Development of a novel fingerprint for chemical reactions and its application to large-scale reaction classification and similarity.

Fingerprint methods applied to molecules have proven to be useful for similarity determination and as inputs to machine-learning models. Here, we present the development of a new fingerprint for chemical reactions and validate its usefulness in building machine-learning models and in similarity assessment. Our final fingerprint is constructed as the difference of the atom-pair fingerprints of products and reactants and includes agents via calculated physicochemical properties. We validated the fingerprints on a large data set of reactions text-mined from granted United States patents from the last 40 years that have been classified using a substructure-based expert system. We applied machine learning to build a 50-class predictive model for reaction-type classification that correctly predicts 97% of the reactions in an external test set. Impressive accuracies were also observed when applying the classifier to reactions from an in-house electronic laboratory notebook. The performance of the novel fingerprint for assessing reaction similarity was evaluated by a cluster analysis that recovered 48 out of 50 of the reaction classes with a median F-score of 0.63 for the clusters. The data sets used for training and primary validation as well as all python scripts required to reproduce the analysis are provided in the Supporting Information.

Initial renal replacement therapy (RRT) modality associates with 90-day postdischarge RRT dependence in critically ill AKI survivors.

PURPOSE: Real-world comparison of RRT modality on RRT dependence at 90 days postdischarge among ICU patients discharged alive after RRT for acute kidney injury (AKI). METHODS: Using claims-linked to US hospital discharge data (Premier PINC AI Healthcare Database [PHD]), we compared continuous renal replacement therapy (CRRT) vs. intermittent hemodialysis (IHD) for AKI in adult ICU patients discharged alive from January 1, 2018 to June 30, 2021. RRT dependence at 90 days postdischarge was defined as ≥2 RRT treatments in the last 8 days. Between-group differences were balanced using inverse probability treatment weighting (IPTW). RESULTS: Of 34,804 patients, 3804 patients (from 382 hospitals) had claims coverage for days 83-90 postdischarge. Compared to IHD-treated patients (n = 2740), CRRT-treated patients (n = 1064) were younger; had more admission to large teaching hospitals, surgery, sepsis, shock, mechanical ventilation, but lower prevalence of comorbidities (p < 0.05 for all). Compared to IHD-treated patients, CRRT-treated patients had lower RRT dependence at hospital discharge (26.5% vs. 29.8%, p = 0.04) and lower RRT dependence at 90 days postdischarge (4.9% vs. 7.4% p = 0.006) with weighted adjusted OR (95% CI): 0.68 (0.47-0.97), p = 0.03. Results persisted in sensitivity analyses including patients who died during days 1-90 postdischarge (n = 112) or excluding patients from hospitals with IHD patients only (n = 335), or when excluding patients who switched RRT modalities (n = 451). CONCLUSIONS: Adjusted for potential confounders, the odds of RRT dependence at 90 days postdischarge among survivors of RRT for AKI was 30% lower for those treated first with CRRT vs. IHD, overall and in several sensitivity analyses. SUMMARY: Critically ill patients in intensive care units (ICU) may develop acute kidney injury (AKI) that requires renal replacement therapy (RRT) to temporarily replace the injured kidney function of cleaning the blood. Two main types of RRT in the ICU are called continuous renal replacement therapy (CRRT), which is performed almost continuously, i.e., for >18 h per day, and intermittent hemodialysis (IHD), which is a more rapid RRT that is usually completed in a little bit over 6 h, several times per week. The slower CRRT may be gentler on the kidneys and is more likely to be used in the sickest patients, who may not be able to tolerate IHD. We conducted a data-analysis study to evaluate whether long-term effects on kidney function (assessed by ongoing need for RRT, i.e., RRT dependence) differ depending on use of CRRT vs. IHD. In a very large US linked hospital-discharge/claims database we found that among ICU patients discharge alive after RRT for AKI, fewer CRRT-treated patients had RRT dependence at hospital discharge (26.5% vs. 29.8%, p = 0.04) and at 90 days after discharge (4.9% vs. 7.4% p = 0.006). In adjusted models, RRT dependence at 90 days postdischarge was >30% lower for CRRT than IHD-treated patients. These results from a non-randomized study suggest that among survivors of RRT for AKI, CRRT may result in less RRT dependence 90 days after hospital discharge.

Chemical name to structure: OPSIN, an open source solution.

We have produced an open source, freely available, algorithm (Open Parser for Systematic IUPAC Nomenclature, OPSIN) that interprets the majority of organic chemical nomenclature in a fast and precise manner. This has been achieved using an approach based on a regular grammar. This grammar is used to guide tokenization, a potentially difficult problem in chemical names. From the parsed chemical name, an XML parse tree is constructed that is operated on in a stepwise manner until the structure has been reconstructed from the name. Results from OPSIN on various computer generated name/structure pair sets are presented. These show exceptionally high precision (99.8%+) and, when using general organic chemical nomenclature, high recall (98.7-99.2%). This software can serve as the basis for future open source developments of chemical name interpretation.

Lessons learned from casualty statistics in health care system development: Afghanistan 2008-2009.

A retrospective study of Afghanistan National Army casualty rates for a 1-year period was completed to assist in health care system assessment and further development during a period of rapid force expansion. Battle and disease nonbattle injuries by Corps area were determined from data on soldier visits from all military health care facilities. The number of fielded forces in each Corps was used to calculate the populations at risk. Total manpower losses from all casualties were tabulated. The 15,336 casualties (175 per thousand fielded soldiers) resulted in the loss of 146,986 duty days (average 9.5 days per casualty). Battle casualties were 739 (8.4 per 1,000) and nonbattle casualties were 14,597(166 per 1,000) with 72% secondary to infectious diseases. Casualty rates from both battle and disease nonbattle injuries were high, but casualty rates were particularly high from infectious diseases. Rapid force expansion in developing countries requires early consideration for resourcing and implementation of preventive medicine programs.

Utilization of Patient-Controlled Analgesia Reduces Length of Stay of Sickle Cell Crisis Hospitalizations.

Background: Sickle cell crisis hospitalizations are emotionally and financially burdensome to patients and healthcare systems, and processes to decrease the frequency or length of stay of these crises should be examined. Methods: This is a multicenter retrospective hospital record review of sickle cell crisis hospitalizations as defined by ICD-10 codes (D57.1-4), from January 2016 through December 2019, examining inpatient medication administration records and length of stay among admitted adults aged 18-65 years. Patient controlled analgesia orders using morphine, hydromorphone, fentanyl and/or merperidine at any point of an admission (n=188) were compared to admissions without any patient-controlled analgesia orders (n=2,159). The primary end point was hospital length of stay in days. A secondary analysis examining patients with or without greater than four admissions was also conducted. Results: The 1,675 patients who met criteria comprised 2,347 sickle cell hospitalizations during the four years examined. Of those admissions, 188 had at least one patient-controlled analgesic documented in their chart and had an average length of stay of 4.54 days (SD 3.34). The 2,159 admissions without any patient-controlled analgesia had an average length of stay of 5.74 days (SD 4.64). The difference of 1.2 days between the groups was statistically significant (p≤0.0001) using a Wilcoxon signed-rank test. Conclusion: Among patients with sickle cell crises who required inpatient hospitalizations, the use of patient-controlled analgesia demonstrated a statistically significant reduction of 1.2 days in their total length of stay. These findings support potentially changing hospital protocols to increase patient-controlled analgesia utilization.

U.S. Government engagement in support of global disease surveillance.

Global cooperation is essential for coordinated planning and response to public health emergencies, as well as for building sufficient capacity around the world to detect, assess and respond to health events. The United States is committed to, and actively engaged in, supporting disease surveillance capacity building around the world. We recognize that there are many agencies involved in this effort, which can become confusing to partner countries and other public health entities. This paper aims to describe the agencies and offices working directly on global disease surveillance capacity building in order to clarify the United States Government interagency efforts in this space.

Vcsa1 acts as a marker of erectile function recovery after gene therapeutic and pharmacological interventions.

PURPOSE: We identified molecular markers of erectile function, particularly those responding to erectile dysfunction treatment. MATERIALS AND METHODS: Sprague-Dawley retired breeder rats were intracorporeally injected with pVAX-hSlo, pSMAA-hSlo or the control plasmid pVAX. One week later the intracorporeal pressure-to-blood pressure ratio and gene expression were determined by microarray analysis and quantitative reverse transcriptase-polymerase chain reaction. Rat corporeal cells were transfected in vitro with pVAX-hSlo, pSMAA-hSlo or pVAX and the change in gene expression was determined. We also determined whether Vcsa1 expression was changed after pharmacotherapy using tadalafil. RESULTS: Animals treated with vectors expressing hSlo had significantly improved erectile function compared to that in controls, accompanied by changed expression of a subset of genes. Vcsa1 was one of the genes that was most changed in expression (the third of approximately 31,000 with greater than 10-fold up-regulation). Changes in gene expression were different than those observed in corporeal cells transfected in vitro, distinguishing gene expression changes that were a direct effect of hSlo over expression. When tadalafil was administered in retired breeder rats, the Vcsa1 transcript increased 4-fold in corporeal tissue compared to that in untreated controls. CONCLUSIONS: Our study identifies a set of genes that are changed in response to improved erectile function, rather than as a direct effect of treatment. We noted Vcsa1 may act as marker of the restoration of erectile function after gene transfer and pharmacotherapy.

ADMET rules of thumb II: A comparison of the effects of common substituents on a range of ADMET parameters.

A systematic analysis of data generated in key in vitro assays within GSK has been undertaken to identify what impact a range of common substituents have on a range of ADMET parameters. These include; P450 1A2, 2C9, 2C19, 2D6 and 3A4 inhibition, hERG inhibition, phosphate buffer solubility and artificial membrane permeability. We do this by identifying all matched molecular pairs, differing by the replacement of a hydrogen atom with a list of predefined substituents. For each substituent we calculate the mean difference in the ADMET parameter for all the matched molecular pairs identified, making a statistical assessment of the difference, as well as assessing the diversity for each example to ensure that the results can be generalized. We also relate the change in activity observed for each substituent to differences in their molecular properties in an effort to identify any structural alerts.

The development of models to predict melting and pyrolysis point data associated with several hundred thousand compounds mined from PATENTS.

BACKGROUND: Melting point (MP) is an important property in regards to the solubility of chemical compounds. Its prediction from chemical structure remains a highly challenging task for quantitative structure-activity relationship studies. Success in this area of research critically depends on the availability of high quality MP data as well as accurate chemical structure representations in order to develop models. Currently, available datasets for MP predictions have been limited to around 50k molecules while lots more data are routinely generated following the synthesis of novel materials. Significant amounts of MP data are freely available within the patent literature and, if it were available in the appropriate form, could potentially be used to develop predictive models. RESULTS: We have developed a pipeline for the automated extraction and annotation of chemical data from published PATENTS. Almost 300,000 data points have been collected and used to develop models to predict melting and pyrolysis (decomposition) points using tools available on the OCHEM modeling platform (http://ochem.eu). A number of technical challenges were simultaneously solved to develop models based on these data. These included the handing of sparse data matrices with >200,000,000,000 entries and parallel calculations using 32 × 6 cores per task using 13 descriptor sets totaling more than 700,000 descriptors. We showed that models developed using data collected from PATENTS had similar or better prediction accuracy compared to the highly curated data used in previous publications. The separation of data for chemicals that decomposed rather than melting, from compounds that did undergo a normal melting transition, was performed and models for both pyrolysis and MPs were developed. The accuracy of the consensus MP models for molecules from the drug-like region of chemical space was similar to their estimated experimental accuracy, 32 °C. Last but not least, important structural features related to the pyrolysis of chemicals were identified, and a model to predict whether a compound will decompose instead of melting was developed. CONCLUSIONS: We have shown that automated tools for the analysis of chemical information have reached a mature stage allowing for the extraction and collection of high quality data to enable the development of structure-activity relationship models. The developed models and data are publicly available at http://ochem.eu/article/99826.

Efficient chemical-disease identification and relationship extraction using Wikipedia to improve recall.

Awareness of the adverse effects of chemicals is important in biomedical research and healthcare. Text mining can allow timely and low-cost extraction of this knowledge from the biomedical literature. We extended our text mining solution, LeadMine, to identify diseases and chemical-induced disease relationships (CIDs). LeadMine is a dictionary/grammar-based entity recognizer and was used to recognize and normalize both chemicals and diseases to Medical Subject Headings (MeSH) IDs. The disease lexicon was obtained from three sources: MeSH, the Disease Ontology and Wikipedia. The Wikipedia dictionary was derived from pages with a disease/symptom box, or those where the page title appeared in the lexicon. Composite entities (e.g. heart and lung disease) were detected and mapped to their composite MeSH IDs. For CIDs, we developed a simple pattern-based system to find relationships within the same sentence. Our system was evaluated in the BioCreative V Chemical-Disease Relation task and achieved very good results for both disease concept ID recognition (F1-score: 86.12%) and CIDs (F1-score: 52.20%) on the test set. As our system was over an order of magnitude faster than other solutions evaluated on the task, we were able to apply the same system to the entirety of MEDLINE allowing us to extract a collection of over 250 000 distinct CIDs.

Big Data from Pharmaceutical Patents: A Computational Analysis of Medicinal Chemists' Bread and Butter.

Multiple recent studies have focused on unraveling the content of the medicinal chemist's toolbox. Here, we present an investigation of chemical reactions and molecules retrieved from U.S. patents over the past 40 years (1976-2015). We used a sophisticated text-mining pipeline to extract 1.15 million unique whole reaction schemes, including reaction roles and yields, from pharmaceutical patents. The reactions were assigned to well-known reaction types such as Wittig olefination or Buchwald-Hartwig amination using an expert system. Analyzing the evolution of reaction types over time, we observe the previously reported bias toward reaction classes like amide bond formations or Suzuki couplings. Our study also shows a steady increase in the number of different reaction types used in pharmaceutical patents but a trend toward lower median yield for some of the reaction classes. Finally, we found that today's typical product molecule is larger, more hydrophobic, and more rigid than 40 years ago.

LeadMine: a grammar and dictionary driven approach to entity recognition.

BACKGROUND: Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. RESULTS: Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. CONCLUSIONS: Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution.

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.

Annotated chemical patent corpus: a gold standard for text mining.

Exploring the chemical and biological space covered by patent applications is crucial in early-stage medicinal chemistry activities. Patent analysis can provide understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. To validate the performance of such methods, a manually annotated patent corpus is essential. In this study we have produced a large gold standard chemical patent corpus. We developed annotation guidelines and selected 200 full patents from the World Intellectual Property Organization, United States Patent and Trademark Office, and European Patent Office. The patents were pre-annotated automatically and made available to four independent annotator groups each consisting of two to ten annotators. The annotators marked chemicals in different subclasses, diseases, targets, and modes of action. Spelling mistakes and spurious line break due to optical character recognition errors were also annotated. A subset of 47 patents was annotated by at least three annotator groups, from which harmonized annotations and inter-annotator agreement scores were derived. One group annotated the full set. The patent corpus includes 400,125 annotations for the full set and 36,537 annotations for the harmonized set. All patents and annotated entities are publicly available at www.biosemantics.org.

Open Data, Open Source and Open Standards in chemistry: The Blue Obelisk five years on.

BACKGROUND: The Blue Obelisk movement was established in 2005 as a response to the lack of Open Data, Open Standards and Open Source (ODOSOS) in chemistry. It aims to make it easier to carry out chemistry research by promoting interoperability between chemistry software, encouraging cooperation between Open Source developers, and developing community resources and Open Standards. RESULTS: This contribution looks back on the work carried out by the Blue Obelisk in the past 5 years and surveys progress and remaining challenges in the areas of Open Data, Open Standards, and Open Source in chemistry. CONCLUSIONS: We show that the Blue Obelisk has been very successful in bringing together researchers and developers with common interests in ODOSOS, leading to development of many useful resources freely available to the chemistry community.

Effects of a minimum interval immunization schedule for diphtheria and tetanus toxoids and acellular pertussis vaccination during a pertussis outbreak.

OBJECTIVE: To examine the impact of a minimum interval schedule for administering diphtheria and tetanus toxoids and acellular pertussis vaccine (DTaP) in infants during a statewide pertussis outbreak on receipt of inactivated polio vaccine (IPV) and pneumococcal conjugate vaccine (PCV). DESIGN: Retrospective cohort study using the state immunization registry. SETTING: Arizona. PARTICIPANTS: Arizona children born between February 1 and September 30, 2005, who received their initial DTaP dose during a statewide pertussis outbreak (N = 45 129). Main Exposures Children who received at least 1 dose of DTaP on the minimum interval schedule (minimum interval group) compared with children who received all doses of DTaP on the standard childhood and adolescent immunization schedule (standard group). OUTCOME MEASURES: Timing and receipt of 3 doses of the DTaP, IPV, and PCV. RESULTS: Compared with children in the standard group, children in the minimum interval group were more likely to receive 3 doses of DTaP (relative risk, 1.34; 95% confidence interval, 1.32-1.35), 3 doses of IPV (1.27; 1.25-1.29), and 3 doses of PCV (1.37; 1.35-1.39). CONCLUSION: Recommending a minimum interval DTaP schedule during a statewide pertussis outbreak had a positive association with the receipt of IPV and PCV, 2 vaccines normally administered at the same time as DTaP.

Issue of prostate gland size, laparoscopic radical prostatectomy, and continence revisited.

OBJECTIVES: To evaluate the influence of prostate gland size on the perioperative, pathologic, and continence outcomes after laparoscopic radical prostatectomy (LRP). METHODS: 144 LRPs for which 18-month continence data were available were performed by a single surgeon, and were retrospectively reviewed. Patients were divided initially into two groups based on final prostate gland size: group 1 (weight less than 50 g) and group 2 (weight 50 g or more). Group 2 patients were stratified into group IIa (weight 50 to 70 g) and Group IIb (weight 70 g or more) to further examine the effect of gland size on continence. RESULTS: Larger glands had higher mean prostate-specific antigen (P <0.05) but among groups there were no significant differences in patient age, Gleason sum, pathological stage, operative time, intraoperative blood loss, or positive margin rate. We noted a significant difference in mean time to recovery of continence for patients with small glands versus large glands: group 1 (8.2 months), group 2 (9.9 months), group IIa (8.5 months), and group IIb (13.8 months) (P <0.05). CONCLUSIONS: Prostate gland size had no effect on perioperative outcomes. However, recovery of postoperative continence can be delayed in patients with large prostates.

Electrical prostate morcellator: an alternative to manual morcellation for laparoscopic nephrectomy specimens? An in vitro study.

OBJECTIVES: To compare the safety and efficacy of morcellation with the electrical prostate morcellator (EPM) or manual morcellation of the kidney, using an internal view within the morcellation sac. METHODS: Thirty porcine kidneys, mean renal mass 174.5 g, were divided into three groups of 10. All morcellations were performed inside the LapSac. Groups 1 and 2 underwent morcellation using the EPM, monitored inside the LapSac using the nephroscope and outside the LapSac with the laparoscope, respectively. Group 3 underwent manual morcellation with ring forceps. The groups were assessed for morcellation time, fragment size, and LapSac integrity. RESULTS: In group 1, one pinhole perforation occurred; in group 2, nine perforations occurred (five large and four pinhole). No perforations occurred (P <0.001) in group 3 (manual morcellation). The mean morcellation time for groups 1 through 3 was, respectively, 86.9, 47.1, and 15.1 minutes (P <0.0001). The corresponding mean fragment size was 0.011, 0.015, and 1.36 g. The difference in mean fragment size was significantly different between the manual morcellation group and the EPM groups (P <0.001), but not between the two EPM groups (P = 0.12). CONCLUSIONS: Manual morcellation was safe, fast, and superior to morcellation with the EPM monitored either inside or outside the LapSac. The high rate of LapSac perforation precludes the use of EPM after laparoscopic radical nephrectomy in the clinical forum.