Recent-Onset Altered Mental Status: Evaluation and Management.

Potential precipitating factors for the recent onset of altered mental status (AMS) include primary central nervous system insults, systemic infections, metabolic disturbances, toxin exposure, medications, chronic systemic diseases, and psychiatric conditions. Delirium is also an important manifestation of AMS, especially in older people who are hospitalized. Clinicians should identify and treat reversible causes of the AMS, some of which require urgent intervention to minimize morbidity and mortality. A history and physical examination guide diagnostic testing. Laboratory testing, chest radiography, and electrocardiography help diagnose infections, metabolic disturbances, toxins, and systemic conditions. Neuroimaging with computed tomography or magnetic resonance imaging should be performed when the initial evaluation does not identify a cause or raises concern for intracranial pathology. Lumbar puncture and electroencephalography are also important diagnostic tests in the evaluation of AMS. Patients at increased risk of AMS benefit from preventive measures. The underlying etiology determines the definitive treatment. When intervention is needed to control patient behaviors that threaten themselves or others, nonpharmacologic interventions are preferred to medications. Physical restraints should rarely be used and only for the shortest time possible. Medications should be used only when nonpharmacologic treatments are ineffective.

Development of a risk prediction nomogram for disposition of acute toxic exposure patients to intensive care unit.

Early risk stratification of acutely poisoned patients is essential to identify patients at high risk of intensive care unit (ICU) admission. We aimed to develop a prognostic model and risk-stratification nomogram based on the readily accessible clinical and laboratory predictors on admission for the probability of ICU admission in acutely poisoned patients. This retrospective cohort study included adult patients with acute toxic exposure to a drug or a chemical substance. Patients' demographic, toxicologic, clinical and laboratory data were collected. Among the 1260 eligible patients, 180 (14.3%) were admitted to the ICU. We developed a generalized prognostic model for predicting ICU admission in patients with acute poisoning. The predictors included the Glasgow coma scale, oxygen saturation, diastolic blood pressure, respiratory rate and blood bicarbonate concentration. The model displayed excellent discrimination and calibration (optimistic-adjusted area under the curve = 0.924 and optimistic-adjusted Hosmer and Lemeshow test = 0.922, respectively) when internally validated. Additionally, we developed prognostic models that determine ICU admission in patients with specific poisonings. Furthermore, we constructed risk-stratification nomograms that rank the probability of ICU admission in these patients. The developed risk-stratification nomograms help decision-making regarding ICU admission in acute poisonings. Future external validation in independent cohorts is necessary before clinical application.

Feasibility of physiologically based pharmacokinetic simulations for assessing pediatric patients after accidental drug ingestion: A case study of a 1.4-year-old girl who ingested alprazolam.

The accidental ingestion of drugs is a common concern, especially in the case of young children. A physiologically based pharmacokinetic (PBPK) model that implements the age-dependent size growth and ontogeny of organ functions can be used to predict the concentration-time profiles of drugs in the pediatric population. In this study, the feasibility of using a PBPK model for predicting the amount of drug accidentally swallowed by a child was assessed based on a case study in an infant. Alprazolam was the drug involved in the current case. The developed PBPK model of alprazolam was first evaluated using pharmacokinetic data obtained in healthy adult male volunteers. Then, it was adapted for application to virtual Japanese pediatric subjects having the same demographic information as the infant of interest. The pharmacokinetic data observed in the infant fell within the range of the 5th and 95th percentiles of the pharmacokinetic simulations after administration of 0.4 mg alprazolam (equivalent to one tablet) in the panel of virtual infants. PBPK simulations could provide estimates of the amount accidentally ingested by a child and also give mechanistic insights into the observed drug concentrations. The current study demonstrates the potential clinical utility of PBPK modeling.

Health risk estimation of metals bioaccumulated in commercial fish from coastal areas and rivers in Bangladesh.

Metal contaminations in commercial fish have become a great public health concern worldwide including Bangladesh. The current study was conducted to provide preliminary evidence of nine metals in three commercially significant fish namely Pampus argenteus, Sardinella longiceps and Tenualosa ilisha collected from four coastal stations- Kuakata, Pathorghata, Cox's Bazar, and Pirojpur, and eight stations of five rivers- Padma, Meghna, Jamuna, Katcha, and Nobogonga in Bangladesh. High magnitudes of Pb (0.74-4.59 mg/kg ww), Cd (0.07-0.24 mg/kg ww), and Mn (0.45-2.03 mg/kg ww) were recorded in the sampling stations that exceeded the maximum permissible limits (MPL) proposed by different recognized organizations. Significant mean differences of metal concentrations were observed (p < 0.05) between species and stations. In fish samples, excessive metals accumulations were recorded from Kuakata (St.1) at the coastal area, and Nobogonga (St. 12) among the rivers. The health risk assessment (HRA) was carried out comprehensively via the estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), and target cancer risk (TR) calculations. The outcomes of EDI, THQ, and HI suggest that chronic exposure to towering Pb content might pose potential health threats to inhabitants particularly living in highly polluted stations of the coastal area. In addition, the massive TR values of Cd intake through fish consumption from the coastal area might create cancer risks. Accordingly, the ingestion of metals contaminated fish portends chronic as well as acute health risks to Bangladeshi people living both at home and abroad.

Inpatient Burden and Mortality of Methanol Intoxication in the United States.

BACKGROUND: This study aimed to assess inpatient prevalence, characteristics, outcomes, and resource utilization of hospitalization for methanol intoxication in the United States. MATERIALS AND METHODS: A total of 603 hospitalized patients with a primary diagnosis of methanol intoxication from 2003 to 2014 were identified in the National Inpatient Sample database. The inpatient prevalence, clinical characteristics, treatments, outcomes, resource utilization, were investigated. Multivariable logistic regression was performed to identify factors independently associated with in-hospital mortality. RESULTS: The overall inpatient prevalence of methanol intoxication among hospitalized patients was 6.4 cases per 1,000,000 admissions in the United States. The mean age was 38±18 (range 0-86) years. 44% used methanol for suicidal attempts. 20% of admissions required mechanical ventilation, and 40% required renal replacement therapy. The three most common complications were metabolic acidosis (44%), hypokalemia (18%), and visual impairment or optic neuritis (8%). The three most common end-organ failures were renal failure (22%), respiratory failure (21%), and neurological failure (17%). 6.5% died in the hospital. Factors associated with increased in-hospital mortality included alcohol drinking, hypernatremia, renal failure, respiratory failure, circulatory failure, and neurological failure. The mean length of hospital stay was 4.0 days. The mean hospitalization cost per patient was $43,222 CONCLUSION: The inpatient prevalence of methanol intoxication in the United States was 6.4 cases per 1,000,000 admissions. The risk of in-hospital mortality mainly depended on the number of end-organ failures.

Antimalarial and cytotoxic drugs on COVID-19 and the cardiovascular burden: Literature review and lessons to be learned.

BACKGROUND: The world is witnessing an unprecedented crisis with Coronavirus disease 2019 (COVID-19). It is important to accurately analyze the available evidence to provide correct clinical guidance for optimal patient care. We aim to discuss current clinical evidence regarding chloroquine, hydroxychloroquine, azithromycin, remdesivir, and the cardiovascular burden of COVID-19. METHODS: A literature review was performed using PubMed and Google Scholar. Additional clinical trials were identified through the "TrialsTracker" project. RESULTS: We found conflicting evidence of chloroquine, hydroxychloroquine plus azithromycin, and remdesivir in COVID-19 despite promising early reports of in vitro antiviral activity against severe acute respiratory syndrome coronavirus 2. Some of the current studies have demonstrated adverse drug reactions to chloroquine and hydroxychloroquine + azithromycin. Widespread systemic inflammation and procoagulant/hypercoagulable state, including thrombotic microangiopathy, endothelial dysfunction, bleeding disorder, and thrombosis are increasingly being witnessed in COVID-19. Evidence of cardiac injury and stroke is mostly reported in hospitalized patients; however, large specialized studies that focus on cardiac or neuropathology are lacking. DISCUSSION: There is no convincing clinical evidence of chloroquine, hydroxychloroquine with or without azithromycin, and remdesivir use in COVID-19. As evidence of systemic inflammation is rapidly unfolding, there is a dire need to maximize our resources to find the best possible solutions to the current crisis while conclusive evidence from clinical trials emerges.

Genomics of Detoxification: How Genomics can be Used for Targeting Potential Intervention and Prevention Strategies Including Nutrition for Environmentally Acquired Illness.

Due to their genomic variants, some individuals are more highly affected by toxicants than others. Toxicant metabolizing and activating variants have been linked with a wide variety of health issues including an increased risk of miscarriages, birth defects, Alzheimer's, benzene toxicity, mercury toxicity and cancer. The study of genomics allows a clinician to identify pathways that are less effective and then gives the clinician the opportunity to counsel their patients about diet, supplements and lifestyle modifications that can improve the function of these pathways or compensate to some extent for their deficits. This article will review a few of these critical pathways relating to phase I and phase 2 detox such as GSTP1, GPX1, GSTT1 deletions, PON1 and some of the CYP 450 system as examples of how an individual's genomic vulnerabilities to toxicants can be addressed by upregulating or downregulating specific pathways via genomically targeted use of foods, supplements and lifestyle changes.

Research Participants' Attitudes towards Receiving Information on Genetic Susceptibility to Arsenic Toxicity in Rural Bangladesh.

BACKGROUND: In human genetics research, it has become common practice for researchers to consider returning genetic information to participants who wish to receive it. Research participants in lower-resource settings may have barriers or competing interests that reduce the benefit or relevance of such information. Thus, the decision to return genetic information in these settings may involve special considerations of participants' interests and preferences. In this project, our goal was to assess Bangladeshi research participants' attitudes towards receiving information regarding genetic susceptibility to the effects of consuming arsenic-contaminated drinking water, a serious environmental health concern in Bangladesh and other countries. METHODS: We administered a short questionnaire to 200 individuals participating in the Health Effects of Arsenic Longitudinal Study. Associations between survey responses and participant characteristics were estimated using logistic regression. RESULTS: Overall, 100% of our participants were interested in receiving information regarding their genetic susceptibility to arsenic toxicities, and 91% indicated that being at increased genetic risk would motivate them to make efforts to reduce their exposure. Lower levels of education showed evidence of association with less concern regarding the health effects of arsenic and lower levels of motivation to reduce exposure in response to genetic information. CONCLUSIONS: Research participants in this low-resource setting appeared interested in receiving information on their genetic susceptibility to arsenic toxicity and motivated to reduce exposure in response to such information. Additional research is needed to understand how best to communicate genetic information in this population and to assess the impact of such information on individuals' behaviors and health.

How to interpret urine toxicology tests.

A 13-year-old girl presents to the emergency department for the second time with an unresponsive episode. She has a GCS (Glasgow Coma Scale) score of 11 on arrival and all other observations are normal. The story is unclear, but there are ongoing safeguarding concerns and the family are known to social services. All investigations are normal. After a period of observation on the ward, her GCS returns to normal and she appears well. Both on the first presentation and this presentation ingestion of a toxin was suspected. However, this was denied by the patient and urine toxicology screen was negative. Does this rule out toxin ingestion? Will this change your management?

Acute kidney injury and the risk of mortality in patients with methanol intoxication.

BACKGROUND: Methanol poisoning is a serious public health issue in developing countries, but few data are available in the literature on acute kidney injury (AKI) after methanol intoxication. METHODS: This study examined the clinical features, spectrum and outcomes of AKI in patients with methanol intoxication and evaluated the predictors of mortality after methanol intoxication. A total of 50 patients with methanol intoxication were seen at Chang Gung Memorial Hospital between 2000 and 2013. Patients were grouped according to the status of renal damage as AKI (n = 33) or non-AKI (n = 19). Demographic, clinical, laboratory, and mortality data were obtained for analysis. RESULTS: Most patients were middle-aged (47.8 ± 14.9 years), predominantly male (74.0%), and habitual alcohol consumers (70.0%). Most incidents were oral exposures (96.0%) and unintentional (66.0%). Two (4.0%) patients attempted suicide by intravenous injection of methanol. Five (10.0%) patients suffered methanol intoxication after ingestion of methomyl pesticide that contained methanol as a solvent. Compared to non-AKI patients, the AKI patients were older (50.9 ± 13.7 versus 41.6 ± 15.6 years, P = 0.034), predominantly male (90.9% versus 42.8%, P = 0.000), more habitual alcohol users (84.8% versus 41.2%, P = 0.001) and had more unintentional exposures (82.8% versus 35.3%, P = 0.001). Furthermore, there was a higher incidence of respiratory failure (63.6% versus 29.4%, P = 0.022) in the AKI group than in the non-AKI group, respectively. The laboratory studies revealed that the AKI patients suffered from more severe metabolic acidosis than the non-AKI patients. By the end of this study, 13 (39.5%) AKI patients and 1 (5.9%) non-AKI patient had died. The overall in-hospital hospital mortality rate was 28%. In a multivariate binary logistic regression model, it was demonstrated that AKI (odds ratio 19.670, confidence interval 1.026-377.008, P = 0.048) and Glasgow coma scale score (odds ratio 1.370, confidence interval 1.079-1.739, P = 0.010) were significant factors associated with mortality. The Kaplan-Meier analysis disclosed that AKI patients suffered lower cumulative survival than non-AKI patients (log-rank test, chi-square = 5.115, P = 0.024). CONCLUSIONS: AKI was common (66.0%) after methanol intoxication and was predictive of in-hospital hospital mortality. The development of AKI was associated with a 19.670-fold higher risk of in-hospital mortality.

DigChem: Identification of disease-gene-chemical relationships from Medline abstracts.

Chemicals interact with genes in the process of disease development and treatment. Although much biomedical research has been performed to understand relationships among genes, chemicals, and diseases, which have been reported in biomedical articles in Medline, there are few studies that extract disease-gene-chemical relationships from biomedical literature at a PubMed scale. In this study, we propose a deep learning model based on bidirectional long short-term memory to identify the evidence sentences of relationships among genes, chemicals, and diseases from Medline abstracts. Then, we develop the search engine DigChem to enable disease-gene-chemical relationship searches for 35,124 genes, 56,382 chemicals, and 5,675 diseases. We show that the identified relationships are reliable by comparing them with manual curation and existing databases. DigChem is available at http://gcancer.org/digchem.

Hydrogen Sulfide Toxicity: Mechanism of Action, Clinical Presentation, and Countermeasure Development.

INTRODUCTION: Hydrogen sulfide (H2S) is found in various settings. Reports of chemical suicide, where individuals have combined readily available household chemicals to produce lethal concentrations of H2S, have demonstrated that H2S is easily produced. Governmental agencies have warned of potential threats of use of H2S for a chemical attack, but currently there are no FDA-approved antidotes for H2S. An ideal antidote would be one that is effective in small volume, readily available, safe, and chemically stable. In this paper we performed a review of the available literature on the mechanism of toxicity, clinical presentation, and development of countermeasures for H2S toxicity. DISCUSSION: In vivo, H2S undergoes an incomplete oxidation after an exposure. The remaining non-oxidized H2S is found in dissolved and combined forms. Dissolved forms such as H2S gas and sulfhydryl anion can diffuse between blood and tissue. The combined non-soluble forms are found as acid-labile sulfides and sulfhydrated proteins, which play a role in toxicity. Recent countermeasure development takes into account the toxicokinetics of H2S. Some countermeasures focus on binding free hydrogen sulfide (hydroxocobalamin, cobinamide); some have direct effects on the mitochondria (methylene blue), while others work by mitigating end organ damage by generating other substances such as nitric oxide (NaNO2). CONCLUSION: H2S exists in two main pools in vivo after exposure. While several countermeasures are being studied for H2S intoxication, a need exists for a small-volume, safe, highly effective antidote with a long shelf life to treat acute toxicity as well as prevent long-term effects of exposure.

Getting to Remission: Differentiating Residual Symptoms From Treatment Side Effects in Major Depressive Disorder.

Despite the availability of effective treatments for MDD, many individuals have difficulty achieving remission. Residual symptoms can be difficult to differentiate from treatment side effects. Through 2 comic-based case presentations, this CME activity depicts common clinical scenarios and provides evidence-based strategies for effectively identifying and managing residual symptoms of MDD.

relSCAN - A system for extracting chemical-induced disease relation from biomedical literature.

This paper proposes an effective and robust approach for Chemical-Induced Disease (CID) relation extraction from PubMed articles. The study was performed on the Chemical Disease Relation (CDR) task of BioCreative V track-3 corpus. The proposed system, named relSCAN, is an efficient CID relation extraction system with two phases to classify relation instances from the Co-occurrence and Non-Co-occurrence mention levels. We describe the case of chemical and disease mentions that occur in the same sentence as 'Co-occurrence', or as 'Non-Co-occurrence' otherwise. In the first phase, the relation instances are constructed on both mention levels. In the second phase, we employ a hybrid feature set to classify the relation instances at both of these mention levels using the combination of two Machine Learning (ML) classifiers (Support Vector Machine (SVM) and J48 Decision tree). This system is entirely corpus dependent and does not rely on information from external resources in order to boost its performance. We achieved good results, which are comparable with the other state-of-the-art CID relation extraction systems on the BioCreative V corpus. Furthermore, our system achieves the best performance on the Non-Co-occurrence mention level.

Chemical-induced disease extraction via recurrent piecewise convolutional neural networks.

BACKGROUND: Extracting relationships between chemicals and diseases from unstructured literature have attracted plenty of attention since the relationships are very useful for a large number of biomedical applications such as drug repositioning and pharmacovigilance. A number of machine learning methods have been proposed for chemical-induced disease (CID) extraction due to some publicly available annotated corpora. Most of them suffer from time-consuming feature engineering except deep learning methods. In this paper, we propose a novel document-level deep learning method, called recurrent piecewise convolutional neural networks (RPCNN), for CID extraction. RESULTS: Experimental results on a benchmark dataset, the CDR (Chemical-induced Disease Relation) dataset of the BioCreative V challenge for CID extraction show that the highest precision, recall and F-score of our RPCNN-based CID extraction system are 65.24, 77.21 and 70.77%, which is competitive with other state-of-the-art systems. CONCLUSIONS: A novel deep learning method is proposed for document-level CID extraction, where domain knowledge, piecewise strategy, attention mechanism, and multi-instance learning are combined together. The effectiveness of the method is proved by experiments conducted on a benchmark dataset.

Chemical-induced disease relation extraction with dependency information and prior knowledge.

Chemical-disease relation (CDR) extraction is significantly important to various areas of biomedical research and health care. Nowadays, many large-scale biomedical knowledge bases (KBs) containing triples about entity pairs and their relations have been built. KBs are important resources for biomedical relation extraction. However, previous research pays little attention to prior knowledge. In addition, the dependency tree contains important syntactic and semantic information, which helps to improve relation extraction. So how to effectively use it is also worth studying. In this paper, we propose a novel convolutional attention network (CAN) for CDR extraction. Firstly, we extract the shortest dependency path (SDP) between chemical and disease pairs in a sentence, which includes a sequence of words, dependency directions, and dependency relation tags. Then the convolution operations are performed on the SDP to produce deep semantic dependency features. After that, an attention mechanism is employed to learn the importance/weight of each semantic dependency vector related to knowledge representations learned from KBs. Finally, in order to combine dependency information and prior knowledge, the concatenation of weighted semantic dependency representations and knowledge representations is fed to the softmax layer for classification. Experiments on the BioCreative V CDR dataset show that our method achieves comparable performance with the state-of-the-art systems, and both dependency information and prior knowledge play important roles in CDR extraction task.

An Integrated Multi-Label Classifier with Chemical-Chemical Interactions for Prediction of Chemical Toxicity Effects.

AIMS AND OBJECTIVE: Chemical toxicity effect is one of the major reasons for declining candidate drugs. Detecting the toxicity effects of all chemicals can accelerate the procedures of drug discovery. However, it is time-consuming and expensive to identify the toxicity effects of a given chemical through traditional experiments. Designing quick, reliable and non-animal-involved computational methods is an alternative way. METHOD: In this study, a novel integrated multi-label classifier was proposed. First, based on five types of chemical-chemical interactions retrieved from STITCH, each of which is derived from one aspect of chemicals, five individual classifiers were built. Then, several integrated classifiers were built by integrating some or all individual classifiers. RESULT AND CONCLUSION: By testing the integrated classifiers on a dataset with chemicals and their toxicity effects in Accelrys Toxicity database and non-toxic chemicals with their performance evaluated by jackknife test, an optimal integrated classifier was selected as the proposed classifier, which provided quite high prediction accuracies and wide applications.