in good sleeper controls (left panel) and primary insomnia patients (right panel).

<p>RT = reaction time; MSLT = multiple sleep latency test.</p

Descriptive data and daytime symptoms in GSCs, PIPs with EDS and PIPs without EDS.

<p>Female value is in %; other values are in mean ± SD;</p><p>Note: BMI = Body Mass Index; PSQI = Pittsburgh Sleep Quality Index; FSS = Flinders Fatigue Scale; BDI = Beck Depression Inventory-I; SAI = State Anxiety Inventory; TAI = Trait Anxiety Inventory.</p>a<p>Kruskal-Wallis Test.</p>b<p>Tukey Test;</p>c<p>PIPs without EDS vs. GCSs.</p>d<p>PIPs with EDS vs. GCSs.</p

Multiple Linear Regression Models predicting overall RT in primary insomnia patients.

<p>Note: RT = reaction time; BDI = Beck Depression Inventory-I; SAI = State Anxiety Inventory; MSLT = multiple sleep latency test.</p><p>Model 1: unadjusted;</p><p>Model 2: adjusted for gender, age and education years;</p><p>Model 3: adjusted for gender, age, education years, BDI and TAI;</p><p>Model 4: adjusted for gender, age, education years, BDI, TAI, sleep latency, total sleep time and sleep efficiency.</p

PSG sleep data in GSCs, PIPs with EDS and PIPs without EDS (mean ± SD).

<p>Note: SOL = sleep onset latency; TIB = time in bed; TST = total sleep time; WASO = wake time after sleep onset; SE = sleep efficiency; REM = rapid eye movement Latency; MA index = microarousal index;</p>a<p>Kruskal-Wallis Test.</p>b<p>Tukey Test.</p>c<p>PIPs without EDS vs. GCSs.</p>d<p>PIPs without EDS vs. PIPs with EDS.</p

Transdermal Cubic Phases of Metformin Hydrochloride: In Silico and in Vitro Studies of Delivery Mechanisms

Transdermal delivery is one of important controlled drug release strategies for drug development. Cubic phases are the assemblies of amphiphilic molecules in water with the hydrophilic–hydrophobic interpenetrating network for transdermal delivery of both hydrophilic and hydrophobic drugs. However, many details about the transdermal delivery of drugs from cubic phases remain unclear. Here, metformin hydrochloride (Met) cubic phases were prepared with glyceryl monooleate (GMO), ethanol, and water. The cubic structure was identified with the polarizing light microscopy and small-angle X-ray scattering method. Dissipative particle dynamics (DPD) was used for building the microstructures of the cubic phases to explore the mechanism of drug release that mainly depended on drug diffusion from the water channels of cubic phases in accordance with the Higuchi equation of in vitro release experiments. The coarse-grained model and molecular docking method showed that GMO could enhance drug permeation through the skin by disturbing the interaction between Met and the skin proteins, and increasing the fluidity of skin lipids, which was confirmed with the Fourier transform infrared spectroscopy, Langmuir monolayer, and immunohistochemistry. Furthermore, in vitro permeation experiments showed the high Met transdermal improvement of cubic phases. Cubic phases are an ideal transdermal delivery system of Met. In silico methods are very useful for analyzing the molecular mechanisms of transdermal formulations