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Background and Purpose We compared the outcomes of endovascular therapy (EVT) in an extended time window in patients with large-vessel occlusion (LVO) between patients with and without pre-stroke disability. Methods In this prespecified analysis of the multinational CT for Late Endovascular Reperfusion study (66 participating sites, 10 countries between 2014 and 2022), we analyzed data from patients with acute ischemic stroke with a pre-stroke modified Rankin Scale (mRS) score of 0–4 and LVO who underwent EVT 6–24 hours from the time last seen well. The primary outcome was the composite of functional independence (FI; mRS score 0–2) or return to the pre-stroke mRS score (return of Rankin, RoR) at 90 days. Outcomes were compared between patients with pre-stroke disability (pre-stroke mRS score 2–4) and those without (mRS score 0–1). Results A total of 2,231 patients (median age, 72 years; median National Institutes of Health Stroke Scale score, 16) were included in the present analysis. Of these, 564 (25%) had pre-stroke disability. The primary outcome (FI or RoR) was observed in 30.7% of patients with pre-stroke disability (FI, 16.5%; RoR, 30.7%) compared to 44.1% of patients without (FI, 44.1%; RoR, 13.0%) (P

BackgroundThe primary objective is to evaluate the safety and effectiveness of Stryker second generation Target® Nano Coils in the treatment of ruptured and unruptured small (

Background Recent randomized clinical trials have demonstrated that endovascular therapy for basilar artery occlusion is safe and potentially effective, predominantly in the non‐White population. The aim of this study was to identify predictors of good functional outcome in posterior circulation strokes in US population after mechanical thrombectomy from the TRACK (Trevo Stent‐Retriever Acute Stroke) and the NASA (North American Solitaire Stent Retriever Acute Stroke) registries from North America. Methods Patient‐level data from the TRACK and NASA registries were pooled, and patients with posterior circulation stroke were included in this analysis. Patients were dichotomized into those with 90‐day good functional outcome (modified Rankin scale [mRS] score 0–2) and poor functional outcome (mRS score ≥3). Baseline and procedural data were compared between the 2 cohorts. Multivariate logistic regression was performed to identify predictors of functional outcome. P

Objective: To characterize the development and performance of a cataract surgery episode-based cost measure for the Medicare Quality Payment Program. Design: Claims-based analysis. Participants: Medicare clinicians with cataract surgery claims between June 1, 2016, and May 31, 2017. Methods: We limited the analysis to claims with procedure code 66984 (routine cataract surgery), excluding cases with relevant ocular comorbidities. We divided episodes into subgroups by surgery location (Ambulatory Surgery Center [ASC] or Hospital Outpatient Department [HOPD]) and laterality (bilateral when surgeries were within 30 days apart). For the episode-based cost measure, we calculated costs occurring between 60 days before surgery and 90 days after surgery, limited to services identified by an expert committee as related to cataract surgery and under the influence of the cataract surgeon. We attributed costs to the clinician submitting the cataract surgery claim, categorized costs into clinical themes, and calculated episode cost distribution, reliability in detecting clinician-dependent cost variation, and costs with versus without complications. We compared episode-based cost scores with hypothetical “nonselective” cost scores (total Medicare beneficiary costs between 60 days before surgery and 90 days after surgery). Main Outcome Measures: Episode costs with and without complications, clinician-dependent variation (proportion of total cost variance), and proportion of costs from cataract surgery-related clinical themes. Results: We identified 583 356 cataract surgery episodes attributed to 10 790 clinicians and 8189 with ≥ 10 episodes during the measurement period. Most surgeries were performed in an ASC (71%) and unilateral (66%). The mean episode cost was $2876. The HOPD surgeries had higher costs; geography and episodes per clinician did not substantially affect costs. The proportion of cost variation from clinician-dependent factors was higher in episode-based compared with nonselective cost measures (94% vs. 39%), and cataract surgery-related clinical themes represented a higher proportion of total costs for episode-based measures. Episodes with complications had higher costs than episodes without complications ($3738 vs. $2276). Conclusions: The cataract surgery episode-based cost measure performs better than a comparable nonselective measure based on cost distribution, clinician-dependent variance, association with cataract surgery-related clinical themes, and quality alignment (higher costs in episodes with complications). Cost measure maintenance and refinement will be important to maintain clinical validity and reliability. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Infection at barrier sites, e.g., skin, activates local immune defenses that limit pathogen spread, while preserving tissue integrity. Phenotypically distinct γδ T cell populations reside in skin, where they shape immunity to cutaneous infection prior to onset of an adaptive immune response by conventional αβ CD4+ (TCD4+) and CD8+ (TCD8+) T cells. To examine the mechanisms used by γδ T cells to control cutaneous virus replication and tissue pathology, we examined γδ T cells after infection with vaccinia virus (VACV). Resident γδ T cells expanded and combined with recruited γδ T cells to control pathology after VACV infection. However, γδ T cells did not play a role in control of local virus replication or blockade of systemic virus spread. We identified a unique wound healing signature that has features common to, but also features that antagonize, the sterile cutaneous wound healing response. Tissue repair generally occurs after clearance of a pathogen, but viral wound healing started prior to the peak of virus replication in the skin. γδ T cells contributed to wound healing through induction of multiple cytokines/growth factors required for efficient wound closure. Therefore, γδ T cells modulate the wound healing response following cutaneous virus infection, maintaining skin barrier function to prevent secondary bacterial infection.

Introduction The advent of mobile stroke units (MSU) provide a golden opportunity to rapidly reverse anticoagulant related intracranial hemorrhage (AC‐ICH) in the pre‐hospital setting. The timely reversal of anticoagulants is crucial in halting hematoma expansion and achieving good clinical outcomes. It is known that AC‐ICH is associated with higher mortality rate and higher hematoma expansion than spontaneous ICH. Multiple studies showed that rapid reversal of AC‐ICH is associated with better outcomes and more stability of the hematoma size. Studies showed there is delay in initiation of therapy and revere the anticoagulants in ICH cases, [1,2}. Here, we report 3 cases of AC‐ICH diagnosed on the MSU with pre‐hospital use of reversal agents. To our knowledge, this is the first case series of Non vitamin K antagonist oral anticoagulants (NOACS) on the MSU. Methods We reviewed the MSU data from May 2016 to December 2020 and those with AC‐ICH were selected. Baseline demographics and clinical presentation were obtained including clinical outcome. Results A total of 3 patients were identified. Patient 1 is a96 year‐oldfemale with history of Atrial fibrillation (AF) that presented with AC‐ICH related to Apixaban and was treated with Prothrombin complex concentrate (PCC). The time of MSU examination to treatment (MSUe‐T) was 41 minutes.The patient was neurologically intact and discharged home after 4 days, no further images were indicated as the patient was stable and back to baseline. Patient 2 is a68 year‐oldmale with history of deep vein thrombosis that presented with AC‐ICH related to Warfarin and was treated with PCC. TheMSUe‐T was 64 minutes. Repeated CT head in the ED was stable. Neurological exam improved and the patient discharged to a skilled nursing facility. Patient 3 is a79 year oldmale with history of AF that presented with AC‐ICH related to Rivaroxaban and was treated with PCC. TheMSUe‐T was 50 minutes. Repeated CT head after 3 days was stable. According to FASTEST trial, the average time from onset to treatment of AC‐ICH was 90–120 min.In this trial, the growth of the ICH during the first hours was associated with increase mortality rate,{3} Conclusions Our data demonstrated that management of AC‐ICH on the MSU is feasible and provides an ample opportunity to reverse AC in the hyper‐early stage of ICH. MSU might have an important role in reducing the mortality for AC‐ICH patients. Larger comparative studies between Emergency department and MSU are needed.

Introduction Simultaneous extracranial‐intracranial management of Tandem lesion in stroke (Seimless technique) is well published and important technique in treating tandem lesions in anterior circulation. However, this technique has been rarely used in the posterior circulation. We present the case of a 70 year‐old male presented with coma and was found to have right vertebral artery origin stenosis with super‐imposed thrombus and tandem occlusion of the basilar artery. We used endovascular Seimless technique successfully with TICI‐3 reperfusion score and good clinical outcome. To our knowledge, this is the first case report for endovascularSeimless technique used in the post circulation. Methods We reviewed our endovascular mechanical thrombectomy data from May 2019 to July 2022 and those with post circulation tandem occlusion were selected. Baseline demographics and clinical presentation were obtained including clinical outcome. Results 70 year‐old male with history of high blood pressure presented with state of coma was found to have right vertebral artery origin stenosis with superimposed pedunculated thrombus and tandem distal third Basilar artery occlusion. Patient underwent simultaneous extracranial‐intracranial management of tandem lesions (Seimless technique) through right trans radial basilar artery thrombectomy with stent retriever and aspiration as well as right vertebral artery origin aspiration and balloon angioplasty over the wire of the stent retriever. Following day, the patient underwent right trans‐radial vertebral artery origin stenosis stenting. Patient’s neurological exam was improved from NIHSS 30 to 0 at the time of discharge. We performed Basilar artery thrombectomy using Rist catheter, Red 62 reperfusion catheter, Rebar microcatheter and Solitaire stent retriever4 mm x 41 mm. After deploying the stent retriever, we advanced sterling balloon 4 mm x 20 mm over the stent retrieverwire and performed optimal balloon angioplasty. Conclusions This particular technique of treating intracranial and extracranial lesion simultaneously for posterior circulation hasn’t been reported in the literature. By presenting this case, we want to conclude that in selective posterior circulation tandem occlusion strokes “Seimless technique” can be used effectively and safely without many complications.

Background Endovascular management of wide‐necked bifurcation aneurysms poses a therapeutic challenge, as coiling often requires the use of multiple adjunctive stent constructs to achieve successful embolization without compromising parent vessel integrity. The Neuroform Atlas Stent System is a novel low‐profile, intraluminal remodeling device. The study aims to investigate the safety and efficacy of Y‐stent–assisted coiling configuration constructs using the next generation stent for aneurysm coil embolization. Methods Subjects undergoing Y‐stent–assisted coiling in the ATLAS IDE (Safety and Effectiveness of the Treatment of Wide Neck, Saccular Intracranial Aneurysms with the Neuroform Atlas Stent System Investigational Device Exception) trial were identified for subgroup analysis. Enrollment in the trial was not stratified on the basis of the procedural stenting technique used. The primary efficacy end point was complete aneurysm occlusion (Raymond–Roy class 1) on 12‐month angiography, in the absence of re‐treatment or parent artery stenosis (>50%) at the target location. The primary safety end point was any major stroke or ipsilateral stroke or neurological death within 12 months. Adjudication of the primary end points was performed by an independent imaging core laboratory and the clinical events committee. Results A total of 60 subjects were identified. The mean age was 59, and 28.3% were men. The median aneurysm size was 6.7 mm, with a median neck size of 4.3 mm. Frequently reported aneurysm locations included the basilar apex (56.7%), anterior communicating artery (20.0%), and middle cerebral artery bifurcation (11.7%). The composite primary efficacy end point was achieved in 81.1% of subjects. Overall, 1.7% of subjects experienced a primary safety endpoint of major ipsilateral stroke or neurological death. Conclusion In the ATLAS IDE aneurysm cohort premarket approval study, Y‐stent–assisted coiling with Neuroform demonstrated high rates of complete aneurysm occlusion at 12 months, with low rates of morbidity.

Rationale & Objective: Patients with a high-risk Apolipoprotein L1 (APOL1) genotype are more likely to develop chronic kidney disease and kidney failure. It is unclear whether this increased risk is entirely mediated by the development of proteinuria. Study Design: Retrospective observational study of the African American Study of Kidney Disease and Hypertension cohort and Chronic Renal Insufficiency Cohort. Exposures & Predictors: Self-identified race (Black/non-Black) and presence of high-risk APOL1 genotype. The primary model was adjusted for age, sex, diabetes, estimated glomerular filtration rate, and urinary protein-creatinine ratio. Outcomes: Time to kidney failure defined as time to dialysis or transplantation. Analytical Approach: We used Cox proportional hazard models to study how proteinuria mediates the association between APOL1 and kidney failure. We modeled proteinuria at baseline and as a time-varying covariate. Results: A high-risk APOL1 genotype was associated with a significantly higher risk of kidney failure, even for patients with minimal proteinuria (HR, 1.87; 95% CI, 1.23-2.84). The association was not significant among patients with high proteinuria (HR, 1.22; 95% CI, 0.93-1.61). When modeling proteinuria as a time-varying covariate, a high-risk APOL1 genotype was associated with higher kidney failure risk even among patients who never developed proteinuria (HR, 2.04; 95% CI, 1.10-3.77). Compared to non-Black patients, Black patients without the high-risk genotype did not have higher risk of kidney failure (HR, 0.96; 95% CI, 0.85-1.10). Limitations: Two datasets were combined to increase statistical power. Limited generalizability beyond the study cohorts. Residual confounding common to observational studies. Conclusions: A high-risk APOL1 genotype is significantly associated with increased kidney failure risk, especially among patients without baseline proteinuria. Although our results suggest that the risk is partially mediated through proteinuria, higher kidney failure risk was present even among patients who never developed proteinuria. Providers should consider screening for the high-risk APOL1 genotype, especially among Black patients without proteinuria in populations with chronic kidney disease.

Abstract Background Gamma-aminobutyric acid type A (GABAA) receptors mainly mediate the effects of gamma-aminobutyric acid, which is the primary inhibitory neurotransmitter in the central nervous system. Abundant evidence suggests that GABAA receptors play a key role in sleep-regulating processes. No genetic association study has explored the relationships between GABAA receptor genes and sleep duration, sleep quality, and sleep timing in humans. Methods We determined the association between single-nucleotide polymorphisms (SNPs) in the GABAA receptor genes GABRA1, GABRA2, GABRB3, GABRA5, and GABRG3 and sleep duration, sleep quality, and sleep timing in the Taiwan Biobank with a sample of 10,127 Taiwanese subjects. There were 10,142 subjects in the original study cohort. We excluded 15 subjects with a medication history of sedative-hypnotics. Results Our data revealed an association of the GABRB3-GABRA5-GABRG3 gene cluster with sleep duration, which has not been previously identified: rs79333046 (beta = − 0.07; P = 1.21 × 10–3) in GABRB3, rs189790076 (beta = 0.92; P = 1.04 × 10–3) in GABRA5, and rs147619342 (beta = − 0.72; P = 3.97 × 10–3) in GABRG3. The association between rs189790076 in GABRA5 and sleep duration remained significant after Bonferroni correction. A variant (rs12438141) in GABRB3 was also found to act as a potential expression quantitative trait locus. Additionally, we discovered interactions between variants in the GABRB3-GABRA5-GABRG3 gene cluster and lifestyle factors, such as tea and coffee consumption, smoking, and physical activity, that influenced sleep duration, although some interactions became nonsignificant after Bonferroni correction. We also found interactions among GABRB3, GABRA5, and GABRG3 that affected sleep duration. Furthermore, we identified an association of rs7165524 (beta = − 0.06; P = 2.20 × 10–3) in GABRA5 with sleep quality and an association of rs79465949 (beta = − 0.12; P = 3.95 × 10–3) in GABRB3 with sleep timing, although these associations became nonsignificant after Bonferroni correction. However, we detected no evidence of an association of individual SNPs in GABRA1 and GABRA2. Conclusions Our results indicate that rs189790076 in GABRA5 and gene–gene interactions among GABRB3, GABRA5, and GABRG3 may contribute to sleep duration in the Taiwanese population.

Abstract Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.

Abstract It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection.

Abstract Background Standard treatment for locally advanced anal squamous cell carcinoma (SCC) consists of concurrent chemoradiation. We evaluated whether racial differences exist in the receipt of standard treatment and its association with survival. Methods From the National Cancer Database, we identified patients diagnosed with anal SCC (Stages 2–3) between 2004 and 2015. Using logistic regression, we evaluated racial differences in the probability of receiving standard chemoradiation. We used Cox proportional hazards models to evaluate associations between race, receipt of standard therapy and survival. Results Our analysis included 19,835 patients. Patients receiving standard chemoradiation had better survival than patients receiving nonstandard therapy (hazard ratio [HR] 0.64; 95% confidence interval [CI] 0.61–0.68; p

Abstract Background Single-cell RNA sequencing (scRNA-seq) is an emerging technology that can assess the function of an individual cell and cell-to-cell variability at the single cell level in an unbiased manner. Dimensionality reduction is an essential first step in downstream analysis of the scRNA-seq data. However, the scRNA-seq data are challenging for traditional methods due to their high dimensional measurements as well as an abundance of dropout events (that is, zero expression measurements). Results To overcome these difficulties, we propose DR-A (Dimensionality Reduction with Adversarial variational autoencoder), a data-driven approach to fulfill the task of dimensionality reduction. DR-A leverages a novel adversarial variational autoencoder-based framework, a variant of generative adversarial networks. DR-A is well-suited for unsupervised learning tasks for the scRNA-seq data, where labels for cell types are costly and often impossible to acquire. Compared with existing methods, DR-A is able to provide a more accurate low dimensional representation of the scRNA-seq data. We illustrate this by utilizing DR-A for clustering of scRNA-seq data. Conclusions Our results indicate that DR-A significantly enhances clustering performance over state-of-the-art methods.

Background: Brain-derived neurotrophic factor (BDNF), the most abundant and widely expressed neurotrophin in the brain, is believed to play an important role in depression and anxiety. In this study, we hypothesized that single-nucleotide polymorphisms (SNPs) within the BDNF gene should be linked with depression and anxiety through complex interactions in the general population. Methods: We analyzed 7,098 Taiwanese participants from the Taiwan Biobank. Measures of anxiety and depression were evaluated using the Patient Health Questionnaire-4 (PHQ-4). We used 43 BDNF polymorphisms in the genetic analysis. Results: We found a nominal association between nine BDNF genetic variants and depression state, after having adjusted for the factors of age and gender. Furthermore, three BDNF SNPs showed evidence of nominal association with PHQ-4 scores. In addition, an interaction significantly existed between the BDNF rs73430670 and BDNF rs4923461 in influencing depression state (p < 0.05). Finally, we found that influence of interaction significantly existed between BDNF rs12418745 and physical activity (p < 0.05) in depression state. Conclusion: Those results suggest that the BDNF genetic variants may contribute to psychological distress independently as well as through SNP-SNP and gene-physical activity interactions in the general population.

Background: Vascular endothelial growth factor A (VEGFA) and brain-derived neurotrophic factor (BDNF), the most abundant and widely expressed neurotrophic factors in the brain, are believed to play an important rôle in depression and anxiety. In this study, we hypothesized that single-nucleotide polymorphisms (SNPs) within the VEGFA and BDNF genes would be linked with psychological distress through complex interactions in the general population. Methods: We analyzed 7,098 Taiwanese subjects from the Taiwan Biobank. Measures of anxiety and depression were evaluated using the Patient Health Questionnaire-4 (PHQ-4). Totally, 15 VEGFA and 43 BDNF polymorphisms were used in the genetic analysis. Results: In our analysis, an interaction was found between the VEGFA rs10434 and BDNF rs12418745 in significantly influencing depression state (p < 0.01). In addition, we found that influence of interaction existed between physical activity and VEGFA genetic variants including rs3025000, rs699947, rs833068, rs833069, rs3024998, and rs3025006 in depression state. But we found no association between 15 VEGFA genetic variants and depression state, after adjusting for age and gender. Furthermore, no VEGFA SNPs showed evidence of association with PHQ-4 scores. Conclusion: These results suggest that the VEGFA and BDNF genetic variants may contribute to psychological distress through gene-gene and gene-physical activity interactions in the general population.

The serotonin [5-hydroxytryptamine (5-HT)] system has been implicated in the pathogenesis of major depressive disorder (MDD). Among the 5-HT receptor subtypes, 5-HT2 is one of the major pharmacological therapeutic targets for MDD. There have been inconsistent findings in previous pharmacogenetic studies investigating the antidepressant therapeutic response using one or several 5-HT2A (HTR2A) genetic polymorphisms. By using gene-based association analysis, we hope to identify genetic variants of HTR2A which are related to MDD susceptibility and its antidepressant therapeutic response. 288 HTR2A single nucleotide polymorphisms in MDD susceptibility have been investigated through a case–control (455 MDD patients and 2, 998 healthy controls) study, as well as in antidepressant efficacy (n = 455) in our current research. The 21-item Hamilton Rating Scale for Depression was used to evaluate measures of antidepressant therapeutic efficacy. From two MDD groups in the antidepressant therapeutic response, by using gene-based analyses, we have identified 14 polymorphisms as suggestive markers for therapeutic response (13 for remission and 1 for response) in both meta- and mega-analyses. All of these HTR2A reported polymorphisms did not reach statistical significance in the case–control association study. This current investigation supported the link between HTR2A variants and antidepressant therapeutic response in MDD but not with MDD susceptibility.

BackgroundAdults with chronic kidney disease (CKD) are hospitalized more frequently than those without CKD, but the magnitude of this excess morbidity and the factors associated with hospitalizations are not well known.Methods and findingsData from 3,939 participants enrolled in the Chronic Renal Insufficiency Cohort (CRIC) Study between 2003 and 2008 at 7 clinical centers in the United States were used to estimate primary causes of hospitalizations, hospitalization rates, and baseline participant factors associated with all-cause, cardiovascular, and non-cardiovascular hospitalizations during a median follow up of 9.6 years. Multivariable-adjusted Poisson regression was used to identify factors associated with hospitalization rates, including demographics, blood pressure, estimated glomerular filtration rate (eGFR), and proteinuria. Hospitalization rates in CRIC were compared with rates in the Nationwide Inpatient Sample (NIS) from 2012. Of the 3,939 CRIC participants, 45.1% were female, and 41.9% identified as non-Hispanic black, with a mean age of 57.7 years, and the mean eGFR is 44.9 ml/min/1.73m2. CRIC participants had an unadjusted overall hospitalization rate of 35.0 per 100 person-years (PY) [95% CI: 34.3 to 35.6] and 11.1 per 100 PY [95% CI: 10.8 to 11.5] for cardiovascular-related causes. All-cause, non-cardiovascular, and cardiovascular hospitalizations were associated with older age (≥65 versus 45 to 64 years), more proteinuria (≥150 to 65 years. Limitations of the study include possible misclassification by diagnostic codes, residual confounding, and potential bias from healthy volunteer effect due to its observational nature.ConclusionsIn this study, we observed that adults with CKD had a higher hospitalization rate than the general population that is hospitalized, and even moderate reductions in kidney function were associated with elevated rates of hospitalization. Causes of hospitalization were predominantly related to cardiovascular disease, but other causes contributed, particularly, genitourinary, digestive, and endocrine, nutritional, and metabolic illnesses. High levels of proteinuria were observed to have the largest association with hospitalizations across a wide range of kidney function levels.

In the wake of recent advances in artificial intelligence research, precision psychiatry using machine learning techniques represents a new paradigm. The D-amino acid oxidase (DAO) protein and its interaction partner, the D-amino acid oxidase activator (DAOA, also known as G72) protein, have been implicated as two key proteins in the N-methyl-D-aspartate receptor (NMDAR) pathway for schizophrenia. Another potential biomarker in regard to the etiology of schizophrenia is melatonin in the tryptophan catabolic pathway. To develop an ensemble boosting framework with random undersampling for determining disease status of schizophrenia, we established a prediction approach resulting from the analysis of genomic and demographic variables such as DAO levels, G72 levels, melatonin levels, age, and gender of 355 schizophrenia patients and 86 unrelated healthy individuals in the Taiwanese population. We compared our ensemble boosting framework with other state-of-the-art algorithms such as support vector machine, multilayer feedforward neural networks, logistic regression, random forests, naive Bayes, and C4.5 decision tree. The analysis revealed that the ensemble boosting model with random undersampling [area under the receiver operating characteristic curve (AUC) = 0.9242 ± 0.0652; sensitivity = 0.8580 ± 0.0770; specificity = 0.8594 ± 0.0760] performed maximally among predictive models to infer the complicated relationship between schizophrenia disease status and biomarkers. In addition, we identified a causal link between DAO and G72 protein levels in influencing schizophrenia disease status. The study indicates that the ensemble boosting framework with random undersampling may provide a suitable method to establish a tool for distinguishing schizophrenia patients from healthy controls using molecules in the NMDAR and tryptophan catabolic pathways.

IntroductionDuring the Covid-19 pandemic, major journals have published a significant number of Covid-19 related articles in a short period of time. While this is necessary to combat the worldwide pandemic, it may have trade-offs with respect to publishing research from other disciplines.ObjectivesTo assess differences in published research design before and after the Covid-19 pandemic.MethodsWe performed a cross-sectional review of all 322 full-length research studies published between October 1, 2019 and April 30, 2020 in three major medical journals. We compared the number of randomized controlled trials (RCTs) and studies with a control group before and after January 31, 2020, when Covid-19 began garnering international attention.ResultsThe number of full-length research studies per issue was not statistically different before and after the Covid-19 pandemic (from 3.7 to 3.5 per issue, p = 0.17). Compared to before January 31, 2020, 0.7 fewer non-Covid-19 studies per issue were published versus after January 31, 2020 (pLimitationsUsing a simple heuristic for assessing research design and lack of generalizability to the general medical literature.ConclusionsIn summary, the increase in Covid-19 studies coincided with a decrease of mostly non-Covid-19 RCTs.

Abstract It is well established that brain-derived neurotrophic factor (BDNF) signaling pathway plays a key role in the pathophysiology of major depressive disorder (MDD) and in therapeutic mechanisms of antidepressants. We aim to identify genetic vairiants related to MDD susceptibility and antidepressant therapeutic response by using gene-based association analysis with genes related to the neurotrophic pathway. The present study investigated the role of genetic variants in the 10 neurotrophic-related genes (BDNF, NGFR, NTRK2, MTOR, VEGFA, S100A10, SERPINE1, ARHGAP33, GSK3B, CREB1) in MDD susceptibility through a case-control (455 MDD patients and 2,998 healthy controls) study and in antidepressant efficacy (n = 455). Measures of antidepressant therapeutic efficacy were evaluated using the 21-item Hamilton Rating Scale for Depression. Our single-marker and gene-based analyses with ten genes related to the neurotrophic pathway identified 6 polymorphisms that reached a significant level (p-value

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Interactions between the peripheral nervous system and resident macrophages (MMs) modulate intestinal homeostatic functions. Activation of β2-adrenergic receptors on MMs has been shown to reduce bacterial challenges. These MMs are also crucial for the development of bowel inflammation in postoperative ileus (POI), an iatrogenic, noninfectious inflammation-based motility disorder. However, the role of the sympathetic nervous system (SNS) in the immune modulation of these MMs during POI or other noninfectious diseases is largely unknown. By employing 6-OHDA-induced denervation, we investigated the changes in the muscularis externa by RNA-seq, quantitative PCR, and flow cytometry. Further, we performed transcriptional phenotyping of sorted CX3CR1+ MMs and ex vivo LPS/M-CSF stimulation on these MMs. By combining denervation with a mouse POI model, we explored distinct changes on CX3CR1+ MMs as well as in the muscularis externa and their functional outcome during POI. Our results identify SNS as an important mediator in noninfectious postoperative inflammation. Upon denervation, MMs anti-inflammatory genes were reduced, and the muscularis externa profile is shaped toward a proinflammatory status. Further, denervation reduced MMs anti-inflammatory genes also in the early phase of POI. Finally, reduced leukocyte infiltration into the muscularis led to a quicker recovery of bowel motility in the late phase of POI.

Evidence suggests that the neuro-inflammation mechanisms associated with interleukin-12 (IL-12) may be linked to Alzheimer's diseases and cognitive aging. In this study, we speculate that single nucleotide polymorphisms (SNPs) in IL-12-associated genes, such as IL12A, IL12B, IL12RB1, and IL12RB2 genes, could be associated with cognitive aging individually and/or via complicated interactions in the elder Taiwanese population. There were totally 3,730 Taiwanese individuals with age ≥60 years from the Taiwan Biobank. Mini-Mental State Examination (MMSE) was analyzed for all participants. We employed MMSE scores to assess cognitive functions. Our analysis revealed that the IL12A gene (including rs116910715, rs78902931, and rs78569420), the IL12B gene (including rs730691), and the IL12RB2 gene (including rs3790558, rs4655538, rs75699623, and rs1874396) were associated with cognitive aging. Among these SNPs, the association with the IL12RB2 rs3790558 SNP remained significant after performing Bonferroni correction (P = 6.87 × 10−4). Additionally, we found that interactions between the IL12A and IL12RB2 genes influenced cognitive aging (P = 0.022). Finally, we pinpointed the effects of interactions between IL12A, IL12B, and IL12RB2 with physical activity (P < 0.001, = 0.002, and < 0.001, respectively). Our study suggests that the IL-12-associated genes may contribute to susceptibility to cognitive aging independently as well as through gene-gene and gene-physical activity interactions.

The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings.

In the wake of recent advances in machine learning research, the study of pharmacogenomics using predictive algorithms serves as a new paradigmatic application. In this work, our goal was to explore an ensemble machine learning approach which aims to predict probable antidepressant treatment response and remission in major depressive disorder (MDD). To discover the status of antidepressant treatments, we established an ensemble predictive model with a feature selection algorithm resulting from the analysis of genetic variants and clinical variables of 421 patients who were treated with selective serotonin reuptake inhibitors. We also compared our ensemble machine learning framework with other state-of-the-art models including multi-layer feedforward neural networks (MFNNs), logistic regression, support vector machine, C4.5 decision tree, naïve Bayes, and random forests. Our data revealed that the ensemble predictive algorithm with feature selection (using fewer biomarkers) performed comparably to other predictive algorithms (such as MFNNs and logistic regression) to derive the perplexing relationship between biomarkers and the status of antidepressant treatments. Our study demonstrates that the ensemble machine learning framework may present a useful technique to create bioinformatics tools for discriminating non-responders from responders prior to antidepressant treatments.

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

In the wake of recent advances in scientific research, personalized medicine using deep learning techniques represents a new paradigm. In this work, our goal was to establish deep learning models which distinguish responders from non-responders, and also to predict possible antidepressant treatment outcomes in major depressive disorder (MDD). To uncover relationships between the responsiveness of antidepressant treatment and biomarkers, we developed a deep learning prediction approach resulting from the analysis of genetic and clinical factors such as single nucleotide polymorphisms (SNPs), age, sex, baseline Hamilton Rating Scale for Depression score, depressive episodes, marital status, and suicide attempt status of MDD patients. The cohort consisted of 455 patients who were treated with selective serotonin reuptake inhibitors (treatment-response rate = 61.0%; remission rate = 33.0%). By using the SNP dataset that was original to a genome-wide association study, we selected 10 SNPs (including ABCA13 rs4917029, BNIP3 rs9419139, CACNA1E rs704329, EXOC4 rs6978272, GRIN2B rs7954376, LHFPL3 rs4352778, NELL1 rs2139423, NUAK1 rs2956406, PREX1 rs4810894, and SLIT3 rs139863958) which were associated with antidepressant treatment response. Furthermore, we pinpointed 10 SNPs (including ARNTL rs11022778, CAMK1D rs2724812, GABRB3 rs12904459, GRM8 rs35864549, NAALADL2 rs9878985, NCALD rs483986, PLA2G4A rs12046378, PROK2 rs73103153, RBFOX1 rs17134927, and ZNF536 rs77554113) in relation to remission. Then, we employed multilayer feedforward neural networks (MFNNs) containing 1–3 hidden layers and compared MFNN models with logistic regression models. Our analysis results revealed that the MFNN model with 2 hidden layers (area under the receiver operating characteristic curve (AUC) = 0.8228 ± 0.0571; sensitivity = 0.7546 ± 0.0619; specificity = 0.6922 ± 0.0765) performed maximally among predictive models to infer the complex relationship between antidepressant treatment response and biomarkers. In addition, the MFNN model with 3 hidden layers (AUC = 0.8060 ± 0.0722; sensitivity = 0.7732 ± 0.0583; specificity = 0.6623 ± 0.0853) achieved best among predictive models to predict remission. Our study indicates that the deep MFNN framework may provide a suitable method to establish a tool for distinguishing treatment responders from non-responders prior to antidepressant therapy.

Background and purposeAs the fourth mobile stroke unit (MSU) in the nation, and the first 24/7 unit worldwide, we review our initial experience with the Mercy Health MSU and institutional protocols implemented to facilitate rapid treatment of acute stroke patients and field triage for patients suffering other time-sensitive, acute neurologic emergencies in Lucas County, Ohio, and the greater Toledo metropolitan area.MethodsData was prospectively collected for all patients transported and treated by the MSU during the first 6 months of service. Data was abstracted from documentation of on-scene emergency medical services (EMS) personnel, critical care nurses, and onboard physicians, who participated through telemedicine.ResultsThe MSU was dispatched 248 times and transported 105 patients after on-scene examination with imaging. Intravenous (IV) tissue plasminogen activator (tPA) was administered to 10 patients; 8 patients underwent successful endovascular therapy after a large vessel occlusion was identified using CT performed within the MSU without post treatment symptomatic hemorrhage. Moreover, 14 patients were treated with IV anti-epileptics for status epilepticus, and 19 patients received IV anti-hypertensive agents for malignant hypertension. MSU alarm to on-scene times and treatment times were 34.7 min (25–49) and 50.6 min (44.4–56.8), respectively.ConclusionThe world’s first 24/7 MSU has been successfully implemented with IV-tPA administration rates and times comparable to other MSUs nation-wide, while demonstrating rapid triage and treatment in the field for neurologic emergencies, including status epilepticus. With the rising number of MSUs worldwide, further data will drive standardized protocols that can be adopted nationwide by EMS.

BACKGROUND:Multidisciplinary care (MDC) programs have been proposed as a way to alleviate the cost and morbidity associated with chronic kidney disease (CKD) in the US. METHODS AND FINDINGS:We assessed the cost-effectiveness of a theoretical Medicare-based MDC program for CKD compared to usual CKD care in Medicare beneficiaries with stage 3 and 4 CKD between 45 and 84 years old in the US. The program used nephrologists, advanced practitioners, educators, dieticians, and social workers. From Medicare claims and published literature, we developed a novel deterministic Markov model for CKD progression and calibrated it to long-term risks of mortality and progression to end-stage renal disease. We then used the model to project accrued discounted costs and quality-adjusted life years (QALYs) over patients' remaining lifetime. We estimated the incremental cost-effectiveness ratio (ICER) of MDC, or the cost of the intervention per QALY gained. MDC added 0.23 (95% CI: 0.08, 0.42) QALYs over usual care, costing $51,285 per QALY gained (net monetary benefit of $23,100 at a threshold of $150,000 per QALY gained; 95% CI: $6,252, $44,323). In all subpopulations analyzed, ICERs ranged from $42,663 to $72,432 per QALY gained. MDC was generally more cost-effective in patients with higher urine albumin excretion. Although ICERs were higher in younger patients, MDC could yield greater improvements in health in younger than older patients. MDC remained cost-effective when we decreased its effectiveness to 25% of the base case or increased the cost 5-fold. The program costed less than $70,000 per QALY in 95% of probabilistic sensitivity analyses and less than $87,500 per QALY in 99% of analyses. Limitations of our study include its theoretical nature and being less generalizable to populations at low risk for progression to ESRD. We did not study the potential impact of MDC on hospitalization (cardiovascular or other). CONCLUSIONS:Our model estimates that a Medicare-funded MDC program could reduce the need for dialysis, prolong life expectancy, and meet conventional cost-effectiveness thresholds in middle-aged to elderly patients with mild to moderate CKD.

With ever-increasing elder populations, age-related cognitive decline, which is characterized as a gradual decline in cognitive capacity in the aging process, has turned out to be a mammoth public health concern. Since genetic information has become increasingly important to explore the biological mechanisms of cognitive decline, the search for genetic biomarkers of cognitive aging has received much attention. There is growing evidence that single-nucleotide polymorphisms (SNPs) within the ADAMTS9, BDNF, CASS4, COMT, CR1, DNMT3A, DTNBP1, REST, SRR, TOMM40, circadian clock, and Alzheimer’s diseases-associated genes may contribute to susceptibility to cognitive aging. In this review, we first illustrated evidence of the genetic contribution to disease susceptibility to age-related cognitive decline in recent studies ranging from approaches of candidate genes to genome-wide association studies. We then surveyed a variety of association studies regarding age-related cognitive decline with consideration of gene–gene and gene–environment interactions. Finally, we highlighted their limitations and future directions. In light of advances in precision medicine and multi-omics technologies, future research in genomic medicine promises to lead to innovative ideas that are relevant to disease prevention and novel drugs for cognitive aging.

Evidence indicates that the pathophysiologic mechanisms associated with insulin resistance may contribute to cognitive aging and Alzheimer's diseases. In this study, we hypothesize that single nucleotide polymorphisms (SNPs) within insulin resistance-associated genes, such as the ADAM metallopeptidase with thrombospondin type 1 motif 9 (ADAMTS9), glucokinase regulator (GCKR), and peroxisome proliferator activated receptor gamma (PPARG) genes, may be linked with cognitive aging independently and/or through complex interactions in an older Taiwanese population. A total of 547 Taiwanese subjects aged over 60 years from the Taiwan Biobank were analyzed. Mini-Mental State Examinations (MMSE) were administered to all subjects, and MMSE scores were used to measure cognitive functions. Our data showed that four SNPs (rs73832338, rs9985304, rs4317088, and rs9831846) in the ADAMTS9 gene were significantly associated with cognitive aging among the subjects (P = 1.5 x 10-6 ~ 0.0002). This association remained significant after performing Bonferroni correction. Additionally, we found that interactions between the ADAMTS9 rs9985304 and ADAMTS9 rs76346246 SNPs influenced cognitive aging (P < 0.001). However, variants in the GCKR and PPARG genes had no association with cognitive aging in our study. Our study indicates that the ADAMTS9 gene may contribute to susceptibility to cognitive aging independently as well as through SNP-SNP interactions.

Increased risk of developing metabolic syndrome (MetS) has been associated with the circadian clock genes. In this study, we assessed whether 29 circadian clock-related genes (including ADCYAP1, ARNTL, ARNTL2, BHLHE40, CLOCK, CRY1, CRY2, CSNK1D, CSNK1E, GSK3B, HCRTR2, KLF10, NFIL3, NPAS2, NR1D1, NR1D2, PER1, PER2, PER3, REV1, RORA, RORB, RORC, SENP3, SERPINE1, TIMELESS, TIPIN, VIP, and VIPR2) are associated with MetS and its individual components independently and/or through complex interactions in a Taiwanese population. We also analyzed the interactions between environmental factors and these genes in influencing MetS and its individual components. A total of 3,000 Taiwanese subjects from the Taiwan Biobank were assessed in this study. Metabolic traits such as waist circumference, triglyceride, high-density lipoprotein cholesterol, systolic and diastolic blood pressure, and fasting glucose were measured. Our data showed a nominal association of MetS with several single nucleotide polymorphisms (SNPs) in five key circadian clock genes including ARNTL, GSK3B, PER3, RORA, and RORB; but none of these SNPs persisted significantly after performing Bonferroni correction. Moreover, we identified the effect of GSK3B rs2199503 on high fasting glucose (P = 0.0002). Additionally, we found interactions among the ARNTL rs10832020, GSK3B rs2199503, PER3 rs10746473, RORA rs8034880, and RORB rs972902 SNPs influenced MetS (P < 0.001 ~ P = 0.002). Finally, we investigated the influence of interactions between ARNTL rs10832020, GSK3B rs2199503, PER3 rs10746473, and RORB rs972902 with environmental factors such as alcohol consumption, smoking status, and physical activity on MetS and its individual components (P < 0.001 ~ P = 0.002). Our study indicates that circadian clock genes such as ARNTL, GSK3B, PER3, RORA, and RORB genes may contribute to the risk of MetS independently as well as through gene-gene and gene-environment interactions.

Background/Aims: All-trans retinoic acid (ATRA), the active form of vitamin A, plays an important role in the growth arrest of numerous types of cancer cells. It has been indicated that cyclin-dependent kinase 5 (Cdk5) activity can be affected by ATRA treatment. Our previous results demonstrate the involvement of Cdk5 in the fate of prostate cancer cells. The purpose of this study is to examine whether Cdk5 is involved in ATRA-induced growth arrest of the castration-resistant cancer cell line DU145 through up-regulating Cdk inhibitor protein, p27. Methods: DU145 cells were treated with ATRA, and cell proliferation, protein expression, and protein localization of Cdk5/p27 were examined. Cell proliferation and cell cycle distribution were also determined under Cdk5 inhibition induced by inhibitor or knockdown. Results: ATRA treatment inhibited DU145 cell proliferation and significantly increased p27 expression through Cdk5 up-regulation. Immunocytochemical data showed that a Cdk5 inhibitor reduced ATRA-triggered nuclear distribution of p27 in DU145 cells. The proliferation inhibition and G1 phase accumulation of DU145 cells were significantly increased by ATRA treatment, whereas Cdk5 inhibitor and siRNA could reverse these effects. Conclusions: Our results demonstrate that ATRA induced growth inhibition in castration-resistant prostate cancer cells through activating Cdk5 and p27. We hope this finding will increase the knowledge of prostate cancer treatment and can be applied in patients' nutritional control in the future.

Cyclin-dependent kinase 5 (CDK5) is a unique member of the cyclin-dependent kinase family. CDK5 is activated by binding with its regulatory proteins, mainly p35, and its activation is essential in the development of the central nervous system (CNS) and neurodegeneration. Recently, it has been reported that CDK5 plays important roles in regulating various biological and pathological processes, including cancer progression. Concerning prostate cancer, the androgen receptor (AR) is majorly involved in tumorigenesis, while CDK5 can phosphorylate AR and promotes the proliferation of prostate cancer cells. Clinical evidence has also shown that the level of CDK5 is associated with the progression of prostate cancer. Interestingly, inhibition of CDK5 prevents prostate cancer cell growth, while drug-triggered CDK5 hyperactivation leads to apoptosis. The blocking of CDK5 activity by its small interfering RNAs (siRNA) or Roscovitine, a pan-CDK inhibitor, reduces the cellular AR protein level and triggers the death of prostate cancer cells. Thus, CDK5 plays a crucial role in the growth of prostate cancer cells, and AR regulation is one of the important pathways. In this review paper, we summarize the significant studies on CDK5-mediated regulation of prostate cancer cells. We propose that the CDK5−p35 complex might be an outstanding candidate as a diagnostic marker and potential target for prostate cancer treatment in the near future.

The genetic influence of single nucleotide polymorphisms (SNPs) on antidepressant efficacy has been previously demonstrated. To evaluate whether there are ethnic differences, we compared the allele frequencies of antidepressant efficacy-related SNPs between the Taiwanese population and four other populations in the HapMap database. We recruited 198 Taiwanese major depression patients and 106 Taiwanese controls. A panel of possible relevant SNPs (in brain-derived neurotrophic factor, 5-hydroxytryptamine receptor 2A, interleukin 1 beta, and G-protein beta 3 subunit genes) was selected for comparisons of allele frequencies using the χ2 test. Our results suggested no difference between Taiwanese patients and controls, but there were significant differences among Taiwanese controls and the other four ethnic groups in brain-derived neurotrophic factor, 5-hydroxytryptamine receptor 2A, interleukin 1 beta and G-protein beta 3 subunit genes. We conclude that there are ethnic differences in the allele frequencies of antidepressant efficacy-related SNPs, and that the degree of variations is consistent with geographic distances. Further investigation is required to verify the attribution of genetic differences to ethnic-specific antidepressant responses.

Wan-Sheng Ke1, Yuchi Hwang2, Eugene Lin21Department of Internal Medicine, Kuang Tien General Hospital, Taichung County, Taiwan; 2Vita Genomics, Inc., Jung-Shing Road, Wugu Shiang, Taipei, TaiwanAbstract: Chronic hepatitis C (CHC) patients often stop pursuing interferon-alfa and ribavirin (IFN-alfa/RBV) treatment because of the high cost and associated adverse effects. It is highly desirable, both clinically and economically, to establish tools to distinguish responders from nonresponders and to predict possible outcomes of the IFN-alfa/RBV treatments. Single nucleotide polymorphisms (SNPs) can be used to understand the relationship between genetic inheritance and IFN-alfa/RBV therapeutic response. The aim in this study was to establish a predictive model based on a pharmacogenomic approach. Our study population comprised Taiwanese patients with CHC who were recruited from multiple sites in Taiwan. The genotyping data was generated in the high-throughput genomics lab of Vita Genomics, Inc. With the wrapper-based feature selection approach, we employed multilayer feedforward neural network (MFNN) and logistic regression as a basis for comparisons. Our data revealed that the MFNN models were superior to the logistic regression model. The MFNN approach provides an efficient way to develop a tool for distinguishing responders from nonresponders prior to treatments. Our preliminary results demonstrated that the MFNN algorithm is effective for deriving models for pharmacogenomics studies and for providing the link from clinical factors such as SNPs to the responsiveness of IFN-alfa/RBV in clinical association studies in pharmacogenomics.Keywords: chronic hepatitis C, artificial neural networks, interferon, pharmacogenomics, ribavirin, single nucleotide polymorphisms

Eugene Lin, Rajiv Padmanabhan, Majaz MoonisDepartment of Neurology, University of Massachusetts Medical School and UMass Memorial Medical Center, Worcester, Massachusetts, USAIntroduction: Antiplatelet therapy remains one of the cornerstones in the management of noncardioembolic ischemic stroke. However, a significant percentage of patients have concomitant gastroesophageal reflux or peptic ulcer disease that requires acid-reducing medications, the most powerful and effective being the proton pump inhibitors (PPIs). Antiplatelet efficacy, at least in vivo, and particularly for clopidogrel, has been shown to be reduced with concomitant proton pump inhibitor use. Whether this is clinically relevant is not clear from the limited studies available.Methods: We conducted an extensive review of studies available on Medline related to pharmacodynamic interactions between the antiplatelet medications and proton pump inhibitors as well as clinical studies that addressed this potential interaction.Results: Based on the present pharmacodynamic and clinical studies we did not find a significant interaction that would reduce the efficacy of antiplatelet agents with concomitant user of proton pump inhibitors.Conclusions: Patients on antiplatelet agents after a transient ischemic attack or ischemic stroke can safely use aspirin, and extended release dipyridamole/aspirin with proton pump inhibitors. Patients on clopidogrel may use other acid-reducing drugs besides proton pump inhibitors. In rare cases where proton pump inhibitors and clopidogrel have to be used concurrently, careful close monitoring for recurrent vascular events is required.Keywords: proton pump inhibitors, antiplatelet medications, clopidogrel, ischemic stroke, cardiovascular events

Eugene Lin1,7, Po See Chen2,6,7, Lung-Cheng Huang3,4, Sen-Yen Hsu51Vita Genomics, Inc., Wugu Shiang, Taipei, Taiwan; 2Department of Psychiatry, Hospital and College of Medicine, National Cheng Kung University, Tainan, Taiwan; 3Department of Psychiatry, National Taiwan University Hospital Yun-Lin Branch, Taiwan; 4Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan; 5Department of Psychiatry, Chi Mei Medical Center, Liouying, Tainan, Taiwan; 6Department of Psychiatry, National Cheng Kung University Hospital, Dou-liou Branch, Yunlin, Taiwan; 7These authors contributed equally to this workAbstract: Major depressive disorder (MDD) is one of the most common mental disorders worldwide. Single nucleotide polymorphisms (SNPs) can be used in clinical association studies to determine the contribution of genes to drug efficacy. A common SNP in the brain-derived neurotrophic factor (BDNF) gene, a methionine (Met) substitution for valine (Val) at codon 66 (Val66Met), is a candidate SNP for influencing antidepressant treatment outcome. In this study, our goal was to determine the relationship between the Val66Met polymorphism in the BDNF gene and the rapid antidepressant response to venlafaxine in a Taiwanese population with MDD. Overall, the BDNF Val66Met polymorphism was found not to be associated with short-term venlafaxine treatment outcome. However, the BDNF Val66Met polymorphism showed a trend to be associated with rapid venlafaxine treatment response in female patients. Future research with independent replication in large sample sizes is needed to confirm the role of the BDNF Val66Met polymorphism identified in this study.Keywords: antidepressant response, brain-derived neurotrophic factor, major depressive disorder, serotonin and norepinephrine reuptake inhibitor, single nucleotide polymorphisms

Naïve anti-viral CD8+ T cells (TCD8+) are activated by the presence of peptide-MHC Class I complexes (pMHC-I) on the surface of professional antigen presenting cells (pAPC). Increasing the number of pMHC-I in vivo can increase the number of responding TCD8+. Antigen can be presented directly or indirectly (cross presentation) from virus-infected and uninfected cells, respectively. Here we determined the relative importance of these two antigen presenting pathways in mousepox, a natural disease of the mouse caused by the poxvirus, ectromelia (ECTV). We demonstrated that ECTV infected several pAPC types (macrophages, B cells, and dendritic cells (DC), including DC subsets), which directly presented pMHC-I to naïve TCD8+ with similar efficiencies in vitro. We also provided evidence that these same cell-types presented antigen in vivo, as they form contacts with antigen-specific TCD8+. Importantly, the number of pMHC-I on infected pAPC (direct presentation) vastly outnumbered those on uninfected cells (cross presentation), where presentation only occurred in a specialized subset of DC. In addition, prior maturation of DC failed to enhance antigen presentation, but markedly inhibited ECTV infection of DC. These results suggest that direct antigen presentation is the dominant pathway in mice during mousepox. In a broader context, these findings indicate that if a virus infects a pAPC then the presentation by that cell is likely to dominate over cross presentation as the most effective mode of generating large quantities of pMHC-I is on the surface of pAPC that endogenously express antigens. Recent trends in vaccine design have focused upon the introduction of exogenous antigens into the MHC Class I processing pathway (cross presentation) in specific pAPC populations. However, use of a pantropic viral vector that targets pAPC to express antigen endogenously likely represents a more effective vaccine strategy than the targeting of exogenous antigen to a limiting pAPC subpopulation.