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Background Understanding how to modulate the microenvironment of tumors that are resistant to immune checkpoint inhibitors represents a major challenge in oncology.Here we investigate the ability of USP7 inhibitors to reprogram the tumor microenvironment (TME) by inhibiting secretion of vascular endothelial growth factor (VEGF) from fibroblasts. Methods To understand the role played by USP7 in the TME, we systematically evaluated the effects of potent, selective USP7 inhibitors on co‐cultures comprising components of the TME, using human primary cells. We also evaluated the effects of USP7 inhibition on tumor growth inhibition in syngeneic models when dosed in combination with immune checkpoint inhibitors (ICIs). Results Abrogation of VEGF secretion from fibroblasts in response to USP7 inhibition resulted in inhibition of tumor neoangiogenesis and increased tumor recruitment of CD8‐positive T‐lymphocytes, leading to significantly improved sensitivity to immune checkpoint inhibitors. In syngeneic models, treatment with USP7 inhibitors led to striking tumor responses resulting in significantly improved survival. Conclusions USP7‐mediated reprograming of the TME is not linked to its previously characterized role in modulating MDM2 but does require p53 and UHRF1 in addition to the well‐characterized VEGF transcription factor, HIF‐1α. This represents a function of USP7 that is unique to fibroblasts, and which is not observed in cancer cells or other components of the TME. Given the potential for USP7 inhibitors to transform “immune desert” tumors into “immune responsive” tumors, this paves the way for a novel therapeutic strategy combining USP7 inhibitors with immune checkpoint inhibitors (ICIs). The oral USP7 inhibitor, ADC‐159, reduces sVEGF from CAFs and impacts tumor vasculature. USP7 inhibition affects HIF‐1α transcriptional modulation, tumor hypoxia and remodeling of the tumor microenvironment creating a permissive immune micro‐climate for infiltrating lymphocytes turning immunologically ‘cold’ tumors, ‘hot’. In preclinical models, combination treatment of ADC‐159 with immunotherapy agents delivers improved anti‐tumor efficacy and survival.

Initial estimates of macroeconomic variables based on incomplete source data can be unreliable. Because of the methodology used by reporting agencies and the presence of reporting errors in the survey data, I argue that initial-released output estimates tend to be irrational and unreliable under uncertainty. Using U.S. nominal and real output real time data from 1985 to 2014 and the Economic Policy Uncertainty (EPU) index proposed by Baker et al. (2013), I investigate the impact of economic policy uncertainty on aggregate output data revisions, modeling the output revisions, and the effect of output data revision on inflation forecasts. In Chapter 2, I find a strong evidence of asymmetric impact of the uncertainty on rationality of the initial-released output data. Also, the results show that the magnitudes of output data revisions tend to be larger when the uncertainty is greater. The out-of-sample predictions indicate that the ability of the EPU index on forecasting the revisions is superior to that of business-cycle indicators suggested by previous study. Chapter 3 analyzes the nature of the output data revisions by applying a common factor model and a large set of information variables (approximately 200 macroeconomic variables) suggested by Giannone, Reichlin, and Small (2008). The results show that the common factors track the revisions quite well. In particular, these factors are able to capture the huge downward revisions of aggregate output during the subprime mortgage crisis in late 2008. Using the common factors as a robustness check for examining the rationality of the initial-released output data, I find that the results in some of previous studies are likely to have omitted-variable bias. Chapter 4 applies the findings in Chapter 2 to literature examining the impact of output data revision on inflation forecasts. The results show that the difference between the forecasting performance of the use of fully revised output gap estimates and that of the real-time output estimates tends to be greater during periods of high uncertainty. This finding implies that previous empirical results on examining the inflation-output gap relationship using unrevised data released during the periods of high uncertainty are likely to be special cases that are not representative of all vintages of the data.

To evaluate the relationships among patient characteristics, irradiation treatment planning parameters, and treatment toxicity of acute radiation dermatitis (RD) after breast hybrid intensity modulation radiation therapy (IMRT). The study cohort consisted of 95 breast cancer patients treated with hybrid IMRT. RD grade ≥2 (2+) toxicity was defined as clinically significant. Patient characteristics and the irradiation treatment planning parameters were used as the initial candidate factors. Prognostic factors were identified using the least absolute shrinkage and selection operator (LASSO)-based normal tissue complication probability (NTCP) model. A univariate cut-off dose NTCP model was developed to find the dose-volume limitation. Fifty-two (54.7%) of ninety-five patients experienced acute RD grade 2+ toxicity. The volume of skin receiving a dose >35 Gy (V35) was the most significant dosimetric predictor associated with RD grade 2+ toxicity. The NTCP model parameters for V35Gy were TV50 = 85.7 mL and γ50 = 0.77, where TV50 was defined as the volume corresponding to a 50% incidence of complications, and γ50 was the normalized slope of the volume-response curve. Additional potential predictive patient characteristics were energy and surgery, but the results were not statistically significant. To ensure a better quality of life and compliance for breast hybrid IMRT patients, the skin volume receiving a dose >35 Gy should be limited to <85.7 mL to keep the incidence of RD grade 2+ toxicities below 50%. To avoid RD toxicity, the volume of skin receiving a dose >35 Gy should follow sparing tolerance and the inherent patient characteristics should be considered.

BackgroundTimely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores.ResultsThe study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores.ConclusionsIt is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.

Cognitive dysfunction is one of the core symptoms in schizophrenia, and it is predictive of functional outcomes and therefore useful for treatment targets. Rather than improving cognitive deficits, currently available antipsychotics mainly focus on positive symptoms, targeting dopaminergic/serotoninergic neurons and receptors in the brain. Apart from investigating the neural mechanisms underlying schizophrenia, emerging evidence indicates the importance of glial cells in brain structure development and their involvement in cognitive functions. Although the etiopathology of astrocytes in schizophrenia remains unclear, accumulated evidence reveals that alterations in gene expression and astrocyte products have been reported in schizophrenic patients. To further investigate the role of astrocytes in schizophrenia, we highlighted recent progress in the investigation of the effect of astrocytes on abnormalities in glutamate transmission and impairments in the blood–brain barrier. Recent advances in animal models and behavioral methods were introduced to examine schizophrenia-related cognitive deficits and negative symptoms. We also highlighted several experimental tools that further elucidate the role of astrocytes. Instead of focusing on schizophrenia as a neuron-specific disorder, an additional astrocytic perspective provides novel and promising insight into its causal mechanisms and treatment. The involvement of astrocytes in the pathogenesis of schizophrenia and other brain disorders is worth further investigation.

Abstract Background and Hypothesis Schizophrenia presents significant treatment challenges, particularly due to medication resistance observed in some patients receiving antipsychotics. Emerging research suggests a potential link between impaired reinforcement learning, the severity of psychotic symptoms, and dopamine system abnormalities. Exploring reinforcement learning in therapeutic settings could provide critical insights into the efficacy of antipsychotic treatments. This study aimed to investigate whether neurocognitive profiles, specifically choice strategies and model fitting parameters assessed using the Dynamic Reward Task (DRT), could provide insights into treatment response variability among patients with schizophrenia. Study Design We conducted a comprehensive neurocognitive assessment on chronic schizophrenia patients experiencing psychotic relapse, categorized by treatment response (high-response vs low-response). Participants underwent DRT, Wisconsin Card Sorting Test (WCST), and Continuous Performance Test (CPT) to evaluate reward processing, executive function, and sustained attention, respectively. We employed statistical analyses to compare task performance between groups and assess changes before and after antipsychotic treatment. Study Results We identified significant differences in treatment effects across different response groups in DRT scores, choice strategies, and model-fitting parameters. Conversely, all schizophrenia groups had consistent abnormalities on the WCST and CPT evaluations compared to controls. Conclusions Our findings highlight the efficacy of DRT, WCST, and CPT in delineating neurocognitive profiles relevant to treatment response in schizophrenia. Specifically, the DRT effectively differentiated between high- and low-response patients. Distinct deficits in reward processing and executive function identified here may serve as potential indicators, informing personalized treatment strategies tailored to individual responses to antipsychotic medication.

Epilepsy is a common neurological disorder that affects millions of people worldwide. Patients with epilepsy generally require long-term antiepileptic therapy and many of them receive polypharmacy. Certain medications, including older-generation antiepileptic drugs, have been known to predispose patients to developing diabetes. Although data mining techniques have become widely used in healthcare, they have seldom been applied in this clinical problem. Here, the authors used association rule mining to discover drugs or drug combinations that may be associated with newly diagnosed diabetes. Their findings indicate in addition to the most common culprits such as phenytoin and valproic acid, prescriptions containing carbamazepine, oxcarbazepine, or lamotrigine may be related to the development of newly diagnosed diabetes. These mined rules are useful as guidance to both clinical practice and future research.

Neuronal oscillations at beta frequencies (20-50 Hz) in the cortico-basal ganglia circuits have long been the leading theory for bradykinesia, the slow movements that are cardinal symptoms in Parkinson's disease (PD). The beta oscillation theory helped to drive a frequency-based design in the development of deep brain stimulation therapy for PD. However, in contrast to this theory, here we have found that bradykinesia can be completely dissociated from beta oscillations in rodent models. Instead, we observed that bradykinesia is causatively regulated by the burst-firing pattern of the subthalamic nucleus (STN) in a feed-forward, or efferent-only, mechanism. Furthermore, STN burst-firing and beta oscillations are two independent mechanisms that are regulated by different NMDA receptors in STN. Our results shift the understanding of bradykinesia pathophysiology from an interactive oscillatory theory toward a feed-forward mechanism that is coded by firing patterns. This distinct mechanism may improve understanding of the fundamental concepts of motor control and enable more selective targeting of bradykinesia-specific mechanisms to improve PD therapy.

Parkinson's disease (PD) is the most prevalent hypokinetic movement disorder, and symptomatic PD pathogenesis has been ascribed to imbalances between the direct and indirect pathways in the basal ganglia circuitry. Here, we applied glutamate receptor blockers to the subthalamic nucleus (STN) of parkinsonian rats and evaluated locomotor behaviors via single-unit and local-field recordings. Using this model, we found that inhibition of NMDAergic cortico-subthalamic transmission ameliorates parkinsonian motor deficits without eliciting any vivid turning behavior and abolishes electrophysiological abnormalities, including excessive subthalamic bursts, cortico-subthalamic synchronization, and in situ beta synchronization in both the motor cortex and STN. Premotor cortex stimulation revealed that cortico-subthalamic transmission is deranged in PD and directly responsible for the excessive stimulation-dependent bursts and time-locked spikes in the STN, explaining the genesis of PD-associated pathological bursts and synchronization, respectively. Moreover, application of a dopaminergic agent via a microinfusion cannula localized the therapeutic effect to the STN, without correcting striatal dopamine deficiency. Finally, optogenetic overactivation and synchronization of cortico-subthalamic transmission alone sufficiently and instantaneously induced parkinsonian-associated locomotor dysfunction in normal mice. In addition to the classic theory emphasizing the direct-indirect pathways, our data suggest that deranged cortico-subthalamic transmission via the NMDA receptor also plays a central role in the pathophysiology of parkinsonian motor deficits.

Electronic medical records (EMRs) have been used extensively in most medical institutions for more than a decade in Taiwan. However, information overload associated with rapid accumulation of large amounts of clinical narratives has threatened the effective use of EMRs. This situation is further worsened by the use of “copying and pasting”, leading to lots of redundant information in clinical notes. This study aimed to apply natural language processing techniques to address this problem. New information in longitudinal clinical notes was identified based on a bigram language model. The accuracy of automated identification of new information was evaluated using expert annotations as the reference standard. A two-stage cross-over user experiment was conducted to evaluate the impact of highlighting of new information on task demands, task performance, and perceived workload. The automated method identified new information with an F1 score of 0.833. The user experiment found a significant decrease in perceived workload associated with a significantly higher task performance. In conclusion, automated identification of new information in clinical notes is feasible and practical. Highlighting of new information enables healthcare professionals to grasp key information from clinical notes with less perceived workload.