Evaluation of direct thrombin inhibitors during a critical heparin shortage

Journal of Thrombosis and Thrombolysis - A recent heparin shortage related to an outbreak of African Swine Flu in China led to substantial increase in the use of direct thrombin inhibitors (DTI) as...     

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.     

Prevalence and potential predictors of gambling disorder among people living with HIV

Specific subsets of the adult population are at an increased risk of problem gambling behaviors. Previous research among these subsets has found increased rates of disordered gambling among those with drug use, alcohol use, mood, anxiety, and personality disorders. To what extent this may apply to the HIV population, known to have a high burden of co-occurring substance use and mental disorders, is not known The current study also examined the effectiveness of The Brief Biosocial Gambling Screen (BBGS) for the diagnosis of gambling disorder.

This study examined the prevalence of gambling behaviors and disordered gambling in patients enrolled in an urban HIV clinic. 100 people living with HIV (PLWH) were assessed on gambling behaviors, impulsivity, and criterion on disordered gambling. Screening for gambling disorder using the BBGS was compared to the American Psychiatric Association DSM-5 criterion.

The mean age was 53, 44% were female, 60% completed high school or above, and 80% self-identified as unemployed/disabled. 13% met four or more criteria for gambling disorder according to DSM-5 criteria. Participants that met criteria were more likely to report marijuana (p?=?.044) and heroin (p?=?.002) use, and greater impulsivity (p?<?0.00001) when compared to participants who did not meet criteria. The BBGS was able to effectively screen individuals for disordered gambling with a sensitivity of 100%, specificity of 90.8%, positive predictive value of 56.2%, and a negative predictive value of 100%.

These results suggest that urban HIV clinics may need to consider actively screening for gambling disorders, and referring to appropriate counseling and treatment for those who screen positive.     

CD4 memory T cells divide poorly in response to antigen because of their cytokine profile

Immunological memory is a hallmark of adaptive immunity, and understanding T cell memory will be central to the development of effective cell-mediated vaccines. The characteristics and functions of CD4 memory cells have not been well defined. Here we demonstrate that the increased size of the secondary response is solely a consequence of the increased antigen-specific precursor frequency within the memory pool. Memory cells proliferated less than primary responding cells, even within the same host. By analyzing the entry of primary and memory cells into the cell cycle, we found that the two populations proliferated similarly until day 5; after this time, fewer of the reactivated memory cells proliferated. At this time, fewer of the reactivated memory cells made IL-2 than primary responding cells, but more made IFNγ. Both these factors affected the low proliferation of the memory cells, because either exogenous IL-2 or inhibition of IFNγ increased the proliferation of the memory cells.     

Amyloid and metabolic positron emission tomography imaging of cognitively normal adults with Alzheimer's parents

This study examines the relationship between fibrillar beta-amyloid (Aβ) deposition and reduced glucose metabolism, a proxy for neuronal dysfunction, in cognitively normal (NL) individuals with a parent affected by late-onset Alzheimer's disease (AD). Forty-seven 40–80-year-old NL received positron emission tomography (PET) with ¹¹C-Pittsburgh compound B (PiB) and 18F-fluoro-2-deoxy-d-glucose (FDG). These included 19 NL with a maternal history (MH), 12 NL with a paternal history (PH), and 16 NL with negative family history of AD (NH). Automated regions of interest, statistical parametric mapping, voxel-wise intermodality correlations, and logistic regressions were used to examine cerebral-to-cerebellar PiB and FDG standardized uptake value ratios across groups. The MH group showed higher PiB retention and lower metabolism in AD regions compared with NH and PH, which were negatively correlated in posterior cingulate, frontal, and parieto-temporal regions (Pearson r ≤ −0.57, p ≤ 0.05). No correlations were observed in NH and PH. The combination of Aβ deposition and metabolism yielded accuracy ≥ 69% for MH vs. NH and ≥ 71% for MH vs. PH, with relative risk = 1.9–5.1 (p values < 0.005). NL individuals with AD-affected mothers show co-occurring Aβ increases and hypometabolism in AD-vulnerable regions, suggesting an increased risk for AD.     

Interaction of Blood Manganese Concentrations with GSTT1 in Relation to Autism Spectrum Disorder in Jamaican Children

Journal of Autism and Developmental Disorders - Using data from 266 age- and sex-matched pairs of Jamaican children with autism spectrum disorder (ASD) and typically developing (TD) controls...     

Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk

BackgroundUsing novel data mining methods such as natural language processing (NLP) on electronic health records (EHRs) for screening and detecting individuals at risk for psychosis. MethodThe study included all patients receiving a first index diagnosis of nonorganic and nonpsychotic mental disorder within the South London and Maudsley (SLaM) NHS Foundation Trust between January 1, 2008, and July 28, 2018. Least Absolute Shrinkage and Selection Operator (LASSO)-regularized Cox regression was used to refine and externally validate a refined version of a five-item individualized, transdiagnostic, clinically based risk calculator previously developed (Harrell’s C = 0.79) and piloted for implementation. The refined version included 14 additional NLP-predictors: tearfulness, poor appetite, weight loss, insomnia, cannabis, cocaine, guilt, irritability, delusions, hopelessness, disturbed sleep, poor insight, agitation, and paranoia. ResultsA total of 92 151 patients with a first index diagnosis of nonorganic and nonpsychotic mental disorder within the SLaM Trust were included in the derivation (n = 28 297) or external validation (n = 63 854) data sets. Mean age was 33.6 years, 50.7% were women, and 67.0% were of white race/ethnicity. Mean follow-up was 1590 days. The overall 6-year risk of psychosis in secondary mental health care was 3.4 (95% CI, 3.3–3.6). External validation indicated strong performance on unseen data (Harrell’s C 0.85, 95% CI 0.84–0.86), an increase of 0.06 from the original model. ConclusionsUsing NLP on EHRs can considerably enhance the prognostic accuracy of psychosis risk calculators. This can help identify patients at risk of psychosis who require assessment and specialized care, facilitating earlier detection and potentially improving patient outcomes.