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

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.

Allosteric Signal within the Receptor-Binding Domain of the SARS-CoV-2 Spike Protein Mediated by a Class 3 Monoclonal Antibody Revealed through Molecular Dynamics Simulations and Protein Residue Networks.

This study investigated the allosteric action within the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein caused by class 3 monoclonal antibody (mAb) binding. As the emergence of SARS-CoV-2 variants has raised concerns about the effectiveness of treatments by antibodies, targeting the highly conserved class 3 epitopes has become an alternative strategy of antibody design. Simulations of explicitly solvated RBD of the BA.2.75 omicron subvariants were carried out both in the presence and in the absence of bebtelovimab, as a model example of class 3 monoclonal antibodies against the RBD of the SARS-CoV-2 spike protein. The comparative analysis showed that bebtelovimab's binding on two α helices at the epitope region disrupted the nearby interaction network, which triggered a denser interaction network formation on the opposite side of the receptor-binding motif (RBM) region and resulted in a "close" conformation that could prevent the ACE2 binding. A better understanding of this allosteric action could lead to the development of alternative mAbs for further variants of concern. In terms of computational techniques, the communicability matrix could serve as a tool to visualize the effects of allostery, as the pairs of amino acids or secondary structures with high communicability could pinpoint the possible sites to transfer the allosteric signal. Additionally, the communicability gain/loss matrix could help elucidate the consequences of allosteric actions, which could be employed along with other allostery quantification techniques in some previous studies.

Prognostic Markers in Tyrosine Kinases Specific to Basal-like 2 Subtype of Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC), a heterogeneous and therapeutically challenging subtype, comprises over 50% of patients categorized into basal-like 1 (BL1) and basal-like 2 (BL2) intrinsic molecular subtypes. Despite their shared basal-like classification, BL2 is associated with a poor response to neoadjuvant chemotherapy and reduced relapse-free survival compared to BL1. Here, the study focused on identifying subtype-specific markers for BL2 through transcriptomic analysis of TNBC patients using RNA-seq and clinical integration. Six receptor tyrosine kinase (TK) genes, including EGFR, EPHA4, EPHB2, PDGFRA, PDGFRB, and ROR1, were identified as potential differentiators for BL2. Correlations between TK mRNA expression and TNBC prognosis, particularly EGFR, PDGFRA, and PDGFRB, revealed potential synergistic interactions in pathways related to cell survival and proliferation. Our findings also suggest promising dual markers for predicting disease prognosis. Furthermore, RT-qPCR validation demonstrated that identified BL2-specific TKs were expressed at a higher level in BL2 than in BL1 cell lines, providing insights into unique characteristics. This study advances the understanding of TNBC heterogeneity within the basal-like subtypes, which could lead to novel clinical treatment approaches and the development of targeted therapies.

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.

In Silico 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.

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.

Integration of FRET and sequencing to engineer kinase biosensors from mammalian cell libraries.

The limited sensitivity of Förster Resonance Energy Transfer (FRET) biosensors hinders their broader applications. Here, we develop an approach integrating high-throughput FRET sorting and next-generation sequencing (FRET-Seq) to identify sensitive biosensors with varying substrate sequences from large-scale libraries directly in mammalian cells, utilizing the design of self-activating FRET (saFRET) biosensor. The resulting biosensors of Fyn and ZAP70 kinases exhibit enhanced performance and enable the dynamic imaging of T-cell activation mediated by T cell receptor (TCR) or chimeric antigen receptor (CAR), revealing a highly organized ZAP70 subcellular activity pattern upon TCR but not CAR engagement. The ZAP70 biosensor elucidates the role of immunoreceptor tyrosine-based activation motif (ITAM) in affecting ZAP70 activation to regulate CAR functions. A saFRET biosensor-based high-throughput drug screening (saFRET-HTDS) assay further enables the identification of an FDA-approved cancer drug, Sunitinib, that can be repurposed to inhibit ZAP70 activity and autoimmune-disease-related T-cell activation.

Advances in emergent biological recognition elements and bioelectronics for diagnosing COVID-19.

Coronaviruses pose a serious threat to public health. Tremendous efforts are dedicated to advance reliable and effective detection of coronaviruses. Currently, the coronavirus disease 2019 (COVID-19) diagnosis mainly relies on the detection of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genetic materials by using reverse transcription-polymerase chain reaction (RT-PCR) assay. However, simpler and more rapid and reliable alternatives are needed to meet high demand during the pandemic. Biosensor-based diagnosis approaches become alternatives for selectively and rapidly detecting virus particles because of their biorecognition elements consisting of biomaterials that are specific to virus biomarkers. Here, we summarize biorecognition materials, including antibodies and antibody-like molecules, that are designed to recognize SARS-CoV-2 biomarkers and the advances of recently developed biosensors for COVID-19 diagnosis. The design of biorecognition materials or layers is crucial to maximize biosensing performances, such as high selectivity and sensitivity of virus detection. Additionally, the recent representative achievements in developing bioelectronics for sensing coronavirus are included. This review includes scholarly articles, mainly published in 2020 and early 2021. In addition to capturing the fast development in the fields of applied materials and biodiagnosis, the outlook of this rapidly evolving technology is summarized. Early diagnosis of COVID-19 could help prevent the spread of this contagious disease and provide significant information to medical teams to treat patients.

Amplitude of high frequency oscillations as a biomarker of the seizure onset zone.

OBJECTIVE: Studies of high frequency oscillations (HFOs) in epilepsy have primarily tested the HFO rate as a biomarker of the seizure onset zone (SOZ), but the rate varies over time and is not robust for all individual subjects. As an alternative, we tested the performance of HFO amplitude as a potential SOZ biomarker using two automated detection algorithms. METHOD: HFOs were detected in intracranial electroencephalogram (iEEG) from 11 patients using a machine learning algorithm and a standard amplitude-based algorithm. For each detector, SOZ and non-SOZ channels were classified using the rate and amplitude of high frequency events, and performance was compared using receiver operating characteristic curves. RESULTS: The amplitude of detected events was significantly higher in SOZ. Across subjects, amplitude more accurately classified SOZ/non-SOZ than rate (higher values of area under the ROC curve and sensitivity, and lower false positive rates). Moreover, amplitude was more consistent across segments of data, indicated by lower coefficient of variation. CONCLUSION: As an SOZ biomarker, HFO amplitude offers advantages over HFO rate: it exhibits higher classification accuracy, more consistency over time, and robustness to parameter changes. SIGNIFICANCE: This biomarker has the potential to increase the generalizability of HFOs and facilitate clinical implementation as a tool for SOZ localization.

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

OBJECTIVE: High-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. METHOD: We 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. RESULT: In 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 < .0045), and the amplitude measurement was more stable over time than the HFO rate, as indicated by a lower coefficient of variation (P < .0125). SIGNIFICANCE: ADA 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.

Automated Detection of High Frequency Oscillations in Human Scalp Electroencephalogram.

High frequency oscillations (HFOs) > 80 Hz are a promising biomarker of epileptic tissue. Recent evidence has shown that spontaneous HFOs can be recorded from the scalp, but detection of these electrographic events remains a challenge. Here, we modified a simple automatic detector, used originally for intracranial EEG (iEEG) recordings, to detect ripples and fast ripples in scalp EEG. We analyzed scalp EEG recordings of seven subjects and validated our detector and artifact rejection algorithm via visual review. Of the candidate events marked by the detector, 40% and 60% were confirmed to be ripples and fast ripples, respectively, by human visual review, making this algorithm suitable for supervised detection. Detected HFOs occurred at a rate of <1/min in most channels, and the average duration was 47 and 24 ms for ripples and fast ripples, respectively. The simplicity of the algorithm, with only a single parameter, enables the consistent application of automatic detection across recording modalities, and it could therefore be a tool for the assessment and localization of epileptic activity.