Predictors of Acute Liver Failure in Patients With Acute Hepatitis A: An Analysis of the 2016-2018 San Diego County Hepatitis A Outbreak.

Background:Between 2016 and 2018, San Diego County experienced a hepatitis A outbreak with a historically high mortality rate (3.4%) that highlighted the need for early recognition of those at risk of developing acute liver failure (ALF). Methods:A retrospective case series of adult hospitalized patients with acute hepatitis A. Results:One hundred six patients with hepatitis A were studied, of whom 11 (10.4%) developed ALF, of whom 7 (6.6%) died. A history of alcohol abuse, hyperbilirubinemia, hypoalbuminemia, hyponatremia, and anemia were associated with increased odds of developing ALF. Initial Maddrey's and Model of End-Stage Liver Disease Sodium (MELD-Na) scores were also associated with the development of ALF. Multivariable analysis showed that a higher initial MELD-Na score (odds ratio [OR], 1.205; 95% confidence interval [CI], 1.018-1.427) and a lower initial serum albumin concentration (OR, 9.35; 95% CI, 1.15-76.9) were associated with increased odds of developing ALF. Combining serum albumin and MELD-Na (SAM; C-statistic, 0.8878; 95% CI, 0.756-0.988) yielded a model that was not better than either serum albumin (C-statistic, 0.852; 95% CI, 0.675-0.976) or MELD-Na (C-statistic, 0.891; 95% CI, 0.784-0.968; P = .841). Finally, positive blood cultures were more common among patients with ALF compared with those without ALF (63.6% vs 4.3%; P < .00001). Conclusions:Hypoalbuminemia was associated with an increased risk of ALF in patients with acute hepatitis A. Positive blood cultures and septic shock as a cause of death were common among patients with ALF. Providers caring for patients with acute hepatitis A should monitor for early signs of sepsis and consider empiric antibiotics, especially in patients presenting with hypoalbuminemia

Serendipitous Discovery of Light-Induced \u3cem\u3e(In Situ)\u3c/em\u3e Formation of An Azo-Bridged Dimeric Sulfonated Naphthol as a Potent PTP1B Inhibito

Background Protein tyrosine phosphatases (PTPs) like dual specificity phosphatase 5 (DUSP5) and protein tyrosine phosphatase 1B (PTP1B) are drug targets for diseases that include cancer, diabetes, and vascular disorders such as hemangiomas. The PTPs are also known to be notoriously difficult targets for designing inihibitors that become viable drug leads. Therefore, the pipeline for approved drugs in this class is minimal. Furthermore, drug screening for targets like PTPs often produce false positive and false negative results. Results Studies presented herein provide important insights into: (a) how to detect such artifacts, (b) the importance of compound re-synthesis and verification, and (c) how in situ chemical reactivity of compounds, when diagnosed and characterized, can actually lead to serendipitous discovery of valuable new lead molecules. Initial docking of compounds from the National Cancer Institute (NCI), followed by experimental testing in enzyme inhibition assays, identified an inhibitor of DUSP5. Subsequent control experiments revealed that this compound demonstrated time-dependent inhibition, and also a time-dependent change in color of the inhibitor that correlated with potency of inhibition. In addition, the compound activity varied depending on vendor source. We hypothesized, and then confirmed by synthesis of the compound, that the actual inhibitor of DUSP5 was a dimeric form of the original inhibitor compound, formed upon exposure to light and oxygen. This compound has an IC50 of 36 μM for DUSP5, and is a competitive inhibitor. Testing against PTP1B, for selectivity, demonstrated the dimeric compound was actually a more potent inhibitor of PTP1B, with an IC50 of 2.1 μM. The compound, an azo-bridged dimer of sulfonated naphthol rings, resembles previously reported PTP inhibitors, but with 18-fold selectivity for PTP1B versus DUSP5. Conclusion We report the identification of a potent PTP1B inhibitor that was initially identified in a screen for DUSP5, implying common mechanism of inhibitory action for these scaffolds

Cross-species transmission potential between wild pigs, livestock, poultry, wildlife, and humans: implications for disease risk management in North America

Cross-species disease transmission between wildlife, domestic animals and humans is an increasing threat to public and veterinary health. Wild pigs are increasingly a potential veterinary and public health threat. Here we investigate 84 pathogens and the host species most at risk for transmission with wild pigs using a network approach. We assess the risk to agricultural and human health by evaluating the status of these pathogens and the co-occurrence of wild pigs, agriculture and humans. We identified 34 (87%) OIE listed swine pathogens that cause clinical disease in livestock, poultry, wildlife, and humans. On average 73% of bacterial, 39% of viral, and 63% of parasitic pathogens caused clinical disease in other species. Non-porcine livestock in the family Bovidae shared the most pathogens with swine (82%). Only 49% of currently listed OIE domestic swine diseases had published wild pig surveillance studies. The co-occurrence of wild pigs and farms increased annually at a rate of 1.2% with as much as 57% of all farms and 77% of all agricultural animals co-occurring with wild pigs. The increasing co-occurrence of wild pigs with livestock and humans along with the large number of pathogens shared is a growing risk for cross-species transmission

A–C Estrogens as Potent and Selective Estrogen Receptor-Beta Agonists (SERBAs) to Enhance Memory Consolidation under Low-Estrogen Conditions

Estrogen receptor-beta (ERβ) is a drug target for memory consolidation in postmenopausal women. Herein is reported a series of potent and selective ERβ agonists (SERBAs) with in vivo efficacy that are A–C estrogens, lacking the B and D estrogen rings. The most potent and selective A–C estrogen is selective for activating ER relative to seven other nuclear hormone receptors, with a surprising 750-fold selectivity for the β over α isoform and with EC50s of 20–30 nM in cell-based and direct binding assays. Comparison of potency in different assays suggests that the ER isoform selectivity is related to the compound’s ability to drive the productive conformational change needed to activate transcription. The compound also shows in vivo efficacy after microinfusion into the dorsal hippocampus and after intraperitoneal injection (0.5 mg/kg) or oral gavage (0.5 mg/kg). This simple yet novel A–C estrogen is selective, brain penetrant, and facilitates memory consolidation

Calibration of the APEX Model to Simulate Management Practice Effects on Runoff, Sediment, and Phosphorus Loss

Process-based computer models have been proposed as a tool to generate data for Phosphorus (P) Index assessment and development. Although models are commonly used to simulate P loss from agriculture using managements that are different from the calibration data, this use of models has not been fully tested. The objective of this study is to determine if the Agricultural Policy Environmental eXtender (APEX) model can accurately simulate runoff, sediment, total P, and dissolved P loss from 0.4 to 1.5 ha of agricultural fields with managements that are different from the calibration data. The APEX model was calibrated with field-scale data from eight different managements at two locations (management-specific models). The calibrated models were then validated, either with the same management used for calibration or with different managements. Location models were also developed by calibrating APEX with data from all managements. The management-specific models resulted in satisfactory performance when used to simulate runoff, total P, and dissolved P within their respective systems, with r2 \u3e 0.50, Nash– Sutcliffe efficiency \u3e 0.30, and percent bias within ±35% for runoff and ±70% for total and dissolved P. When applied outside the calibration management, the management-specific models only met the minimum performance criteria in one-third of the tests. The location models had better model performance when applied across all managements compared with management-specific models. Our results suggest that models only be applied within the managements used for calibration and that data be included from multiple management systems for calibration when using models to assess management effects on P loss or evaluate P Indices

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images

Tree-mycorrhizal associations detected remotely from canopy spectral properties

A central challenge in global ecology is the identification of key functional processes in ecosystems that scale, but do not require, data for individual species across landscapes. Given that nearly all tree species form symbiotic relationships with one of two types of mycorrhizal fungi – arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) fungi – and that AM- and ECM-dominated forests often have distinct nutrient economies, the detection and mapping of mycorrhizae over large areas could provide valuable insights about fundamental ecosystem processes such as nutrient cycling, species interactions, and overall forest productivity. We explored remotely sensed tree canopy spectral properties to detect underlying mycorrhizal association across a gradient of AM- and ECM-dominated forest plots. Statistical mining of reflectance and reflectance derivatives across moderate/high-resolution Landsat data revealed distinctly unique phenological signals that differentiated AM and ECM associations. This approach was trained and validated against measurements of tree species and mycorrhizal association across ~130 000 trees throughout the temperate United States. We were able to predict 77% of the variation in mycorrhizal association distribution within the forest plots (P \u3c 0.001). The implications for this work move us toward mapping mycorrhizal association globally and advancing our understanding of biogeochemical cycling and other ecosystem processes

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers