Automatic Detection of High Frequency Oscillations in Humans with Epilepsy

Epilepsy is a chronic neurological disorder characterized by seizures. Although most patients respond favorably to medications, some patients continue having seizures and require surgery or alternative treatments. Recently, high frequency oscillations (HFOs) have been proposed as a biomarker of epileptic tissue providing a seizure onset zone (SOZ) localization and relate to surgery outcomes.Visual HFO identification, a gold standard HFO marking, has limitations, such as, subjective, and time consuming; therefore, automatic detection algorithms have been developed. However, the automatic detections suffer from complex optimization and specific to recording. We present an algorithm with amplitude threshold as single parameter that requires optimization tuned by an iterative procedure. Algorithm is used to study HFOs in intracranial (iEEG) and scalp EEG. In the iEEG, our detector achieved 99.6% sensitivity with 1.1% false positive rate (FPR), and 37.3% false detection rate. Furthermore, the algorithm was used to detect HFOs in scalp EEG. Of the marked candidate events, 40% and 60% were visually confirmed to be ripples and fast ripples by three reviewers.As all HFO study rely on an empirical, derived from visual observation, rather than physiological definition, we introduce the anomaly HFO detection algorithm (ADA). The algorithm integrates machine learning techniques, including anomaly detection, pattern matching, and clustering and classification to identify anomalous patterns in high frequency signals without prior assumption of the shape, amplitude, or duration. The events detected by ADA are the different population to the conventional HFOs. The amplitude of detected events is a superior candidate as a SOZ biomarker with area under the receiver operation characteristic curve (AUC), sensitivity and FPR at 0.959, 93.6% and 5.6% when comparing to the rate of conventional HFO, which was exclusively used as a biomarker, (AUC:0.912, sensitivity:86.0% and FPR:13.3%). Moreover, the amplitude is more robust to the additional events, and stable across recording segments. We believe ADA and simple detection algorithm will be powerful tools for the assessment and localization of epileptic activity providing unbiased estimation of HFO properties. Furthermore, the amplitude of HFO can become a superior candidate as the SOZ biomarker for epilepsy patients comparing to the rate of HFO

Automatic Detection of High Frequency Oscillations in Humans with Epilepsy

Epilepsy is a chronic neurological disorder characterized by seizures. Although most patients respond favorably to medications, some patients continue having seizures and require surgery or alternative treatments. Recently, high frequency oscillations (HFOs) have been proposed as a biomarker of epileptic tissue providing a seizure onset zone (SOZ) localization and relate to surgery outcomes.Visual HFO identification, a gold standard HFO marking, has limitations, such as, subjective, and time consuming; therefore, automatic detection algorithms have been developed. However, the automatic detections suffer from complex optimization and specific to recording. We present an algorithm with amplitude threshold as single parameter that requires optimization tuned by an iterative procedure. Algorithm is used to study HFOs in intracranial (iEEG) and scalp EEG. In the iEEG, our detector achieved 99.6% sensitivity with 1.1% false positive rate (FPR), and 37.3% false detection rate. Furthermore, the algorithm was used to detect HFOs in scalp EEG. Of the marked candidate events, 40% and 60% were visually confirmed to be ripples and fast ripples by three reviewers.As all HFO study rely on an empirical, derived from visual observation, rather than physiological definition, we introduce the anomaly HFO detection algorithm (ADA). The algorithm integrates machine learning techniques, including anomaly detection, pattern matching, and clustering and classification to identify anomalous patterns in high frequency signals without prior assumption of the shape, amplitude, or duration. The events detected by ADA are the different population to the conventional HFOs. The amplitude of detected events is a superior candidate as a SOZ biomarker with area under the receiver operation characteristic curve (AUC), sensitivity and FPR at 0.959, 93.6% and 5.6% when comparing to the rate of conventional HFO, which was exclusively used as a biomarker, (AUC:0.912, sensitivity:86.0% and FPR:13.3%). Moreover, the amplitude is more robust to the additional events, and stable across recording segments. We believe ADA and simple detection algorithm will be powerful tools for the assessment and localization of epileptic activity providing unbiased estimation of HFO properties. Furthermore, the amplitude of HFO can become a superior candidate as the SOZ biomarker for epilepsy patients comparing to the rate of HFO

Transcriptomic Analysis of Subtype-Specific Tyrosine Kinases as Triple Negative Breast Cancer Biomarkers

Triple negative breast cancer (TNBC) shows impediment to the development of targeted therapies due to the absence of specific molecular targets. The high heterogeneity across TNBC subtypes, which can be classified to be at least four subtypes, including two basal-like (BL1, BL2), a mesenchymal (M), and a luminal androgen receptor (LAR) subtype, limits the response to cancer therapies. Despite many attempts to identify TNBC biomarkers, there are currently no effective targeted therapies against this malignancy. In this study, thus, we identified the potential tyrosine kinase (TK) genes that are uniquely expressed in each TNBC subtype, since TKs have been typically used as drug targets. Differentially expressed TK genes were analyzed from The Cancer Genome Atlas (TCGA) database and were confirmed with the other datasets of both TNBC patients and cell lines. The results revealed that each TNBC subtype expressed distinct TK genes that were specific to the TNBC subtype. The identified subtype-specific TK genes of BL1, BL2, M, and LAR are LYN, CSF1R, FGRF2, and SRMS, respectively. These findings could serve as a potential biomarker of specific TNBC subtypes, which could lead to an effective treatment for TNBC patients

<i>In Silico</i> Identification of Potential Sites for a Plastic-Degrading Enzyme by a Reverse Screening through the Protein Sequence Space and Molecular Dynamics Simulations

The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico alanine scanning mutagenesis (ASM) and site-saturation mutagenesis (SSM) were employed to construct the protein sequence space from binding energy of the PETase–PET interaction to identify the number and position of mutation sites and their appropriate side-chain properties that could improve the PETase–PET interaction. The binding mechanisms of the potential PETase variant were investigated through atomistic molecular dynamics simulations. The results show that up to two mutation sites of PETase are preferable for use in protein engineering to enhance the PETase activity, and the proper side chain property depends on the mutation sites. The predicted variants agree well with prior experimental studies. Particularly, the PETase variants with S238C or Q119F could be a potential candidate for improving PETase. Our combination of in silico ASM and SSM could serve as an alternative protocol for protein engineering because of its simplicity and reliability. In addition, our findings could lead to PETase improvement, offering an important contribution towards a sustainable future

Detection of anomalous high-frequency events in human intracranial EEG.

ObjectiveHigh-frequency oscillations (HFOs) are a promising biomarker for the epileptogenic zone. However, no physiological definition of an HFO has been established, so detection relies on the empirical definition of an HFO derived from visual observation. This can bias estimates of HFO features such as amplitude and duration, thereby hindering their utility as biomarkers. Therefore, we set out to develop an algorithm that detects high-frequency events in the intracranial EEG that are morphologically distinct from background without requiring assumptions about event amplitude or shape.MethodWe propose the anomaly detection algorithm (ADA), which uses unsupervised machine learning to identify segments of data that are distinct from the background. We apply ADA and a standard HFO detector using a root mean square amplitude threshold to intracranial EEG from 11 patients undergoing evaluation for epilepsy surgery. The rate, amplitude, and duration of the detected events and the percent overlap between the two detectors are compared.ResultIn the seizure onset zone (SOZ), ADA detected a subset of conventional HFOs. In non-SOZ channels, ADA detected at least twice as many events as the standard approach, including some conventional HFOs; however, ADA also identified many low and intermediate amplitude events missed by the standard amplitude-based method. The rate of ADA events was similar across all channels; however, the amplitude of ADA events was significantly higher in SOZ channels (P&nbsp;&lt;&nbsp;.0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P&nbsp;&lt;&nbsp;.0125).SignificanceADA does not require human supervision, parameter optimization, or prior assumptions about event shape, amplitude, or duration. Our results suggest that the algorithm's estimate of event amplitude may differentiate SOZ and non-SOZ channels. Further studies will examine the utility of HFO amplitude as a biomarker for epilepsy surgical outcome

Beyond rates: time-varying dynamics of high frequency oscillations as a biomarker of the seizure onset zone

Objective. High frequency oscillations (HFOs) recorded by intracranial electrodes have generated excitement for their potential to help localize epileptic tissue for surgical resection. However, the number of HFOs per minute (i.e. the HFO 'rate') is not stable over the duration of intracranial recordings; for example, the rate of HFOs increases during periods of slow-wave sleep. Moreover, HFOs that are predictive of epileptic tissue may occur in oscillatory patterns due to phase coupling with lower frequencies. Therefore, we sought to further characterize between-seizure (i.e. 'interictal') HFO dynamics both within and outside the seizure onset zone (SOZ).Approach. Using long-term intracranial EEG (mean duration 10.3 h) from 16 patients, we automatically detected HFOs using a new algorithm. We then fit a hierarchical negative binomial model to the HFO counts. To account for differences in HFO dynamics and rates between sleep and wakefulness, we also fit a mixture model to the same data that included the ability to switch between two discrete brain states that were automatically determined during the fitting process. The ability to predict the SOZ by model parameters describing HFO dynamics (i.e. clumping coefficients and coefficients of variation) was assessed using receiver operating characteristic curves.Main results. Parameters that described HFO dynamics were predictive of SOZ. In fact, these parameters were found to be more consistently predictive than HFO rate. Using concurrent scalp EEG in two patients, we show that the model-found brain states corresponded to (1) non-REM sleep and (2) awake and rapid eye movement sleep. However the brain state most likely corresponding to slow-wave sleep in the second model improved SOZ prediction compared to the first model for only some patients.Significance. This work suggests that delineation of SOZ with interictal data can be improved by the inclusion of time-varying HFO dynamics

COVID-19 impact on blood donor characteristics and seroprevalence of transfusion-transmitted infections in southern Thailand between 2018 and 2022

Abstract Blood safety is a critical aspect of healthcare systems worldwide involving rigorous screening, testing, and processing protocols to minimize the risk of transfusion-transmitted infections (TTIs). The present study offers a comprehensive assessment of the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), and syphilis among blood donors in southern Thailand. It explores the consequences of the COVID-19 pandemic on the blood transfusion service, donor characteristics, and the prevalence of TTIs. A retrospective analysis of 65,511 blood donors between 2018 and 2022 was conducted at Songklanagarind Hospital, Thailand. The socio-demographic characteristics of the donors were examined using the Chi-square test to assess the relationship between TTIs serological positivity and donor characteristics. The donors were divided into pre-COVID-19 (2018–2019) and during COVID-19 (2020–2022) groups to evaluate the impacts of COVID-19. The study found that HBV had the highest overall prevalence at 243 per hundred thousand (pht), followed by syphilis (118 pht), HCV (32 pht), and HIV (31 pht) over a five-year period of study. After COVID-19, the prevalence of HBV decreased by 21.8%; HCV decreased by 2.1%; HIV increased by 36.4%; and syphilis increased by 9.2%. The socio-demographic characteristics and TTIs prevalence were significantly altered over time. This study provides insights into blood donor characteristics and TTIs prevalence in southern Thailand, highlighting the understanding of the impact of COVID-19 on the spread of TTIs