Statistical methods for assessing treatment effects on ordinal outcomes and selecting optimal treatment on survival outcomes using observational data.

This dissertation consists of two projects investigating statistical methods in causal inference and personalized medication using observational data. In the first project, we propose a parametric marginal structural ordinal logistic regression model (MS-OLRM) to assess treatment effects on ordinal outcomes. Average treatment effect (ATE) is used to measure the difference of the mean outcomes if all patients would have been treated compared with the outcomes if they would not have been treated. Many statistical methods have been developed to estimate ATE when the outcome is continuous or binary. The methodology on assessing treatment effect for an ordinal outcome is less studied. For an ordinal outcome, the concept of mean may not be appropriate. For example, the difference in breast cancer between stage II versus stage I is quite different from that between stage IV versus stage III. For an ordinal outcome, we propose use superiority score to measure the treatment effect. Superiority score measures whether the outcome under treatment is stochastically larger than the outcome under control. We propose using the MSOLRM along with the inverse probability of treatment weighting (IPTW) to estimate the superiority score under treatment compared with control. This methodology adjusts confounding factors between treatment and outcome by using IPTW. In the weighted sample, all covariates become balanced among different treatment groups. Extensive simulation studies are carried out to examine the performance of the proposed method. We apply the proposed method to assess the treatment effects of medications and behavior therapies on patients’ recovery from alcohol use disorders using the Kentucky Medicaid 2012-2019 database. In the second project, we propose a doubly robust method for selecting optimal treatment regimen for survival outcome using observational data. In the proposed method, we apply the generalized partial linear single-index models (GPLSIMs) directly to model the contrast functions (i.e., the outcome difference between treatment and control). We consider the outcome under control as nuisance function, and we target to estimate the contrast functions using A-learning method and structural mean model. The optimal treatment regimen is defined as the treatment which results in the optimal outcome. The contrast functions can be consistently estimated if either the outcome model under control or the generalized propensity scores are correctly specified. When the outcome model under control is estimated using GPLSIM, the outcome model is less prone to mis-specification, which results in a more robust estimation for contrast functions and optimal treatment selection. Extensive simulation studies are carried out to examine the performance of the proposed method. The simulation results show the good performance of the proposed method. We apply the proposed method to select the optimal exercise level based on patients’ comorbidity and other characteristics using the National Health and Nutrition Examination Survey (NHANES) III data sets

Satellite-like CdS nanoparticles anchoring onto porous NiO nanoplates for enhanced visible-light photocatalytic properties.

Novel CdS/NiO nanocomposites assembled by satellite-like CdS nanoparticles anchoring onto porous NiO nanoplates have been fabricated by a step synthesis process, which involves a chemical bathing method followed by a heat treatment, and a microwave-assisted aqueous chemical reaction. The structure and photocatalytic properties of products were characterized by various techniques. More significantly, benefiting from the synergistic effect of CdS/NiO heterojunction, the as-prepared CdS/NiO architectures exhibited superior photocatalytic activity for decolorization of Congo red. The degradation rate on CdS/NiO nanocomposites achieves about 3.5 times higher than that of pure CdS nanocrystals under visible light irradiation for 30 min, suggesting a promising application in water purification.This work was supported by the Key Projects of Support Program for Outstanding Young Talents of Anhui Province (gxyqZD2016151), the Natural Science Foundation of Anhui Province (1808085MB40), the Program of Study Abroad for Excellent Young Scholar of Anhui Province (gxfxZD2016221), the Natural Science Foundation of Anhui Province Educational Committee (KJ2014ZD08, KJ2015A145), and the Special Foundation for Scientists of Hefei University (15CR06)

3D multilayered Bi4O5Br2 nanoshells displaying excellent visible light photocatalytic degradation behaviour for resorcinol.

High-ordered three-dimensional multilayered Bi4O5Br2 nanoshells have been fabricated successfully via a green ultrasound-assisted anion exchange reaction followed by a calcination treatment approach. The products are characterised by X-ray diffraction, field-emission scanning electron microscopy, transmission electron microscopy, high-resolution transmission electron microscopy, UV–vis diffuse reflectance spectrum and N2 adsorption/desorption isotherms. The results reveal that ternary Bi4O5Br2 nanoshells possess a pure monoclinic phase with the average thickness of ca. 12 nm, and the walls are of 10–12 layers constructed by nanograins with 10 nm in size. The specific surface is measured to be 36.18 m2 g-1 and the band gap energy E g value is calculated to be 2.52 eV. The possible formation process for Bi4O5Br2 nanoshells is simply proposed. According to the photocatalytic degradation for resorcinol under visible light irradiation, the as-prepared Bi4O5Br2 nanoshells exhibit excellent photocatalytic performance, which is not only far beyond the degradation rate of BiOBr precursor nanosheets but also superior to that of other reported Bi4O5Br2 architectures, suggesting a practical application for the treatment of organic pollutants.This work was supported by the Program of Study Abroad for Excellent Young Scholar of Anhui Province (gxfxZD2016221), the Key Projects of Support Program for Outstanding Young Talents of Anhui Province (gxyqZD2016151), the Natural Science Foundation of Anhui Province (1808085MB40), the Natural Science Foundation of Anhui Province Educational Committee (KJ2014ZD08, KJ2015A145), and the Special Foundation for Scientists of Hefei University (15CR06)

Facile template-free synthesis of hierarchically porous NiO hollow architectures with high-efficiency adsorptive removal of Congo red

Hierarchically porous NiO hollow architectures (HPHAs) were synthesized via a one-pot facile chemical bath deposition method and followed by a calcination process. The crystal structure, component and morphology of the products were characterized by various techniques. The results revealed that hierarchical architectures with hollow interior are composed of mesoporous NiO nanoflakes with thickness of about 8 nm. Interestingly, the as-synthesized NiO HPHAs have the unusual three-ordered porous features including a microscale hollow interior and two mesoscale pores which are attributed to the holes on the surface of nanoflakes with an average diameter of about 3.9 nm and the cavities on the wall of microsphere in the range of 20–40 nm in diameter formed by interconnecting nanoflakes. These comprehensive hierarchically porous structures are beneficial for the adsorption performance towards Congo red in water. The absorptive capacity over NiO HPHAs achieved about 1.8 and 4.0 times as high as that of the precursor β-Ni(OH)2 hollow microspheres (HSs) and the commercial activity carbon (AC) under the same conditions. The studies of adsorption kinetics illustrated that the adsorption behavior perfectly obeyed the pseudo-second-order model and the adsorption isotherm fits the Langmuir adsorption assumption well. The maximum adsorption capacities were calculated to be 490.2 mg g−1 according to the Langmuir equation, which is excellent result compared to NiO absorbents. The high-efficiency adsorption capacities for NiO HPHAs are attributed to the large specific surface area, the synergistic effect of micro-mesoporous structure and the electrostatic interaction of NiO with CR molecules. Additionally, NiO HPHAs can be easily renewed and has good chemical stability, indicating a great promising absorbent in the application for the removal of diazo organics in wastewater.Hefei Universit

Contents and colophon : Philological Studies of the Ainu Language 2

Alignment results of 5S gene NTS sequences from all kinds of groupers. a: The 266 bp NTS sequences of E. coioides, diploid hybrid and triploid hybrid; b: The 272 bp NTS sequences of E. coioides, diploid hybrid and triploid hybrid; c: The 275 bp NTS sequences of E. lanceolatus, diploid hybrid and triploid hybrid; d: The 284 bp NTS sequences of E. lanceolatus, diploid hybrid and triploid hybrid. The TATA sequences were framed in boxes. Dots indicated the identical nucleotides. In bold letters were shown the nucleotide substitutions. (TIF 2265 kb

TfR1 binding with H-ferritin nanocarrier achieves prognostic diagnosis and enhances the therapeutic efficacy in clinical gastric cancer

H-ferritin (HFn) nanocarrier is emerging as a promising theranostic platform for tumor diagnosis and therapy, which can specifically target tumor cells via binding transferrin receptor 1 (TfR1). This led us to investigate the therapeutic function of TfR1 in GC. The clinical significance of TfR1 was assessed in 178 GC tissues by using a magneto-HFn nanoparticle-based immunohistochemistry method. The therapeutic effects of doxorubicin-loaded HFn nanocarriers (HFn-Dox) were evaluated on TfR1-positive GC patient-derived xenograft (GC-PDX) models. The biological function of TfR1 was investigated through in vitro and in vivo assays. TfR1 was upregulated (73.03%) in GC tissues, and reversely correlated with patient outcome. TfR1-negative sorted cells exhibited tumor-initiating features, which enhanced tumor formation and migration/invasion, whereas TfR1-positive sorted cells showed significant proliferation ability. Knockout of TfR1 in GC cells also enhanced cell invasion. TfR1-deficient cells displayed immune escape by upregulating PD-L1, CXCL9, and CXCL10, when disposed with IFN-γ. Western blot results demonstrated that TfR1-knockout GC cells upregulated Akt and STAT3 signaling. Moreover, in TfR1-positive GC-PDX models, the HFn-Dox group significantly inhibited tumor growth, and increased mouse survival, compared with that of free-Dox group. TfR1 could be a potential prognostic and therapeutic biomarker for GC: (i) TfR1 reversely correlated with patient outcome, and its negative cells possessed tumor-aggressive features; (ii) TfR1-positive cells can be killed by HFn drug nanocarrier. Given the heterogeneity of GC, HFn drug nanocarrier combined with other therapies toward TfR1-negative cells (such as small molecules or immunotherapy) will be a new option for GC treatment

Prediction model of preeclampsia using machine learning based methods: a population based cohort study in China

IntroductionPreeclampsia is a disease with an unknown pathogenesis and is one of the leading causes of maternal and perinatal morbidity. At present, early identification of high-risk groups for preeclampsia and timely intervention with aspirin is an effective preventive method against preeclampsia. This study aims to develop a robust and effective preeclampsia prediction model with good performance by machine learning algorithms based on maternal characteristics, biophysical and biochemical markers at 11–13 + 6 weeks’ gestation, providing an effective tool for early screening and prediction of preeclampsia.MethodsThis study included 5116 singleton pregnant women who underwent PE screening and fetal aneuploidy from a prospective cohort longitudinal study in China. Maternal characteristics (such as maternal age, height, pre-pregnancy weight), past medical history, mean arterial pressure, uterine artery pulsatility index, pregnancy-associated plasma protein A, and placental growth factor were collected as the covariates for the preeclampsia prediction model. Five classification algorithms including Logistic Regression, Extra Trees Classifier, Voting Classifier, Gaussian Process Classifier and Stacking Classifier were applied for the prediction model development. Five-fold cross-validation with an 8:2 train-test split was applied for model validation.ResultsWe ultimately included 49 cases of preterm preeclampsia and 161 cases of term preeclampsia from the 4644 pregnant women data in the final analysis. Compared with other prediction algorithms, the AUC and detection rate at 10% FPR of the Voting Classifier algorithm showed better performance in the prediction of preterm preeclampsia (AUC=0.884, DR at 10%FPR=0.625) under all covariates included. However, its performance was similar to that of other model algorithms in all PE and term PE prediction. In the prediction of all preeclampsia, the contribution of PLGF was higher than PAPP-A (11.9% VS 8.7%), while the situation was opposite in the prediction of preterm preeclampsia (7.2% VS 16.5%). The performance for preeclampsia or preterm preeclampsia using machine learning algorithms was similar to that achieved by the fetal medicine foundation competing risk model under the same predictive factors (AUCs of 0.797 and 0.856 for PE and preterm PE, respectively).ConclusionsOur models provide an accessible tool for large-scale population screening and prediction of preeclampsia, which helps reduce the disease burden and improve maternal and fetal outcomes

Metabolomics analysis unveils important changes involved in the salt tolerance of Salicornia europaea

Salicornia europaea is one of the world’s salt-tolerant plant species and is recognized as a model plant for studying the metabolism and molecular mechanisms of halophytes under salinity. To investigate the metabolic responses to salinity stress in S. europaea, this study performed a widely targeted metabolomic analysis after analyzing the physiological characteristics of plants exposed to various NaCl treatments. S. europaea exhibited excellent salt tolerance and could withstand extremely high NaCl concentrations, while lower NaCl conditions (50 and 100 mM) significantly promoted growth by increasing tissue succulence and maintaining a relatively stable K+ concentration. A total of 552 metabolites were detected, 500 of which were differently accumulated, mainly consisting of lipids, organic acids, saccharides, alcohols, amino acids, flavonoids, phenolic acids, and alkaloids. Sucrose, glucose, p-proline, quercetin and its derivatives, and kaempferol derivatives represented core metabolites that are responsive to salinity stress. Glycolysis, flavone and flavonol biosynthesis, and phenylpropanoid biosynthesis were considered as the most important pathways responsible for salt stress response by increasing the osmotic tolerance and antioxidant activities. The high accumulation of some saccharides, flavonoids, and phenolic acids under 50 mM NaCl compared with 300 mM NaCl might contribute to the improved salt tolerance under the 50 mM NaCl treatment. Furthermore, quercetin, quercetin derivatives, and kaempferol derivatives showed varied change patterns in the roots and shoots, while coumaric, caffeic, and ferulic acids increased significantly in the roots, implying that the coping strategies in the shoots and roots varied under salinity stress. These findings lay the foundation for further analysis of the mechanism underlying the response of S. europaea to salinity

Novel paradigms for the gut–brain axis during alcohol withdrawal, withdrawal-associated depression, and craving in patients with alcohol use disorder

Introduction: Patients with alcohol use disorder (AUD) exhibit symptoms such as alcohol withdrawal, depression, and cravings. The gut-immune response may play a significant role in manifesting these specific symptoms associated with AUD. This study examined the role of gut dysfunction, proinflammatory cytokines, and hormones in characterizing AUD symptoms. Methods: Forty-eight AUD patients [men (n = 34) and women (n = 14)] aged 23–63 years were grouped using the Clinical Institute Withdrawal Assessment of Alcohol Scale (CIWA) as clinically significant (CS-CIWA [score > 10] [n = 22]) and a clinically not-significant group (NCS-CIWA [score ≤ 10] [n = 26]). Clinical data (CIWA, 90-day timeline followback [TLFB90], and lifetime drinking history [LTDH]) and blood samples (for testing proinflammatory cytokines, hormones, and markers of intestinal permeability) were analyzed. A subset of 16 AUD patients was assessed upon admission for their craving tendencies related to drug-seeking behavior using the Penn-Alcohol Craving Score (PACS). Results: CS-CIWA group patients exhibited unique and significantly higher levels of adiponectin and interleukin (IL)-6 compared to NCS-CIWA. In the CS group, there were significant and high effects of association for the withdrawal score with gut-immune markers (lipopolysaccharide [LPS], adiponectin, IL-6, and IL-8) and for withdrawal-associated depression with gut-immune markers (scored using MADRS with LPS, soluble cells of differentiation type 14 [sCD14], IL-6, and IL-8). Craving (assessed by PACS, the Penn-Alcohol Craving Scale) was significantly characterized by what could be described as gut dysregulation (LBP [lipopolysaccharide binding protein] and leptin) and candidate proinflammatory (IL-1β and TNF-α) markers. Such a pathway model describes the heavy drinking phenotype, HDD90 (heavy drinking days past 90 days), with even higher effects (R2 = 0.955, p = 0.006) in the AUD patients, who had higher ratings for cravings (PACS > 5). Discussion: The interaction of gut dysfunction cytokines involved in both inflammation and mediating activity constitutes a novel pathophysiological gut–brain axis for withdrawal symptoms and withdrawal-associated depression and craving symptoms in AUD. AUD patients with reported cravings show a significant characterization of the gut–brain axis response to heavy drinking. Trial registration: ClinicalTrials.gov, identifier: NCT# 00106106