Top Classes Research Articles

#1018 Profiles of medication use in dialysis patients globally

Abstract Background and Aims Global patterns of medication use in dialysis have not been defined so far. This project aims to provide a real-world view of medication use in dialysis and to characterize therapeutic classes and types of medications. An anonymized dialysis dataset of a global kidney care network called Apollo Dial DB was used. The most common prescribed medications administered at home by the patients were assessed. Method This is a multi-center, retrospective study using real-life data from adult dialysis patients captured in the first version of the Apollo Dial DB dataset. This dataset contains anonymized dialysis data (longitudinal observation level data) on more than 540,000 unique dialysis patients in 40 countries from Jan 2018-Mar 2021. Home medications were analyzed based on Anatomical Therapeutic Chemical (ATC) codes. Top (most common) 5 ATC codes for home medication are presented, once as 4-digit ATC code classes (e.g., A12A for Calcium) and once as full/exact ATC code (e.g., A12AA04 as 7-digit code for Calcium carbonate). Results In Apollo Dial DB 497,723 patients had 10,538,324 documented home medication entries (on average 21 medication prescriptions per patient outside the dialysis clinic), thereof 114,978 patients had 1,607,168 ATC code entries. Each ATC code entry represents a documented prescription of individual length. All percentages shown below are related to 1,607,168 ATC code entries (not to patients). The top 4-digit ATC code classes of home medication were Calcium (8.60%), Vitamin A and D, including combinations (7.33%), All other Therapeutic Products (6.84%), Antithrombotic Agents (5.89%) and Selective Calcium Channel Blockers with mainly vascular Effects (5.39%) (Fig. 1). Out of the ATC class Calcium, Calcium carbonate (4.89%) and Calcium acetate (3.22%) are listed as number 1 and 3 of the top 5 ‘full’ ATC codes. Alfacalcidol (3.36%) of ‘Vitamin A and D, including combinations’ is also number 2 of the ‘full’ ATC code list. Amlodipine (3.07%), number 4 of the full ATC code list, represents the majority of ‘Selective Calcium Channel Blockers with mainly vascular Effects’. Sevelamer of ‘All other Therapeutic Products’ is listed as number 5 in the ATC code list with 2.91% (Fig. 2). Conclusion The top 5 home ATC classes of medications account for 34% of all home prescriptions in dialysis patients. They largely represent phosphate binders, vitamin A & D, antithrombotics, and antihypertensives/antiarrhythmic medications. Although, outside the dialysis clinic each patient has on average 21 medication prescriptions, a lot of these prescriptions represent the major medication classes. It is worthwhile to mention the presented data are related to prescriptions and not to duration of use, e.g., both a medication prescribed for 6 months and a prescription for 1 week are documented only once, even if time on drug varies. Further research is needed to explore global patterns, including further ATC code groups particularly on a patient level, accounting for duration of use and medication formulations, real-world application of various drugs (e.g., GLP1 drugs, HIF PH inhibitor) and accounting for US data with NDC codes.

How rich were the rich? An empirically-based taxonomy of pre-industrial bases of wealth

The paper uses fifty-three social tables, ranging from Greece in 330 BCE to Mexico in 1940 to estimate the share and level of income of the top 1 % in pre-industrial societies. The share of the top 1 % covers a vast range from around 10 % to more than 40 % of society's income and does not always move together with the estimated Gini coefficient and the Inequality Extraction Ratio. I provide a taxonomy of pre-industrial societies based on the social class and type of assets (land, control of government, merchant capital, citizenship) that are associated with the top classes as well as lack of assets associated with poverty.

Neuropathological stage-dependent proteome mapping of the olfactory tract in Alzheimer's disease: From early olfactory-related omics signatures to computational repurposing of drug candidates.

Alzheimer's disease (AD) is the most common form of dementia, characterized by an early olfactory dysfunction, progressive memory loss, and behavioral deterioration. Albeit substantial progress has been made in characterizing AD-associated molecular and cellular events, there is an unmet clinical need for new therapies. In this study, olfactory tract proteotyping performed in controls and AD subjects (n = 17/group) showed a Braak stage-dependent proteostatic impairment accompanied by the progressive modulation of amyloid precursor protein and tau functional interactomes. To implement a computational repurposing of drug candidates with the capacity to reverse early AD-related olfactory omics signatures (OMSs), we generated a consensual OMSs database compiling differential omics datasets obtained by mass-spectrometry or RNA-sequencing derived from initial AD across the olfactory axis. Using the Connectivity Map-based drug repurposing approach, PKC, EGFR, Aurora kinase, Glycogen synthase kinase, and CDK inhibitors were the top pharmacologic classes capable to restore multiple OMSs, whereas compounds with targeted activity to inhibit PI3K, Insulin-like growth factor 1 (IGF-1), microtubules, and Polo-like kinase (PLK) represented a family of drugs with detrimental potential to induce olfactory AD-associated gene expression changes. To validate the potential therapeutic effects of the proposed drugs, in vitro assays were performed. These validation experiments revealed that pretreatment of human neuron-like SH-SY5Y cells with the EGFR inhibitor AG-1478 showed a neuroprotective effect against hydrogen peroxide-induced damage while the pretreatment with the Aurora kinase inhibitor Reversine reduced amyloid-beta (Aβ)-induced neurotoxicity. Taken together, our data pointed out that OMSs may be useful as substrates for drug repurposing to propose novel neuroprotective treatments against AD.

Two-Level Information-Retrieval-Based Model for Bug Localization Based on Bug Reports

Software bugs are a noteworthy concern for developers and maintainers. When a failure is detected late, it costs more to be fixed. To repair the bug that caused the software failure, the location of the bug must first be known. The process of finding the defective source code elements that led to the failure of the software is called bug localization. Effective approaches for automatically locating bugs using bug reports are highly desirable, as they would reduce bug-fixing time, consequently lowering software maintenance costs. With the increasing size and complexity of software projects, manual bug localization methods have become complex, challenging, and time-consuming tasks, which motivates research on automated bug localization techniques. This paper introduces a novel bug localization model, which works on two levels. The first level localizes the buggy classes using an information retrieval approach and it has two additional sub-phases, namely the class-level feature scoring phase and the class-level final score and ranking phase, which ranks the top buggy classes. The second level localizes the buggy methods inside these classes using an information retrieval approach and it has two sub-phases, which are the method-level feature scoring phase and the method-level final score and ranking phase, which ranks the top buggy methods inside the localized classes. A model is evaluated using an AspectJ dataset, and it can correctly localize and rank more than 350 classes and more than 136 methods. The evaluation results show that the proposed model outperforms several state-of-the-art approaches in terms of the mean reciprocal rank (MRR) metrics and the mean average precision (MAP) in class-level bug localization.

Drug-induced interstitial lung disease: a real-world pharmacovigilance study of the FDA Adverse Event Reporting System from 2004 to 2021.

Drug-induced interstitial lung disease (DILD) is an increasingly common cause of morbidity and mortality. However, due to the lack of specificity, DILD detection remains an unsolved public health challenge. For the first time, we aimed to examine DILD reports submitted to the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) to identify demographic characteristics and top drugs associated with DILD at a group level (including age, sex, drug class, and country stratification) and individual drug level. A retrospective analysis of the FAERS database was examined by disproportionality analysis. We reviewed the FAERS database from 2004 to 2021, using search terms 'interstitial lung disease' and sorting cases by generic drug name. The reporting odds ratio, proportional reporting ratio, and Bayesian confidence propagation neural network were calculated as the measure of strength of association. There were 32,821 DILD reports in the FAERS. After excluding reports without age, sex, or country data according to the specific measurement, the median age of patients was 68 (interquartile range: 59), 54.77% were male, and 46.00% of reports came from Japan. The top drug classes related to DILD in the FAERS were antineoplastic, followed by cardiovascular and antirheumatic agents, in varying order in different sexes. Fam-trastuzumab deruxtecan-nxki, ramucirumab, and eribulin were the top three drugs with the highest strength of association. We also found some drugs without DILD in the labels, such as amiodarone, temsirolimus, and ursodiol. There are significant differences in DILD reports in various countries. For example, the United States and France reported more cardiovascular agents, whereas Canada reported more antirheumatic agents. We found the top drugs and drug classes that were associated with DILD in the FAERS, which provides a real-world window for different ages, sexes, and countries to formulate precise pharmacovigilance policies.

Syntactic and Pragmatic Functions of Chinese-English Bilingual Children’s Code-Switching

Based on the bilingual children’s and adults’ code-switching (CS) dependency treebanks, this paper investigates the syntactic features and pragmatic functions of the Chinese-English bilingual children’s CS and compares them with bilingual adults’. It is mainly found that (1) As to the bilingual children, the mixed sentences present the longest mean sentence length (MSL), followed by those of the dominant language and the weak language. Similarly, Chinese-English adults’ mixed sentences present longer MSL than monolingual Chinese and English; (2) Subjects, objects, adverbials, and attributives are four major syntactic functions. Regarding bilingual children’s CS, objects are the most frequently switched dependency relations and subjects are the least. Differently, as to bilingual adults, attributives are most frequently switched, and subjects are the least. (3) Nouns, pronouns, determiners, adjectives, adverbs, and prepositions are the top word classes involved in four major syntactic relations; (4) The adverbial dependency relations present the longest mean dependency distance (MDD), and the attributives present the shortest for both bilingual children and adults; (5) The major causes that make different MDDs are the CS peripherality, the distributions of top word classes and adjacent dependency relations; (6) Six major pragmatic functions are performed by bilingual children and adults: filling lexical gaps, emphasis or expressing the intense feelings, explaining, giving “orders” or requirements, quotation, reiteration. The results syntactically and pragmatically suggest that there exist great similarities between bilingual children’s and adults’ code-switching.

Quality Assessment and Enhancement Strategies for Learning Environments in Top Classes of Kindergarten—A Reflection Based on the Perspective of School Readiness

Quality Assessment and Enhancement Strategies for Learning Environments in Top Classes of Kindergarten—A Reflection Based on the Perspective of School Readiness

The young and the old, the fast and the slow: a large-scale study of productivity classes and rank advancement

ABSTRACT We examined a large sample of Polish science, technology, engineering, mathematics, and medicine (STEMM) scientists (N = 16,083) to study rank advancement and productivity in the past 40 years. We used two previously neglected time dimensions – promotion age and promotion speed – to construct individual lifetime biographical and publication profiles. We followed a classificatory approach and the new methodological approach of journal prestige–normalized productivity. All scientists were allocated to different productivity, promotion age, and promotion speed classes (top 20%, middle 60%, and bottom 20%). The patterns found were consistent across all disciplines: scientists in young promotion age classes (and fast promotion speed classes) in the past were currently the most productive. In contrast, scientists in old promotion age classes (and slow promotion speed classes) in the past were currently the least productive. In the three largest disciplines, the young-old promotion age productivity differential for associate professors was 100–200% (150–200% for full professors), and the fast-slow promotion speed productivity differential for associate professors was 80–150% (100–170% for full professors). Our results were partly supported by a regression analysis in which we examined odds ratio estimates of belonging to top productivity classes. To examine the sample, we combined biographical and demographic data collected from the national register of all Polish scientists and publication metadata on all Polish articles indexed in Scopus (N = 935,167).

Comparative analysis of the intestinal microbiome in Rattus norvegicus from different geographies.

Rat species Rattus norvegicus, also known as the brown street rat, is the most abundant mammal after humans in urban areas, where they co-exist with humans and domestic animals. The reservoir role of R. norvegicus of zoonotic pathogens in cities among rodent-borne diseases that could endanger the lives of humans and other mammals. Therefore, understanding the normal microbiome of R. norvegicus is crucial for understanding and preventing zoonotic pathogen transmission to humans and animals. We investigated the intestinal microbiome of free-living R. norvegicus collected from the Ruili, Nujiang, and Lianhe regions of Yunnan, China, using 16S rRNA gene sequence analysis. Proteobacteria, followed by Firmicutes, and Bacteroidetes were abundant in the intestines of R. norvegicus; however, bacterial compositions varied significantly between samples from different locations. Following a similar trend, Gammaproteobacteria, Bacilli, and Clostridia were among the top bacterial classes in most intestinal samples. The situation differed slightly for the Lianhe and Nujiang samples, although Phyla Bacteroidota and Spirochaetota were most prevalent. The Alpha diversity, Chao1, and Simpson indexes revealed microbial richness among the R. norvegicus samples. A slight variation was observed among the samples collected from Ruili, Nujiang, and Lianhe. At species levels, several opportunistic and zoonotic bacterial pathogens, including Lactococcus garvieae, Uruburuella suis, Bartonella australis, Clostridium perfringens, Streptococcus azizii, Vibrio vulnificus, etc., were revealed in the R. norvegicus intestines, implying the need for a regular survey to monitor and control rodent populations. In conclusion, we explored diverse microbial communities in R. norvegicus intestines captured from different regions. Further, we identified several opportunistic and potential bacterial pathogens, which still need to be tested for their underlying pathogenesis. The findings of our current study should be considered a warning to the health authorities to implement rat control and surveillance strategies globally.

Causes of Drug-Induced Severe Cutaneous Adverse Reaction Epidermal Necrolysis (EN): An Analysis Using FDA Adverse Event Reporting System (FAERS) Database.

The purpose of the study is to analyze FAERS data to identify drugs associated with Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN), determine demographics, drug classes involved, most likely resulted in death, and highlight emerging trends in SJS/TEN reactions. We reviewed the publicly available FAERS database from 2004-2021. Using search terms "Stevens-Johnson syndrome" or "Toxic epidermal necrolysis", we identified the reports of SJS/TEN or SJS/TEN followed by death that might associated with specific drugs. Then the amounts and trends were counted analyzed. During the study period of 2004-2021, the Food and Drug Administration (FDA) received a total of 14,363,139 reports of adverse reactions, among which 24,976 were linked to SJS or TEN. After excluding the cases with incomplete or insufficient information on age, gender, or country of origin, the median median age of patients was 53.82 (IQR = 57.52), the females accounted for 56.59% (12,827 cases) and 8,507 (38.34%) originated in the United States. The top 50 drugs were associated with 15,149 cases (60.65%). The subsequent fatal outcome occurring in 4878 out of 24,976 cases (19.53%). Top 3 drug classes associated with SJS/TEN in FAERS were antiepileptics, non-steroidal anti-inflammatory drugs (NSAIDs) and others. Top drug classes associated with SJS/TEN deaths were antineoplastic agents and cephalosporins. Linear regression showed that the annual percentage of monoclonal antibody-related SJS/TEN reactions increased at an average rate of 0.25% (95% confidence interval: 0.18, 0.32) from 0.00% in 2004 to 4.79% in 2021, faster than any other drug class except antigout drug (allopurinol). By using the publicly available FAERS data, we have identified some important themes and trends in drug-related SJS/TEN reactions. Monoclonal antibodies and proton pump inhibitors are drugs with emerging trends causing SJS/TEN. Additionally, cephalosporin antibiotics have a higher mortality rate following SJS/TEN.

Identifying novel candidate compounds for therapeutic strategies in retinopathy of prematurity via computational drug-gene association analysis.

Retinopathy of prematurity (ROP) is the leading cause of preventable childhood blindness worldwide. Although interventions such as anti-VEGF and laser have high success rates in treating severe ROP, current treatment and preventative strategies still have their limitations. Thus, we aim to identify drugs and chemicals for ROP with comprehensive safety profiles and tolerability using a computational bioinformatics approach. We generated a list of genes associated with ROP to date by querying PubMed Gene which draws from animal models, human studies, and genomic studies in the NCBI database. Gene enrichment analysis was performed on the ROP gene list with the ToppGene program which draws from multiple drug-gene interaction databases to predict compounds with significant associations to the ROP gene list. Compounds with significant toxicities or without known clinical indications were filtered out from the final drug list. The NCBI query identified 47 ROP genes with pharmacologic annotations present in ToppGene. Enrichment analysis revealed multiple drugs and chemical compounds related to the ROP gene list. The top ten most significant compounds associated with ROP include ascorbic acid, simvastatin, acetylcysteine, niacin, castor oil, penicillamine, curcumin, losartan, capsaicin, and metformin. Antioxidants, NSAIDs, antihypertensives, and anti-diabetics are the most common top drug classes derived from this analysis, and many of these compounds have potential to be readily repurposed for ROP as new prevention and treatment strategies. This bioinformatics analysis creates an unbiased approach for drug discovery by identifying compounds associated to the known genes and pathways of ROP. While predictions from bioinformatic studies require preclinical/clinical studies to validate their results, this technique could certainly guide future investigations for pathologies like ROP.

Environmental Parameters Determine the Structure of Soil Bacteria under Different Land use Types in Tailings Area

Soil microorganisms contribute substantially to a wide range of services and thereby improve recovery in ecosystem restoration. However, there is relatively limited information on how microbial communities respond to different land-use types with similar plant species and their potential roles in supporting soil restoration in mine tailings. To understand the importance of the response of soil microbial communities to the stress of tailings area in different soil habitats, this article selected three different land use types (Tailings area, Buffer zone and Vegetable field) as the variables in the Yanzibian tailings area in Ningqiang, Shaanxi Province, China. Soil physicochemical properties and heavy metal concentrations of the different land use types were compared. Illumina MiSeq. 2500 Sequencing Technology was used to analyze the abundance and structural diversity of the microbial community in soil samples. The results showed that most of the soil samples were unsafe with multiple metals. All soil samples in the buffer zone and tailings ponds were acidic. With the acidity decreased, bacterial community richness and diversity increased significantly. Briefly, different environmental factors and soil microbial communities were significantly distinct across different land-use types. Cu, Zn, Pb, pH, MC (Moisture content), TN (Total nitrogen), TP (Total phosphorus), and TK (Total potassium) were essential factors affecting the abundance and structural diversity of soil microbial communities across three different land-use types. As a result, Actinobacteria, Alphaproteobacteria, and Gammaproteobacteria were the top dominant classes in the buffer zone and Vegetable field soils, while Sulfobacillia dominated in tailings pond soils. Further, we found that with the continuous decline of the pH, the dominant genera transferred from norank_f__norank_o__Vicinamibacterales, Bacillus and Nocardioides to the norank_f__norank_o__Gaiellales and norank_f__norank_o__Acidobacteriales, then to Sulfobacillus. These confirmed that the responses of microbiota to heavy metals stress varied in different land-use types. Together, this study provides important information on the occurrence and distribution of soil microbiomes in tailings areas and their potentially beneficial roles in soil restoration.

Compact Functional Testing for Neuromorphic Computing Circuits

We address the problem of testing Artificial Intelligence (AI) hardware accelerators implementing Spiking Neural Networks (SNNs). We define a metric to quickly rank available samples for training and testing based on their fault detection capability. The metric measures the inter-class spike count difference of a sample for the fault-free design. In particular, each sample is assigned a score equal to the spike count difference between the first two top classes. The hypothesis is that samples with small scores achieve high fault coverage because they are prone to misclassification, i.e., a small perturbation in the network due to a fault will result in these samples being misclassififed with high probability. We show that the proposed metric correlates with the per-sample fault coverage and that retaining a set of high-ranked samples in the order of ten achieves near perfect fault coverage for critical faults that affect the SNN accuracy. The proposed test generation approach is demonstrated on two SNNs modelled in Python and on actual neuromorphic hardware. We discuss fault modeling and perform an analysis to reduce the fault space so as to speed up test generation time.

Only a Few Classes Confusing: Pixel-Wise Candidate Labels Disambiguation for Foggy Scene Understanding

Not all semantics become confusing when deploying a semantic segmentation model for real-world scene understanding of adverse weather. The true semantics of most pixels have a high likelihood of appearing in the few top classes according to confidence ranking. In this paper, we replace the one-hot pseudo label with a candidate label set (CLS) that consists of only a few ambiguous classes and exploit its effects on self-training-based unsupervised domain adaptation. Specifically, we formulate the problem as a coarse-to-fine process. In the coarse-level process, adaptive CLS selection is proposed to pick a minimal set of confusing candidate labels based on the reliability of label predictions. Then, representation learning and label rectification are iteratively performed to facilitate feature clustering in an embedding space and to disambiguate the confusing semantics. Experimentally, our method outperforms the state-of-the-art methods on three realistic foggy benchmarks.

A Riemann-Hurwitz-Plücker formula

We prove a simultaneous generalization of the classical Riemann-Hurwitz and Plücker formulas, addressing the total number of inflection points of a morphism from a (smooth, projective) curve to an arbitrary (smooth, projective) higher-dimensional variety. Our definition of inflection point is relative to an algebraic family of divisors on the target variety, and our formula is obtained using the theory of localized top Chern classes. In assigning multiplicities to inflection points, we frequently have to consider excess degeneracy loci, but we are able to show nonetheless that the multiplicities are always nonnegative, and are positive under very mild hypotheses.

Analysis of commonly prescribed analgesics using

Environmental context Pharmaceuticals find their way to wastewater mainly through hospital and industrial effluents, and in turn affect all living organisms. The routine analysis of different water sources is tedious and of high cost. Our work presents a safe, low-cost method for analysing water samples to ensure proper cleanup of water and its suitability for human and animal use. Rationale Analgesics are one of the top classes of commonly prescribed drugs, and used over the counter. Therefore, they are most likely to be detected in wastewater samples coming from hospital and industrial effluents. Methodology This study focused on developing an in-silico UV spectroscopic manipulation of variant signal nature of low cost, using the methods of: advanced amplitude centring (AAC), mean centring of ratio spectra (MCR), successive derivative subtraction (SDS) and continuous wavelet transformation (CWT), for the determination of a ternary mixture of three analgesics: paracetamol (PCM), diclofenac (DCF) and ibuprofen (IBU) in water samples after sample cleanup using dispersive liquid–liquid microextraction (DLLME). Results The proposed methods were compared to those reported in terms of greenness, simplicity and effectiveness using the greenness assessment tools (Eco-scale & AGREE) and white analytical chemistry (WAC) tool. The AAC method showed the highest scores: an Eco-scale of 71, AGREE of 0.55 and RGB of 84.4 when compared to the reported methods. Discussion The AAC method was applied effectively for the study of surface water samples and industrial effluents with high accuracy and precision. Thus, real water samples could be routinely analysed with minimal cost to ensure proper cleanup of water and its suitability for human and animal use.

Thrombin as a Potential Proxy to Select for Horn Fly Abundance in Beef Cattle.

Horn flies are a major nuisance to cattle and induce significant economic losses. Fly abundance varies within and across breeds and genetic analyses have shown sufficient genetic variation to permit selection. A major bottleneck for selecting against horn fly abundance is the complexity of measuring fly attraction phenotypes. Easy-to-measure proxy phenotypes could be an attractive option to indirectly estimate fly abundance. In the current study, thrombin was investigated as a potential proxy to assess fly abundance. Fly counts and blood samples were collected on 355 cows. Pearson correlation between subjective fly count and thrombin was -0.13, indicating a decrease in fly abundance with the increase in thrombin concentration. When thrombin was discretized into three classes, there was a 22% difference in fly count between the top and bottom classes. Heritability estimates of thrombin were 0.38 and 0.39 using linear and threshold models, respectively. The correlation between estimated thrombin breeding values and fly count was around -0.18. There was a noticeably lower density of high fly counts among animals with high breeding values for thrombin. These results indicate that thrombin could be used in combination with other biological factors to estimate fly abundance and as a proxy for selection against fly abundance.

Integrative Transcriptomic and Phytohormonal Analyses Provide Insights into the Cold Injury Recovery Mechanisms of Tea Leaves.

Tea plant is susceptible to low temperature, while the cold injury recovery mechanisms of tea leaves are still unclear. Windbreak has an effective and gradient range of protecting tea plants. Tea plants with increasing cold damage degree have varying recovery status accordingly, which are the ideal objects for investigating the cold injury recovery mechanisms of tea leaves. Here, we investigated the transcriptome and phytohormone profiles of tea leaves with different cold injury degrees in recovery (adjacent to the windbreak), and the levels of chlorophylls, malondialdehyde, major phytohormones as well as the activities of peroxidase (POD) and superoxide dismutase (SOD) were also measured. The results showed the content of total chlorophylls and the activity of POD in mature tea leaves gradually decreased with the distance to windbreak, while SOD showed the opposite. The major phytohormones were highly accumulated in the moderately cold-injured tea leaves. The biosynthesis of abscisic acid (ABA) was enhanced in the moderate cold damaged tea leaves, suggesting that ABA plays an important role in the cold response and resistance of tea plants. The transcriptomic result showed that the samples in different rows were well discriminated, and the pathways of plant-pathogen interaction and flavonoid biosynthesis were enriched based on KEGG analysis. WRKY, GRAS and NAC were the top classes of transcription factors differentially expressed in the different cold-injured tea leaves. Thus, windbreak is effective to protect adjacent tea plants from cold wave, and phytohormones importantly participate in the cold injury recovery of tea leaves.

Prioritization of god class design smell: A multi-criteria based approach

Context: Design smell Prioritization is a significant activity that tunes the process of software quality enhancement and raises its life cycle. Objective: A multi-criteria merge strategy for Design Smell prioritization is described. The strategy is exemplified with the case of God Class Design Smell. Method: An empirical adjustment of the strategy is performed using a dataset of 24 open source projects. Empirical evaluation was conducted in order to check how is the top ranked God Classes obtained by the proposed technique compared against the top ranked God class according to the opinion of developers involved in each of the projects in the dataset. Results: Results of the evaluation show the strategy should be improved. Analysis of the differences between projects where respondents answer correlates with the strategy and those projects where there is no correlation should be done.

Should we consider panel-based pharmacogenetic testing in gynecologic oncology patients? (224)

Objectives: Pharmacogenetic (PGx) testing identifies variations in genes that help predict efficacy, risk of side effects, and appropriate doses of certain medications. Evidence-based guidelines from the Clinical Pharmacogenetic Implementation Consortium (CPIC) facilitate the application of PGx testing in clinical practice. Antiemetics, NSAIDs, and opioids, which are metabolized via cytochrome p450 enzymes, are commonly prescribed medications for gynecologic oncology patients. This study aimed to evaluate the prescribing rate for medications impacted by PGx variants by gynecologic oncologists. Methods: The study design was a cross-sectional retrospective review. The electronic medical record was queried for unique prescriptions orders placed by gynecologic oncologists at a single academic institution for CPIC level A/B medications between July 15, 2018- July 14, 2021. The frequencies of actionable phenotypes for PGx genes were estimated using the European phenotype frequencies provided by CPIC. The projected prevalence of drug-gene interactions (DGI) was estimated by multiplying prescription drug counts by phenotype frequencies. Results: A total of 10,624 patients were treated by the gynecologic oncology service and 86% were ordered at least one CPIC level A medication. The top five CPIC level A medications were ondansetron (n=6807, 64%), ibuprofen (n=5815, 55%), omeprazole (n=1929, 18%), tramadol (n=1450, 14%), and lansoprazole (n=1395, 13%). The top drug classes impacted by PGx were antiemetics (64%), NSAIDs (63%), proton pump inhibitors (PPIs) (44%) and opioids (14%). DGIs projected to have clinical consequence include: ibuprofen/CYP2C9 (n=989, 9%), ondansetron/CYP2D6 (n=204, 2%), omeprazole/CYP2C19 (n=136, 1%), lansoprazole/CYP2C19 (n=98, 1%) and tramadol/CYP2D6 (n=87, 1%). Additionally, 1015 patients with genetic results were identified in the Penn Medicine BioBank for further clinical follow-up to assess efficacy and adverse drug reactions. Conclusions: CPIC level A medications are commonly prescribed for gynecologic oncology patients. Implementation of multi-gene panels containing CYP2D6, CYP2C9, and CYP2C19 for the guidance of commonly prescribed antiemetics, analgesics, and PPIs would be of greatest benefit in this population. Further research is necessary to evaluate side effects and drug responses for patients with PGx variants in this patient population. Objectives: Pharmacogenetic (PGx) testing identifies variations in genes that help predict efficacy, risk of side effects, and appropriate doses of certain medications. Evidence-based guidelines from the Clinical Pharmacogenetic Implementation Consortium (CPIC) facilitate the application of PGx testing in clinical practice. Antiemetics, NSAIDs, and opioids, which are metabolized via cytochrome p450 enzymes, are commonly prescribed medications for gynecologic oncology patients. This study aimed to evaluate the prescribing rate for medications impacted by PGx variants by gynecologic oncologists. Methods: The study design was a cross-sectional retrospective review. The electronic medical record was queried for unique prescriptions orders placed by gynecologic oncologists at a single academic institution for CPIC level A/B medications between July 15, 2018- July 14, 2021. The frequencies of actionable phenotypes for PGx genes were estimated using the European phenotype frequencies provided by CPIC. The projected prevalence of drug-gene interactions (DGI) was estimated by multiplying prescription drug counts by phenotype frequencies. Results: A total of 10,624 patients were treated by the gynecologic oncology service and 86% were ordered at least one CPIC level A medication. The top five CPIC level A medications were ondansetron (n=6807, 64%), ibuprofen (n=5815, 55%), omeprazole (n=1929, 18%), tramadol (n=1450, 14%), and lansoprazole (n=1395, 13%). The top drug classes impacted by PGx were antiemetics (64%), NSAIDs (63%), proton pump inhibitors (PPIs) (44%) and opioids (14%). DGIs projected to have clinical consequence include: ibuprofen/CYP2C9 (n=989, 9%), ondansetron/CYP2D6 (n=204, 2%), omeprazole/CYP2C19 (n=136, 1%), lansoprazole/CYP2C19 (n=98, 1%) and tramadol/CYP2D6 (n=87, 1%). Additionally, 1015 patients with genetic results were identified in the Penn Medicine BioBank for further clinical follow-up to assess efficacy and adverse drug reactions. Conclusions: CPIC level A medications are commonly prescribed for gynecologic oncology patients. Implementation of multi-gene panels containing CYP2D6, CYP2C9, and CYP2C19 for the guidance of commonly prescribed antiemetics, analgesics, and PPIs would be of greatest benefit in this population. Further research is necessary to evaluate side effects and drug responses for patients with PGx variants in this patient population.