Cost implications of new treatments for advanced colorectal cancer: Cost-effectiveness of CRC Treatment

Since 1996, six new drugs have been introduced for the treatment of metastatic colorectal cancer. While promising, these drugs are frequently given in the palliative, and are much more expensive than older treatments. The objective of this study is to measure the cost implications of treatment with sequential regimens that include chemotherapy and/or monoclonal antibodies

Penaid Nonproliferation: Hindering the Spread of Countermeasures Against Ballistic Missile Defenses

For more information on this publication, visit www.rand.org/t/RR378This research describes an approach to hindering the spread of countermeasures against ballistic missile defenses. (Such countermeasures, when incorporated in an attacker’s missile, are also called penetration aids, or penaids.) The approach involved compiling an unclassified list of penaid-relevant items that might be subject to internationally agreed-upon export controls. The list is formatted to fit into the export-control structure of current international policy against the proliferation of missiles capable of delivering weapons of mass destruction. This policy, the Missile Technology Control Regime, creates two levels of control. One is a set of tight restrictions against a small number of items, such as complete missiles or their major subsystems. The other is a set of case-by-case export reviews for lower-level components and dual-use items.Prepared for the Naval Postgraduate School, Project on Advanced Systems and Concepts for Combating WM

Cruise Missile Penaid Nonproliferation: Hindering the Spread of Countermeasures Against Cruise Missile Defenses

For more information on this publication, visit www.rand.org/t/RR743This research describes an approach to hindering the spread of countermeasures against cruise missile defenses. (Such countermeasures, when incorporated in an attacker’s missile or employed in conjunction with such a missile, are called penetration aids, or penaids.) This approach involved compiling an unclassified list of penaid-relevant items that might be subject to internationally agreed-upon export controls. The list is designed to fit into the export-control structure of the current international policy against the proliferation of missiles capable of delivering weapons of mass destruction. This policy, the Missile Technology Control Regime (MTCR), sets rules agreed to by 34 governments for restricting the export of items, listed in a technical annex. This report recommends controls on 18 penaid-relevant items and subitems. Because cruise missile penaids can have applications either not restricted by the MTCR (e.g., for manned aircraft) or subject only to the regime’s less rigorous controls (e.g., for relatively small cruise missiles), the report recommends that the 18 items be subject to case-by-case export reviews under MTCR procedures. To be effective, these less rigorous controls will require energetic implementation, and cooperation by Russia and China will be critical.Prepared for the Naval Postgraduate School, Project on Advanced Systems and Concepts for Combating WMDDefense Threat Reduction AgencyPrepared for the Naval Postgraduate School, Project on Advanced Systems and Concepts for Combating WM

Optimizing detection and deep learning-based classification of pathological high-frequency oscillations in epilepsy.

OBJECTIVE: This study aimed to explore sensitive detection methods for pathological high-frequency oscillations (HFOs) to improve seizure outcomes in epilepsy surgery. METHODS: We analyzed interictal HFOs (80-500 Hz) in 15 children with medication-resistant focal epilepsy who underwent chronic intracranial electroencephalogram via subdural grids. The HFOs were assessed using the short-term energy (STE) and Montreal Neurological Institute (MNI) detectors and examined for spike association and time-frequency plot characteristics. A deep learning (DL)-based classification was applied to purify pathological HFOs. Postoperative seizure outcomes were correlated with HFO-resection ratios to determine the optimal HFO detection method. RESULTS: The MNI detector identified a higher percentage of pathological HFOs than the STE detector, but some pathological HFOs were detected only by the STE detector. HFOs detected by both detectors had the highest spike association rate. The Union detector, which detects HFOs identified by either the MNI or STE detector, outperformed other detectors in predicting postoperative seizure outcomes using HFO-resection ratios before and after DL-based purification. CONCLUSIONS: HFOs detected by standard automated detectors displayed different signal and morphological characteristics. DL-based classification effectively purified pathological HFOs. SIGNIFICANCE: Enhancing the detection and classification methods of HFOs will improve their utility in predicting postoperative seizure outcomes

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach.

Intracranially recorded interictal high-frequency oscillations have been proposed as a promising spatial biomarker of the epileptogenic zone. However, its visual verification is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish high-frequency oscillations generated from the epileptogenic zone (epileptogenic high-frequency oscillations) from those generated from other areas (non-epileptogenic high-frequency oscillations). To address these issues, we constructed a deep learning-based algorithm using chronic intracranial EEG data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: (i) replicate human expert annotation of artefacts and high-frequency oscillations with or without spikes, and (ii) discover epileptogenic high-frequency oscillations by designing a novel weakly supervised model. The 'purification power' of deep learning is then used to automatically relabel the high-frequency oscillations to distill epileptogenic high-frequency oscillations. Using 12 958 annotated high-frequency oscillation events from 19 patients, the model achieved 96.3% accuracy on artefact detection (F1 score = 96.8%) and 86.5% accuracy on classifying high-frequency oscillations with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the algorithm trained from 84 602 high-frequency oscillation events from nine patients who achieved seizure-freedom after resection, the majority of such discovered epileptogenic high-frequency oscillations were found to be ones with spikes (78.6%, P < 0.001). While the resection ratio of detected high-frequency oscillations (number of resected events/number of detected events) did not correlate significantly with post-operative seizure freedom (the area under the curve = 0.76, P = 0.06), the resection ratio of epileptogenic high-frequency oscillations positively correlated with post-operative seizure freedom (the area under the curve = 0.87, P = 0.01). We discovered that epileptogenic high-frequency oscillations had a higher signal intensity associated with ripple (80-250 Hz) and fast ripple (250-500 Hz) bands at the high-frequency oscillation onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-epileptogenic high-frequency oscillations. We then designed perturbations on the input of the trained model for non-epileptogenic high-frequency oscillations to determine the model's decision-making logic. The model confidence significantly increased towards epileptogenic high-frequency oscillations by the artificial introduction of the inverted T-shaped signal template (mean probability increase: 0.285, P < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, P < 0.001). With this deep learning-based framework, we reliably replicated high-frequency oscillation classification tasks by human experts. Using a reverse engineering technique, we distinguished epileptogenic high-frequency oscillations from others and identified its salient features that aligned with current knowledge

PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.

Objective. This study aims to develop and validate an end-to-end software platform, PyHFO, that streamlines the application of deep learning (DL) methodologies in detecting neurophysiological biomarkers for epileptogenic zones from EEG recordings.Approach. We introduced PyHFO, which enables time-efficient high-frequency oscillation (HFO) detection algorithms like short-term energy and Montreal Neurological Institute and Hospital detectors. It incorporates DL models for artifact and HFO with spike classification, designed to operate efficiently on standard computer hardware.Main results. The validation of PyHFO was conducted on three separate datasets: the first comprised solely of grid/strip electrodes, the second a combination of grid/strip and depth electrodes, and the third derived from rodent studies, which sampled the neocortex and hippocampus using depth electrodes. PyHFO demonstrated an ability to handle datasets efficiently, with optimization techniques enabling it to achieve speeds up to 50 times faster than traditional HFO detection applications. Users have the flexibility to employ our pre-trained DL model or use their EEG data for custom model training.Significance. PyHFO successfully bridges the computational challenge faced in applying DL techniques to EEG data analysis in epilepsy studies, presenting a feasible solution for both clinical and research settings. By offering a user-friendly and computationally efficient platform, PyHFO paves the way for broader adoption of advanced EEG data analysis tools in clinical practice and fosters potential for large-scale research collaborations

Glutamate Is a Positive Autocrine Signal for Glucagon Release

An important feature of glucose homeostasis is the effective release of glucagon from the pancreatic α cell. The molecular mechanisms regulating glucagon secretion are still poorly understood. We now demonstrate that human α cells express ionotropic glutamate receptors (iGluRs) that are essential for glucagon release. A lowering in glucose concentration results in the release of glutamate from the α cell. Glutamate then acts on iGluRs of the AMPA/kainate type, resulting in membrane depolarization, opening of voltage-gated Ca 2+ channels, increase in cytoplasmic free Ca 2+ concentration, and enhanced glucagon release. In vivo blockade of iGluRs reduces glucagon secretion and exacerbates insulin-induced hypoglycemia in mice. Hence, the glutamate autocrine feedback loop endows the α cell with the ability to effectively potentiate its own secretory activity. This is a prerequisite to guarantee adequate glucagon release despite relatively modest changes in blood glucose concentration under physiological conditions