Effect of participation of typical clinical pharmacists in patient medication in ICUs with different approaches: An investigation and case analysis.

Purpose: To investigate and study the impact on patient recovery of pharmacists' participation in the medication guidance and intervention of patients in intensive care units (ICUs). Patients and methods: We analyzed the drug use and typical cases of patients in the ICU of our hospital during clinical pharmacists' consultations or active monitoring from July 2019 to December 2020. This study included a total of 292 cases, which were distributed in the following eight areas: drug adjustment based on patients with hepatic and renal insufficiency,optimisation of treatment regimens based on drug interaction, identification and intervention based on adverse drug reactions, selection of blood purification methods based on drug intoxication, infusion access adjustment based on drug compatibility, drug adjustment based on genetic testing, antibiotic dose adjustment based on pharmacokinetics/pharmacodynamics (PK/PD) and others. The main observation indexes included consultation opinion efficiency rates and acceptance rates. The acceptance rate is calculated according to the acceptance of the clinical pharmacist's opinion by the competent doctor (fully accepted, partially accepted, not accepted). Calculate the efficiency rate according to the effect of the intervention measures. Definition of effectiveness: improvement of therapeutic effect of drugs; Avoid the occurrence of potential adverse drug reactions; Avoid the occurrence of potential drug compatibility reaction (for example, adjust the infusion sequence to avoid turbidity and sedimentation in the infusion pathway). Results: The results showed that clinical pharmacists participated in 292 cases of consultation or active monitoring of patients in the hospital's ICU based mainly on specific patient medications, drug interactions, adverse drug reactions, blood purification therapy, drug compatibility/venous infusion pathways, blood drug concentrations/drug-gene monitoring, drug PK/PD, etc. The doctor in charge acceptance rate of the consultation advice was 88.46% (completely accepted by 59.3%, partially accepted by 30.8%), and the effective rate of the consultation was 80.2%. Conclusion: Based on different purposes of intervention, the acceptance rate and efficiency rate were different. The efficiency rate with complete acceptance was significantly higher than that with partial acceptance or non-acceptance. The intervention opinions proposed by a comprehensive team in which pharmacists participate are of great help to patients and can reduce adverse reactions and medication risks. The higher the acceptance rate of their opinions, the higher the effective rate.

Room-Temperature Magnetism in 2D MnGa4 -H Induced by Hydrogen Insertion.

2D room-temperature magnetic materials are of great importance in future spintronic devices while only very few are reported. Herein, a plasma-enhanced chemical vapor deposition approach is exploited to construct the 2D room-temperature magnetic MnGa4 -H single crystal with a thickness down to 2.2 nm. The employment of H2 plasma makes hydrogen atoms can be easily inserted into the MnGa4 lattice to modulate the atomic distance and charge state, thereby ferrimagnetism can be achieved without destroying the structural configuration. The as-obtained 2D MnGa4 -H crystal is high-quality, air-stable, and thermo-stable, demonstrating robust and stable room-temperature magnetism with a high Curie temperature above 620 K. This work enriches the 2D room-temperature magnetic family and opens up the possibility for the development of spintronic devices based on 2D magnetic alloys.

A berberine induced disassembly towards zwitterionic surfactant as mimicking cell membrane for light-scattering sensing and logic devices.

A label-free light-scattering sensor for berberine determination was developed based on Gemini zwitterionic surfactant as logic devices. Amphiphilic phosphodiesters quaternary ammonium nanoparticles (PQANPs) with bionic phosphate ester structure were selected as a model for mimicking cell membrane. PQANPs self-assembled and formed the micelle structure, emitting strong light-scattering signal. Interestingly, the addition of berberine induced remarkable decrease of light-scattering attribute to its interfering behavior of PQANPs aggregation. Disassembly of PQANPs could be triggered due to electrostatic interaction and hydrophobic force between PQANPs and berberine. The berberine attached to the PQANPs surface and generated nanocomposites, resulting in significant reduce of light-scattering signal. Hence, it generated a strong light-scattering signal variation according to the change of the concentration of target. Our proposed light-scattering on-off sensor could be applied for berberine detection with detection limit of 27 nM. Moreover, a logic gate system was constructed based on PQANPs sensor with berberine and the interfering substances as the inputs and the light-scattering intensity as an output, which could hold great potential application in future clinical diagnosis and drug analysis.

Dual Self-Built Gating Boosts the Hydrogen Evolution Reaction.

Optimizing the intrinsic activity of active sites is an appealing strategy for accelerating the kinetics of the catalytic process. Here, a design principle, namely "dual self-built gating", is proposed to tailor the electronic structures of catalysts. Catalytic improvement is confirmed in a model catalyst with a ReS2 -WS2 /WS2 hybridized heterostructure. As demonstrated in experimental and theoretical results, the dual gating can bidirectionally guide electron transfer and redistribute at the interface, endowing the model catalyst with an electron-rich region. The tailored electronic structures balance the adsorption of intermediates and the desorption of hydrogen synergistically to enhance the reaction kinetics for the hydrogen evolution reaction. Interestingly, the effect of dual gating can be easily amplified by the electric field. The overpotential and Tafel slope (49 mV, 35 mV dec-1 ) obtained under the electric field for ReS2 -WS2 /WS2 catalyst with the dual self-built gating effect are far below than those (210 mV, 116 mV dec-1 ) of the pure WS2 catalyst, which exhibits nearly four times improvement. The concept of dual gating can be applied to more systems, offering a new guideline for designing advanced electrocatalysts.

Constrained parametric model for simultaneous inference of two cumulative incidence functions.

We propose a parametric regression model for the cumulative incidence functions (CIFs) commonly used for competing risks data. The model adopts a modified logistic model as the baseline CIF and a generalized odds-rate model for covariate effects, and it explicitly takes into account the constraint that a subject with any given prognostic factors should eventually fail from one of the causes such that the asymptotes of the CIFs should add up to one. This constraint intrinsically holds in a nonparametric analysis without covariates, but is easily overlooked in a semiparametric or parametric regression setting. We hence model the CIF from the primary cause assuming the generalized odds-rate transformation and the modified logistic function as the baseline CIF. Under the additivity constraint, the covariate effects on the competing cause are modeled by a function of the asymptote of the baseline distribution and the covariate effects on the primary cause. The inference procedure is straightforward by using the standard maximum likelihood theory. We demonstrate desirable finite-sample performance of our model by simulation studies in comparison with existing methods. Its practical utility is illustrated in an analysis of a breast cancer dataset to assess the treatment effect of tamoxifen, adjusting for age and initial pathological tumor size, on breast cancer recurrence that is subject to dependent censoring by second primary cancers and deaths.

Variations in discovery-based preeclampsia candidate genes.

Preeclampsia is a common and potentially lethal pregnancy disorder with lifelong increased risk of cardiovascular disease in survivors. Our prior global gene expression microarray analysis led to a novel set of 36 candidates in first trimester placentas of women who subsequently developed preeclampsia. In this report, we present preliminary studies demonstrating biomarkers of genotype and methylation variations in a subset of these candidate genes in maternal leukocyte and fetoplacental DNA of 28 case and 27 control dyads. We tested 84 single nucleotide polymorphisms (SNPs) using MassArray iPLEX and 50 CpG sites using EpiTYPER assays. Promising prediction modeling was identified with 25 SNPs selected using Fisher's exact tests (p ≤ 0.05) and 20 CpG sites selected on fold change. Genotype Distribution Analysis identified SNP variations that differed between nine paired cases versus paired controls. The findings validate the examined candidate genes and support feasibility of methods for further biomarker development. The integrative approach that was implemented begins to translate the 36 candidates toward clinical utility as a screening modality for preeclampsia.

Lung tissues in patients with systemic sclerosis have gene expression patterns unique to pulmonary fibrosis and pulmonary hypertension.

OBJECTIVE: Pulmonary complications, including pulmonary fibrosis (PF) and pulmonary arterial hypertension (PAH), are the leading cause of mortality in patients with systemic sclerosis (SSc). The aim of this study was to compare the molecular fingerprint of lung tissue and matching primary fibroblasts from patients with SSc with that of lung tissue and fibroblasts from normal donors, patients with idiopathic pulmonary fibrosis (IPF), and patients with idiopathic pulmonary arterial hypertension (IPAH). METHODS: Lung tissue samples were obtained from 33 patients with SSc who underwent lung transplantation. Tissues and cells from a subgroup of SSc patients with predominantly PF or PAH were compared to those from normal donors, patients with IPF, and patients with IPAH. Microarray data were analyzed using efficiency analysis for determination of the optimal data-processing methods. Real-time polymerase chain reaction and immunohistochemistry were used to confirm differential levels of messenger RNA and protein, respectively. RESULTS: Consensus efficiency analysis identified 242 and 335 genes that were differentially expressed in lungs and primary fibroblasts, respectively. SSc-PF and IPF lungs shared enriched functional groups in genes implicated in fibrosis, insulin-like growth factor signaling, and caveolin-mediated endocytosis. Gene functional groups shared by SSc-PAH and IPAH lungs included those involved in antigen presentation, chemokine activity, and interleukin-17 signaling. CONCLUSION: Using microarray analysis on carefully phenotyped SSc and comparator lung tissues, we demonstrated distinct molecular profiles in tissues and fibroblasts from patients with SSc-associated lung disease compared to idiopathic forms of lung disease. Unique molecular signatures were generated that are disease specific (SSc) and phenotype specific (PF versus PAH). These signatures provide new insights into the pathogenesis and potential therapeutic targets of SSc-related lung disease.

Determining the statistical significance of survivorship prediction models.

OBJECTIVES: The assessment of statistical significance of survivorship differences of model-predicted groups is an important step in survivorship studies. Some models determined to be significant using current methodologies are assumed to have predictive capabilities. These methods compare parameters from predicted classes, not random samples from homogenous populations, and they may be insensitive to prediction errors. Type I-like errors can result wherein models with high prediction error rates are accepted. We have developed and evaluated an alternate statistic for determining the significance of survivorship between or among model-derived survivorship classes. METHODS: We propose and evaluate a new statistical test, the F* test, which incorporates parameters that reflect prediction errors that are unobserved by the current methods of evaluation. RESULTS: We found that the Log Rank test identified fewer failed models than the F* test. When both the tests were significant, we found a more accurate model. Using two prediction models applied to eight datasets, we found that the F* test gave a correct inference five out of eight times, whereas the Log Rank test only identified one model out of the eight correctly. CONCLUSION: Our empirical evaluation reveals that the hypothesis testing inferences derived using the F* test exhibit better parity with the accuracy of prediction models than the other options. The generalizable prediction accuracy of prediction models should be of paramount importance for model-based survivorship prediction studies.

Clinical decision modeling system.

BACKGROUND: Decision analysis techniques can be applied in complex situations involving uncertainty and the consideration of multiple objectives. Classical decision modeling techniques require elicitation of too many parameter estimates and their conditional (joint) probabilities, and have not therefore been applied to the problem of identifying high-performance, cost-effective combinations of clinical options for diagnosis or treatments where many of the objectives are unknown or even unspecified. METHODS: We designed a Java-based software resource, the Clinical Decision Modeling System (CDMS), to implement Naïve Decision Modeling, and provide a use case based on published performance evaluation measures of various strategies for breast and lung cancer detection. Because cost estimates for many of the newer methods are not yet available, we assume equal cost. Our use case reveals numerous potentially high-performance combinations of clinical options for the detection of breast and lung cancer. RESULTS: Naïve Decision Modeling is a highly practical applied strategy which guides investigators through the process of establishing evidence-based integrative translational clinical research priorities. CDMS is not designed for clinical decision support. Inputs include performance evaluation measures and costs of various clinical options. The software finds trees with expected emergent performance characteristics and average cost per patient that meet stated filtering criteria. Key to the utility of the software is sophisticated graphical elements, including a tree browser, a receiver-operator characteristic surface plot, and a histogram of expected average cost per patient. The analysis pinpoints the potentially most relevant pairs of clinical options ('critical pairs') for which empirical estimates of conditional dependence may be critical. The assumption of independence can be tested with retrospective studies prior to the initiation of clinical trials designed to estimate clinical impact. High-performance combinations of clinical options may exist for breast and lung cancer detection. CONCLUSION: The software could be found useful in simplifying the objective-driven planning of complex integrative clinical studies without requiring a multi-attribute utility function, and it could lead to efficient integrative translational clinical study designs that move beyond simple pair wise competitive studies. Collaborators, who traditionally might compete to prioritize their own individual clinical options, can use the software as a common framework and guide to work together to produce increased understanding on the benefits of using alternative clinical combinations to affect strategic and cost-effective clinical workflows.