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Objective: Preeclampsia (PE) is the leading cause of maternal death worldwide and is associated with long-term morbidity in both mothers and newborns. Animal modeling is considered a functional source for understanding PE pathogenesis, diagnostic standards, and therapeutic approaches. Materials and Methods: This study aimed to demonstrate and evaluate the use of N-nitro-L-arginine methyl ester (L-NAME) in a Wistar rat model under conditions similar to PE. A total of 12 rats were divided into 4 groups, each consisting of 3 members, including the pregnant control group and treatment groups administered low-dose (PE 25 mg/kg L-NAME/day), medium-dose (PE 50 mg/kg L-NAME/day), and high-dose L-NAME (PE 75 mg/kg L-NAME/day) L-NAME from gestational day 4 to 19. Measurements included blood pressure, creatinine, and proteinuria levels, placental histological changes, and placental tissue hypoxia-inducible factor 1-alpha, and plasma endothelial nitric oxide synthase levels. Results: The results showed that intervention with L-NAME at 75 mg/kg body weight/day (PE3) induced PE earlier than that with 50 mg/kg body weight/day L-NAME. Conclusion: The model conditions also support further research into PE pathogenesis.

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In the present study, we investigated whether curcumin administration would interfere with the main renal features of l-NAME-induced hypertension model. For this purpose, we conducted both in vitro and in vivo experiments to evaluate renal indicators of inflammation, oxidative stress, and metalloproteinases (MMPs) expression/activity. Hypertension was induced by l-NAME (70 mg/kg/day), and Wistar rats from both control and hypertensive groups were treated with curcumin (50 or 100 mg/kg/day; gavage) or vehicle for 14 days. Blood and kidneys were collected to determine serum creatinine levels, histological alterations, oxidative stress, MMPs expression and activity, and ED1 expression. l-NAME increased blood pressure, but both doses of curcumin treatment reduced these values. l-NAME treatment increased creatinine levels, glomeruli area, Bowman's space, kidney MMP-2 activity, as well as MMP-9 and ED1 expression, and reduced the number of glomeruli. Curcumin treatment prevented the increase in creatinine levels, MMP-2 activity, and reduced MMP-2, MMP-9, ED1, and superoxide levels, as well as increased superoxide dismutase activity and partially prevented glomeruli alterations. Moreover, curcumin directly inhibited MMP-2 activity in vitro. Thus, our main findings demonstrate that curcumin reduced l-NAME-induced hypertension and renal glomerular alterations, inhibited MMP-2 and MMP-9 expression/activity, and reduced oxidative stress and inflammatory processes, which may indirectly impact hypertension-induced renal outcomes.

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The cardioprotective effect of ischemic preconditioning (IPC) and ischemic postconditioning (IPoC) in adult hearts is mediated by nitric oxide (NO). During the early developmental period, rat hearts exhibit higher resistance to ischemia-reperfusion (I/R) injury, contain higher levels of serum nitrates, and their resistance cannot be further increased by IPC or IPoC. NOS blocker (L-NAME) lowers their high resistance. Wistar rat hearts (postnatal Days 1 and 10) were perfused according to Langendorff and exposed to 40 min of global ischemia followed by reperfusion with or without IPoC. NO and reactive oxygen species donors (DEA-NONO, SIN-1) and L-NAME were administered. Tolerance to ischemia decreased between Days 1 and 10. DEA-NONO (low concentrations) significantly increased tolerance to I/R injury on both Days 1 and 10. SIN-1 increased tolerance to I/R injury on Day 10, but not on Day 1. L-NAME significantly reduced resistance to I/R injury on Day 1, but actually increased resistance to I/R injury on Day 10. Cardioprotection by IPoC on Day 10 was not affected by either NO donors or L-NAME. It can be concluded that resistance of the neonatal heart to I/R injury is NO dependent, but unlike in adult hearts, cardioprotective interventions, such as IPoC, are most likely NO independent.

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Clinical notes contain valuable information for research and monitoring quality of care. Named Entity Recognition (NER) is the process for identifying relevant pieces of information such as diagnoses, treatments, side effects, etc., and bring them to a more structured form. Although recent advancements in deep learning have facilitated automated recognition, particularly in English, NER can still be challenging due to limited specialized training data. This exacerbated in hospital settings where annotations are costly to obtain without appropriate incentives and often dependent on local specificities. In this work, we study whether this annotation process can be effectively accelerated by combining two practical strategies. First, we convert usually passive annotation tasks into a proactive contest to motivate human annotators in performing a task often considered tedious and time-consuming. Second, we provide pre-annotations for the participants to evaluate how recall and precision of the pre-annotations can boost or deteriorate annotation performance. We applied both strategies to a text de-identification task on French clinical notes and discharge summaries at a large Swiss university hospital. Our results show that proactive contest and average quality pre-annotations can significantly speed up annotation time and increase annotation quality, enabling us to develop a text de-identification model for French clinical notes with high performance (F1 score 0.94).

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Dispensing errors pose a significant health risk, with drug name similarity being a potential contributory factor. To determine the impact of drug name similarity on dispensing errors within clinical settings, we analyzed 563 dispensing errors at an acute hospital in Japan from April 2015 to June 2018. Drug name similarity between two drugs was classified into Name-Similar and Name-Dissimilar groups using the m2-vwhtfrag index, the value of the drug name similarity. Drug efficacy similarity was categorized into Efficacy-Same, Efficacy-Close, and Efficacy-Far. The drug name similarity and drug efficacy similarity of all possible pair combinations were obtained and similarly classified. The proportion of the number of pairs with dispensing errors per the total number of drug pairs in the hospital's drug formulary in each category was calculated. The highest proportion of the number of pairs with dispensing errors was 36% for the Efficacy-Same and Name-Similar group, and the lowest proportion was 0.022% for the Efficacy-Far and Name-Dissimilar group. The proportion of the number of pairs with dispensing errors was significantly higher in the Name-Similar category than in the Name-Dissimilar category for all drug efficacy categories. Our results indicate that drug name similarity increases the risk of dispensing errors, and that m2-vwhtfrag is a useful indicator to assess dispensing errors in clinical practice. Such drug name and efficacy similarity evaluations can help identify factors causing dispensing errors, and predict the risk of dispensing errors for newly adopted drugs, considering the relationship with the whole drug formulary in the hospital dispensary.

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Name agreement (NA) refers to the degree to which speakers agree on a picture's name. A robust finding is that speakers are faster to name pictures with high agreement (HA) than those with low agreement (LA). This NA effect is thought to occur because LA pictures strongly activate several names, so speakers need time to select one. HA pictures, in contrast, strongly activate a single name, so there is no need to select one name out of several alternatives. Recent models of lexical access suggest that the structure of the mental lexicon changes with experience. Thus, speakers should consider a range of names when naming LA pictures, but the extent to which they consider each of these names should change with experience. We tested these hypotheses in two picture-naming experiments. In Experiment 1, participants were faster to name LA than HA pictures when they named each picture once. Importantly, they were faster to produce modal names (provided by most participants) than alternative names for LA pictures, consistent with the view that speakers activate multiple names for LA pictures. In Experiment 2, participants were familiarised with the modal name before the experiment and named each picture three times. Although there was still an NA effect when participants named the pictures the first time, it was reduced in comparison to Experiment 1 and was further reduced with each picture repetition. Thus, familiarisation and repetition reduced the NA effect but did not eliminate it, suggesting speakers activate a range of plausible names.

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BACKGROUND: Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization. OBJECTIVE: This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment. METHODS: We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score. RESULTS: Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora. CONCLUSIONS: BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

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Generic medications contain the identical active ingredient in the same concentration as their branded counterparts and are administered in the same manner, aiming to deliver comparable efficacy, dosage, and clinical outcomes. Nevertheless, variations in additives and formulation processes, particularly noticeable in topical medications, can influence factors like ease of use and patient adherence. Therefore, in this study, we aimed to compare the rheological attributes of branded and generic injectable ointments, assessing disparities in formulation performance and their impact on patient care. Posterisan® Forte and Hemoporison® ointments were used as the branded and generic versions, respectively, and their viscosity, ductility, and viscoelastic properties were evaluated. Posterisan® Forte showcased enhanced spread ability, maintaining uniform flow characteristics across varying temperatures, whereas Hemoporison® displayed pronounced thixotropic properties and stiffness, suggesting potential benefits for applications necessitating reversible viscosity adjustments and heightened rigidity. Despite sharing identical additives, observable differences in physical characteristics highlight the necessity of understanding formulation traits, which could influence ointment behavior. Alterations in fluidity and viscosity may affect how patients perceive and apply the medication, potentially influencing treatment outcomes and the occurrence of adverse effects.

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A Special-purpose Committee on Fungal Names with the Same Epithet was established at the XIX International Botanical Congress (IBC) in Shenzhen, China in 2017, with a mandate to report to the 12th International Mycological Congress (IMC) with recommendations on a preferred course of action with respect to names of pleomorphic fungi sharing the same epithet under the International Code of Nomenclature for algae, fungi, and plants. This report provides a synthesis of the deliberations from the Special-purpose Committee. We discuss the arguments for and against the proposed solution to the problems that have arisen regarding the nomenclature of fungi described in multiple morphs using the same epithet. We also propose a gentler method of addressing the problem using existing procedures.

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The International Union of Pure and Applied Chemistry (IUPAC) name given in the title is incorrect. The correct IUPAC name for this molecule is tetraspiro[2.1.25.1.29.1.213.13]hexadecane-4,8,12,16-tetraone. The incorrect name given in the title, unfortunately, makes the carbon atom hexavalent at two different (3 and 5) positions. In addition, the two other keto groups (at positions 1 and 7) would appear on two of the cyclopropane rings if one adopts to the incorrect name. Nevertheless, this wrong name is a good example to discuss the importance of IUPAC nomenclature in the classroom with students.

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Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce guided LDA deconvolution (GLDADec), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to The Cancer Genome Atlas tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

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The diversity of pathogenetic mechanisms underlying arterial hypertension leads to the necessity to devise a personalized approach to the diagnosis and treatment of the disease. Metabolomics is one of the promising methods for personalized medicine, as it provides a comprehensive understanding of the physiological processes occurring in the body. The metabolome is a set of low-molecular substances available for detection in a sample and representing intermediate and final products of cell metabolism. Changes in the content and ratio of metabolites in the sample mark the corresponding pathogenetic mechanisms by highlighting them, which is especially important for such a multifactorial disease as arterial hypertension. To identify metabolomic markers for hypertensive conditions of different origins, three forms of arterial hypertension (AH) were studied: rats with hereditary AH (ISIAH rat strain); rats with AH induced by L-NAME administration (a model of endothelial dysfunction with impaired NO production); rats with AH caused by the administration of deoxycorticosterone in combination with salt loading (hormone-dependent form - DOCA-salt AH). WAG rats were used as normotensive controls. 24-hour urine samples were collected from all animals and analyzed by quantitative NMR spectroscopy for metabolic profiling. Then, potential metabolomic markers for the studied forms of hypertensive conditions were identified using multivariate statistics. Analysis of the data obtained showed that hereditary stress-induced arterial hypertension in ISIAH rats was characterized by a decrease in the following urine metabolites: nicotinamide and 1-methylnicotinamide (markers of inflammatory processes), N- acetylglutamate (nitric oxide cycle), isobutyrate and methyl acetoacetate (gut microbiota). Pharmacologically induced forms of hypertension (the L-NAME and DOCA+NaCl groups) do not share metabolomic markers with hereditary AH. They are differentiated by N,N-dimethylglycine (both groups), choline (the L-NAME group) and 1-methylnicotinamide (the group of rats with DOCA-salt hypertension).

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OBJECTIVE: Author name incompleteness, referring to only first initial available instead of full first name, is a long-standing problem in MEDLINE and has a negative impact on biomedical literature systems. The purpose of this study is to create an Enhanced Author Names (EAN) dataset for MEDLINE that maximizes the number of complete author names. MATERIALS AND METHODS: The EAN dataset is built based on a large-scale name comparison and restoration with author names collected from multiple literature databases such as MEDLINE, Microsoft Academic Graph, and Semantic Scholar. We assess the impact of EAN on biomedical literature systems by conducting comparative and statistical analyses between EAN and MEDLINE's author names dataset (MAN) on 2 important tasks, author name search and author name disambiguation. RESULTS: Evaluation results show that EAN improves the number of full author names in MEDLINE from 69.73 million to 110.9 million. EAN not only restores a substantial number of abbreviated names prior to the year 2002 when the NLM changed its author name indexing policy but also improves the availability of full author names in articles published afterward. The evaluation of the author name search and author name disambiguation tasks reveal that EAN is able to significantly enhance both tasks compared to MAN. CONCLUSION: The extensive coverage of full names in EAN suggests that the name incompleteness issue can be largely mitigated. This has significant implications for the development of an improved biomedical literature system. EAN is available at https://zenodo.org/record/10251358, and an updated version is available at https://zenodo.org/records/10663234.

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A recent decision reveals how a New Zealand's disciplinary tribunal promoted justice for an unwell lawyer in a case of professional misconduct. In 2023, the Lawyers and Conveyancers Disciplinary Tribunal (LCDT) applied a 'merciful approach' when assessing the lawyer's misconduct and health issues. In Auckland Standards Committee 3 v Ms W [2023], the LCDT discussed the impacts of reproductive treatment in relation to the practitioner's conduct. This decision is the foundation to compare the disciplinary regime for legal and health practitioners in New Zealand. The article outlines New Zealand's framework for discipline of lawyers, noting the absence of a health pathway. The article discusses opportunities to resolve cases involving impaired lawyers outside the disciplinary system, including benefits and disadvantages of mandatory reporting. While focusing on the legal profession, the discussion is relevant to other professions and examines health-promoting regulatory strategies from other jurisdictions.

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PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.

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OBJECTIVE: Social media-based public health research is crucial for epidemic surveillance, but most studies identify relevant corpora with keyword-matching. This study develops a system to streamline the process of curating colloquial medical dictionaries. We demonstrate the pipeline by curating a Unified Medical Language System (UMLS)-colloquial symptom dictionary from COVID-19-related tweets as proof of concept. METHODS: COVID-19-related tweets from February 1, 2020, to April 30, 2022 were used. The pipeline includes three modules: a named entity recognition module to detect symptoms in tweets; an entity normalization module to aggregate detected entities; and a mapping module that iteratively maps entities to Unified Medical Language System concepts. A random 500 entity samples were drawn from the final dictionary for accuracy validation. Additionally, we conducted a symptom frequency distribution analysis to compare our dictionary to a pre-defined lexicon from previous research. RESULTS: We identified 498 480 unique symptom entity expressions from the tweets. Pre-processing reduces the number to 18 226. The final dictionary contains 38 175 unique expressions of symptoms that can be mapped to 966 UMLS concepts (accuracy = 95%). Symptom distribution analysis found that our dictionary detects more symptoms and is effective at identifying psychiatric disorders like anxiety and depression, often missed by pre-defined lexicons. CONCLUSIONS: This study advances public health research by implementing a novel, systematic pipeline for curating symptom lexicons from social media data. The final lexicon's high accuracy, validated by medical professionals, underscores the potential of this methodology to reliably interpret, and categorize vast amounts of unstructured social media data into actionable medical insights across diverse linguistic and regional landscapes.

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Science journalism is a critical way for the public to learn about and benefit from scientific findings. Such journalism shapes the public's view of the current state of science and legitimizes experts. Journalists can only cite and quote a limited number of sources, who they may discover in their research, including recommendations by other scientists. Biases in either process may influence who is identified and ultimately included as a source. To examine potential biases in science journalism, we analyzed 22,001 non-research articles published by Nature and compared these with Nature-published research articles with respect to predicted gender and name origin. We extracted cited authors' names and those of quoted speakers. While citations and quotations within a piece do not reflect the entire information-gathering process, they can provide insight into the demographics of visible sources. We then predicted gender and name origin of the cited authors and speakers. We compared articles with a comparator set made up of first and last authors within primary research articles in Nature and a subset of Springer Nature articles in the same time period. In our analysis, we found a skew toward quoting men in Nature science journalism. However, quotation is trending toward equal representation at a faster rate than authorship rates in academic publishing. Gender disparity in Nature quotes was dependent on the article type. We found a significant over-representation of names with predicted Celtic/English origin and under-representation of names with a predicted East Asian origin in both in extracted quotes and journal citations but dampened in citations.

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The perennial problem of author name ambiguity has attracted increasing attention in the academic community. Drawing on the literature, this article first highlights the pervasiveness of the problem and discusses its adverse consequences. It then analyzes the behavioral causes of the problem in the Chinese context and attributes them to personal, cultural, and institutional factors. Informed by this analysis and recognizing ORCID as a promising solution, we propose an ORCID-based "Prevention plus Cure" campaign against author name ambiguity. The prevention objective relies on researchers' consistent use of ORCID, while the cure objective involves retrospectively integrating ORCIDs into backfile publications. We also outline the responsibilities of various stakeholders to ensure the success of the campaign. Furthermore, we argue that universal adoption of ORCID can help curb authorship-related misconduct, discern predatory journals and publishers, and track researchers' undesirable records of academic publishing. We then analyze the current status of ORCID adoption in China, identify potential challenges, propose tentative solutions to address them, and highlight ORCID as a tool that can be utilized to empower China's combat against research misconduct. In conclusion, we emphasize the importance of conducting empirical research to inform more effective promotion of ORCID adoption in China.

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OBJECTIVE: Clozapine is considered the most reliable drug for treatment-resistant schizophrenia. In 2014, a generic formulation of clozapine (Clzapine) was introduced in Korea. This study was performed to provide clinical information regarding the use of clozapine and to compare efficacy and tolerability when converting from the brand-name formulation (Clozaril) to the generic formulation during longterm maintenance treatment among Korean patients with schizophrenia. METHODS: This mirror-image study retrospectively investigated the electronic medical records of patients who had switched from Clozaril to Clzapine with a ≥1-year duration for each formulation. Clinical data were collected, including information regarding clozapine use, psychiatric hospitalization, co-medications, and blood test findings. Data before and after the switch were compared using paired t-tests. RESULTS: Among 332 patients, the mean 1-year dosages were 233.32±149.35 mg/day for Clozaril and 217.36±136.66 mg/day for Clzapine. The mean clozapine concentration-to-dose ratios were similar before and after the switch (Clozaril, 1.33±0.68; Clzapine, 1.26±0.80). Switching from Clozaril to Clzapine resulted in no significant differences in the hospitalization rate, hospitalization duration, or laboratory findings (liver function parameters, serum cholesterol level, and serum glucose level). Equivalent doses of co-prescribed antidepressants were decreased, but concomitant medications otherwise showed no significant differences. CONCLUSION: Clinical efficacy and tolerability appear comparable when switching to Clzapine during clozapine maintenance treatment. This study offers descriptive real-world clinical insights into clozapine maintenance treatment in Korea, thereby providing patients with more treatment options and contributing to the development of maintenance guidelines tailored to the Korean population.