Normal Background Research Articles

Early Continuous Electroencephalography, Clinical Parameters, and Short-Term Functional Outcomes in Pediatric Traumatic Brain Injury: Single-Center, Retrospective Cohort, 2010-2020.

Traumatic brain injury (TBI) is a leading cause of pediatric morbidity and mortality. This study first investigates clinical characteristics and continuous electroencephalography (cEEG) parameters associated with short-term functional outcomes in pediatric patients following TBI. Second, we use these data for a hypothesis-generating model about outcomes. Retrospective cohort study. PICU within a quaternary care hospital. Pediatric patients (< 18 yr) admitted from January 2010 to December 2020 with TBI who underwent cEEG within 72 hours of admission. None. Patient demographics, clinical parameters, hospital course, and cEEG features were reviewed for associations with mortality and new disability (as defined by change in Functional Status Score between admission and discharge of ≥ 3 points). A statistical prediction model for patient outcome was created combining cEEG parameters with admission Glasgow Coma Scale (GCS) score and radiographic findings. We included 142 patients: 100 (70%) of whom had no new disability at hospital discharge; 42 (30%) had a new disability, including eight deaths. Univariate analysis showed favorable outcomes were associated with normal electroencephalogram background, reactivity, and sleep features (p < 0.001 for each). A model inclusive of these electroencephalogram parameters and GCS had high predictive ability for outcome with 0.94 with 95% CI (0.90-0.98). Specific cEEG findings observed acutely after injury, in combination with other clinical characteristics, may serve as biomarkers for short-term functional outcomes after pediatric TBI. Further validation of the model in another population is now required.

Heavy Metal Distribution in Aquatic Products from Eastern Guangdong and Associated Health Risk Assessment

With the rapid industrialization and urbanization of coastal areas, marine pollution (such as heavy metals) is increasingly contaminating the environment, posing significant public health risks. Eastern Guangdong, a key aquaculture and fisheries hub in China, has a growing market for aquatic products. Heavy metals persist in the environment and are difficult to degrade and bioaccumulate in marine organisms through the food web, presenting carcinogenic and mutagenic risks to humans, as top predators. This study analyzed 10 key species commonly consumed by residents of eastern Guangdong (bivalves, crustaceans, and fish), measuring the concentrations of six heavy metals (As, Cd, Cr, Cu, Ni, Pb) using inductively coupled plasma mass spectrometry (ICP-MS). Pollution levels were assessed using the pollution index (Pi), and dietary exposure risks were evaluated via the target hazard quotient (THQ) for different age groups. Results showed that Pi values for all metals were within normal background levels, but bivalves had a high capacity for Cd accumulation, with pollution severity ranking as bivalves &gt; crustaceans &gt; fish. The THQ values for both adults and teenagers were &lt;1 across all samples, indicating no risk to the health of residents. However, the TTHQ for Sanguinolaria sp. exceeded 1, indicating potential health risks. This study highlights the health risks of consuming heavy metal-contaminated aquatic products, particularly bivalves. Reducing the consumption of these high-metal species could help lower dietary exposure and associated risks. Our findings provide essential data for the quality assessment of aquatic products and offer dietary recommendations for residents in eastern Guangdong.

Generation of Monosomy 21q Human iPS Cells by CRISPR/Cas9-Mediated Interstitial Megabase Deletion.

Missing an entire chromosome or chromosome arm in normal diploid cells has a deleterious impact on cell viability, which may contribute to the development of specific birth defects. Nevertheless, the effects of chromosome loss in human cells have remained unexplored due to the lack of suitable model systems. Here, we developed an efficient, selection-free approach to generate partial monosomy in human induced pluripotent stem cells (iPSCs). The introduction of Cas9 proteins and a pair of gRNAs induces over megabase-sized interstitial chromosomal deletions. Using human chromosome 21 (HSA21) as a model, partial monosomy 21q (PM21q) iPSC lines with deletions ranging from 4.5 to 27.9 Mb were isolated. A 33.6 Mb deletion, encompassing all protein-coding genes on 21q, was also achieved, establishing the first 21q monosomy human iPSC line. Transcriptome and proteome analyses revealed that the abundances of mRNA and protein encoded by the majority of genes in the monosomic regions are half of the diploid expression level, indicating an absence of dosage compensation. The ability to generate customized partial monosomy cell lines on an isogenic, karyotypically normal background should facilitate the gain of novel insights into the impact of chromosome loss on cellular fitness.

Large, but short-term, increase in fecal indicator bacteria following extreme flooding from Hurricane Harvey in Houston, TX

Hurricane Harvey caused widespread flooding along the Texas Gulf Coast in August 2017; some areas of Houston received &amp;gt;150 cm of rainfall within a few days. Due to concerns over fecal contamination of floodwaters, surface water samples were collected at six locations in the southeastern Houston area immediately before and after the hurricane and then every 1 to 2 weeks thereafter over a 2-month period. Total E. coli was enumerated using the IDEXX Quanti-Tray/2000 system. DNA extracted from water samples was analyzed via quantitative real-time PCR (qPCR) for general and source-specific total Bacteroidales and human Bacteroidales markers, and digital PCR (dPCR) for antibiotic resistance genes (ARG) and a plasmid (pBI143) associated with human waste. SourceTracker2 was used to determine human source contributions based on metagenomic analysis of PCR-amplified 16S rRNA gene fragments. Samples collected immediately after the hurricane had elevated levels of E. coli, ranging from 488 to 1,733 CFU 100 ml−1. After 1 week, E. coli levels decreased to &amp;lt;100 MPN 100 ml−1. Total Bacteroidales numbers were elevated immediately following the hurricane and remained high for 12 days. Human-source contributions, as assessed by PCR methods and metagenomic analysis, peaked within 12 days after the hurricane consistently across all sampling sites. Multiple regression analysis of environmental parameters, copies of ARG and pBI143, and metagenomic data confirmed that human waste caused the dramatic, short-term, high levels of fecal contamination of floodwaters generated by Hurricane Harvey. Fecal indicators approached normal background levels approximately 3 weeks after the rainfall ended.

Glucagon-Like Peptide-1 Receptor-Targeted PET/CT With 68 Ga-HBED-CC-Exendin-4 in Localizing Insulinoma : A Head-to-Head Comparison to 68 Ga-NOTA-Exendin-4.

Modifying glucagon-like peptide-1 receptor (GLP-1R)-targeted PET agent to achieve faster renal clearance and preserved high affinity to GLP-1R is clinically relevant. The aim of this study is to assess the performance of a newly developed GLP-1R-targeted agent, 68 Ga-HBED-CC-exendin-4 in localizing insulinoma, and its biodistribution, as compared with previously introduced 68 Ga-NOTA-exendin-4. Nineteen patients with endogenous hyperinsulinemic hypoglycemia were enrolled and referred for 68 Ga-HBED-CC-exendin-4 PET/CT and 68 Ga-NOTA-exendin-4 PET/CT within 2 consecutive days. Diagnostic performance of the 2 tracers in localizing insulinoma was evaluated, and SUV of the lesion, normal pancreas background, kidneys, and bladder were measured. 68 Ga-HBED-CC-exendin-4 and 68 Ga-NOTA-exendin-4 PET/CT exhibited an equivalent efficacy in detection rate (both sensitivity of 100%). Although SUV max of the tumor in 68 Ga-HBED-CC-exendin-4 was significantly lower than that in 68 Ga-NOTA-exendin-4 (20.01 ± 9.41 vs 31.78 ± 15.46, P < 0.001) at 50 minutes postinjection, there was no significant difference in the tumor-to-background ratio between the 2 agents (8.61 ± 3.57 vs 8.18 ± 3.38, P = 0.326), and the lesions could be visible as early as 4 minutes postinjection for both agents in patients who underwent dynamic PET/CT. In addition, 68 Ga-HBED-CC-exendin-4 exhibited approximately 30% decrease of the renal accumulation compared with 68 Ga-NOTA-exendin-4 (SUV mean , 42.21 ± 5.79 vs 58.58 ± 10.06 at 50 minutes, P < 0.001). 68 Ga-HBED-CC-exendin-4 is an effective agent for localizing insulinoma showing similar detectability and tumor-to-background ratio compared with 68 Ga-NOTA-exendin-4. Notably, 68 Ga-HBED-CC-exendin-4 exhibits significantly lower renal uptake than 68 Ga-NOTA-exendin-4, which might potentially benefit the detection of the tumors adjacent to the left kidneys.

Role of early postnatal aEEG and brain MRI in predicting neurodevelopmental outcome in preterm infants at the age of 1 year

BackgroundPreterm birth is a principal reason for perinatal morbidity and mortality increasing the incidence of severe neurodevelopmental deficits. There is growing proof that early postnatal amplitude-integrated electroencephalography (aEEG) has a prognostic value for neurodevelopmental consequence in preterm born neonates. Furthermore, MRI has been widely utilized to enhance comprehension of the brain substrate responsible for neurodevelopmental abnormalities. Thus, this study aims at evaluating the role of early postnatal aEEG and brain MRI in forecasting neurodevelopmental consequence in preterm infants at the age of 1 year.MethodsA cohort study performed in the neonatal intensive care unit of a tertiary hospital during the duration from October 2021 to June 2023 including 60 preterm neonates < 32 weeks of gestation. All cases were monitored by aEEG within the 1st 72 h of life for at least 4 h, and then brain MRI and aEEG were done at term equivalent age (TEA) of 40 weeks. Regarding the neurodevelopmental outcome, our cases were assessed by Bayley scale III screening test at the age of 1 year.ResultsOf the study participants, 41.7% were males, and 58.3% were females with a mean gestational age of 30.40 ± 0.94 weeks and mean weight of 1.36 ± 0.17 kg. aEEG showed that 83.3% of the cases had continuous normal background activity at TEA, and MRI showed that 75% of the cases were normal. Comparing between non-affected and affected groups as categorized by Bayley scale regarding aEEG and MRI findings, there was greatly statistically significant difference between the two groups (P < 0.001). Brain MRI showed higher sensitivity and accuracy than aEEG.ConclusionBrain MRI at TEA is more sensitive and accurate than aEEG to predict the neurodevelopmental outcome. aEEG at TEA is more predictor for neurodevelopmental outcome than at birth. The combination of both aEEG and brain MRI at TEA gives more prediction about the degree of affection in neurodevelopmental outcome in preterm infants.

The Pathophysiology and Biomarkers of Delirium.

Delirium is a major disturbance in the mental state characterized by fluctuations in arousal, deficits in attention, distorted perception, and disruptions in memory and cognitive processing. Delirium affects approximately 18% to 25% of hospital inpatients, with even higher rates observed during critical illness. To develop therapies to shorten the duration and limit the adverse effects of delirium, it is important to understand the mechanisms underlying its presentation. Neuroimaging modalities such as magnetic resonance imaging (MRI), positron emission tomography, functional MRI, and near-infrared spectroscopy point to global atrophy, white matter changes, and disruptions in cerebral blood flow, oxygenation, metabolism, and connectivity as key correlates of delirium pathogenesis. Electroencephalography demonstrates generalized slowing of normal background activity, with pathologic decreases in variability of oscillatory patterns and disruptions in functional connectivity among specific brain regions. Elevated serum biomarkers of inflammation, including interleukin-6, C-reactive protein, and S100B, suggest a role of dysregulated inflammatory processes and cellular metabolism, particularly in perioperative and sepsis-related delirium. Emerging animal models that can mimic delirium-like clinical states will reveal further insights into delirium pathophysiology. The combination of clinical and basic science methods of exploring delirium shows great promise in elucidating its underlying mechanisms and revealing potential therapeutic targets.

Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search Game.

This study explores the intersection of personality, attention and task performance in traditional 2D and immersive virtual reality (VR) environments. A visual search task was developed that required participants to find anomalous images embedded in normal background images in 3D space. Experiments were conducted with 30 subjects who performed the task in 2D and VR environments while their eye movements were tracked. Following an exploratory correlation analysis, we applied machine learning techniques to investigate the predictive power of gaze features on human data derived from different data collection methods. Our proposed methodology consists of a pipeline of steps for extracting fixation and saccade features from raw gaze data and training machine learning models to classify the Big Five personality traits and attention-related processing speed/accuracy levels computed from the Group Bourdon test. The models achieved above-chance predictive performance in both 2D and VR settings despite visually complex 3D stimuli. We also explored further relationships between task performance, personality traits and attention characteristics.

Base excision repair and double strand break repair cooperate to modulate the formation of unrepaired double strand breaks in mouse brain

We lack the fundamental information needed to understand how DNA damage in the brain is generated and how it is controlled over a lifetime in the absence of replication check points. To address these questions, here, we integrate cell-type and region-specific features of DNA repair activity in the normal brain. The brain has the same repair proteins as other tissues, but normal, canonical repair activity is unequal and is characterized by high base excision repair (BER) and low double strand break repair (DSBR). The natural imbalance creates conditions where single strand breaks (SSBs) can convert to double strand breaks (DSBs) and reversibly switch between states in response to oxidation both in vivo and in vitro. Our data suggest that, in a normal background of repair, SSBs and DSBs are in an equilibrium which is pushed or pulled by metabolic state. Interconversion of SSB to DSBs provides a physiological check point, which would allow the formation of unrepaired DSBs for productive functions, but would also restrict them from exceeding tolerable limits.

385. TRANSLATING MOLECULAR-TARGETING FLUORESCENT PROBES IN OESOPHAGEAL CANCER PRE-CLINICAL MODELS

Abstract Background Intra-operative molecular imaging (IMI) is an emerging field that utilises tumour-targeting fluorescent probes to improve oncological outcomes. A novel class of probe known as quenched activity-based probes (qABP), developed to measure enzyme activity in-vitro, has been repurposed to target tumour-expressed proteases known as cathepsins. Unlike other tumour-targeting probes, fluorescence is inhibited up until the qABP covalently bonds to cathepsins, releasing an inhibitory quencher which results in fluorescence. This may improve the contrast between normal tissue and tumour. Our laboratory group sought to obtain pre-clinical evidence to eventually translate qABP’s into the operating room for oesophageal, junctional and gastric cancer. Methods Cathepsin activity was screened in cell lines from the normal oesophagus, Barrett’s metaplasia, oesophageal adenocarcinoma (OAC), squamous cell carcinoma (SCC) and gastric adenocarcinoma (GAC) with two qABP’s known as BMV109 and VGT309. We additionally screened patient biopsies from oesophageal, junctional and gastric cancers. Cell line and tissue samples were analysed using in-gel fluorescence (Amersham Typhoon®) and Western blot. In-vivo, cell line xenografts were established in NSG mice using subcutaneous and orthotopic models. Tumour-bearing mice were then injected with VGT-309 and then imaged at specific timepoints using the IVIS® spectrum imager. Results Cathepsin activity was found in all oesophago-gastric cell lines and selectivity proven with a cathepsin inhibitor. Matched biopsies were collected from patients prior before and after neoadjuvant chemo-radiotherapy. Baseline tumour biopsies exhibited significantly higher cathepsin activity than normal biopsies, with these differences preserved in biopsies collected after chemo-radiotherapy (p &amp;lt;0.001). In-vivo, OAC and GAC xenografts were identified in NSG mice as early as 12 hours post injection of VGT-309, exhibiting a 2.5-fold increase in fluorescence compared to normal background stomach. Bio-distribution analysis demonstrated that VGT-309 accumulated in liver and kidney, but less so in the murine oesophagus and stomach. Conclusion Translating qABP’s into the operating room has the potential to detect early cancers in Barrett’s metaplasia, reduce positive margin rates and identify lymph node metastasis. Our research group continues to investigate qABP’s, including determining their sensitivity to detect lymph node metastasis with the aim of translating this technology into a phase 1 clinical trial.

Genetic alteration of SLCO1B3 defines constitutional indocyanine green excretory defect in patients who underwent hepatectomy.

Constitutional indocyanine green (ICG) excretory defects must be distinguished when assessing liver function. The absence of OATP1B3 expression due to homogenous alterations in the SLCO1B3 gene has been recently reported to induce ICG excretory defects; however, its association with the clinical examinations and the clinical implications of heterogeneous SLCO1B3 gene alteration remain unclear. OATP1B3 expression was evaluated in 49 patients who underwent hepatectomy after evaluation of the ICG retention rate at 15min (ICGR15) and technetium-99 m-galactosyl serum albumin (99mTc-GSA) hepatic scintigraphy. Additionally, alterations in SLCO1B3 were analyzed in patients without OATP1B3 expression. Subsequently, 59 patients who underwent hepatectomy for colorectal liver metastasis (CRLM) were analyzed. Of 49 patients, 6 (12%) had absent OATP1B3 expression. They had significantly higher ICGR15 value (74.7% vs. 23.5%; p<0.0001), better modified albumin-bilirubin (ALBI) grade (≤grade 2A, 100% vs. 42%; p=0.010), more normal 99mTc-GSA hepatic scintigraphy (100% vs. 28%; p=0.0003), and better pathological liver fibrosis (F0-1, 100% vs. 49%; p=0.027) compared to those with OATP1B3 expression. Three available frozen blocks of cases without OATP1B3 expression showed homozygous alterations in SLCO1B3. Of 59 patients with CRLM in normal liver background, five (8.5%) had heterozygous insertion in SLCO1B3, however they had no difference in ICGR15 values or other clinical findings compared to the other patients. Constitutional ICG excretory defects may be defined by the complete absence of OATP1B3 expression. The modified ALBI grade and 99mTc-GSA hepatic scintigraphy were useful for detecting constitutional ICG excretory defects.

Calibration HPGe detector using IAEA-U source for CBRNe

Detecting and preventing the illicit movement of radioactive materials within a country is crucial, requiring the identification of radiologic anomalies against the normal radiation background. High-purity germanium (HPGe) detectors, known for their precision and sensitivity, have become popular for analyzing radioactive materials in CBRNe scenarios. This study focused on calibrating an HPGe detector for CBRNe applications, using gamma-ray spectra from an IAEA-U reference source. Energy calibration involved identifying peaks in the spectra and creating a calibration curve for energy and channel number data. Efficiency calibration, determined using the known activity of the source, revealed a linear relationship between energy and detector response. Over four years, systematic efficiency calibrations showed a deviation of only 3% well below the recommended limit of 5%. These findings underscore the reliability of the system as a reference spectrometry method for accurate detection of radioactive materials. Graphical abstract

Attention Similarity Outlier Suppression with Hierarchical Feature Integration for Defect Segmentation

Defect segmentation holds significant importance in industrial applications. However, most existing algorithms are tailored to specific scenarios, lacking a unified defect segmentation algorithm for defect segmentation. Based on the analysis of the characteristics of defects, we aim to propose a unified transformer‐based defect segmentation algorithm. On the one hand, the defect foreground exhibits significant differences from the normal background. Nevertheless, the attention mechanism in the transformer involves all feature nodes in the feature map to the similarity score of one patch with all other patches. This approach may compromise the features of potential defect areas where the defect foreground significantly contrasts with the background. We introduce the Attention Similarity Outlier Suppression (ASOS) strategy to address this challenge. Our method is designed to select valid attention scores and filter out outlier scores for appropriate attention fusion, in which the feature of potential defect areas is not easily affected by background. On the other hand, the defect regions often have relatively small areas. We incorporate a Hierarchical Feature Integration (HFI) module in our framework to facilitate defect recognition at different scales. By progressively up‐sampling, features can achieve better integration to generate the final segmentation result. Extensive experiments have verified the effectiveness of our proposed method in defect segmentation tasks. Moreover, thorough ablation studies have demonstrated the efficacy of our algorithm, reinforcing its robust performance. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

RipViz: Finding Rip Currents by Learning Pathline Behavior.

We present a hybrid machine learning and flow analysis feature detection method, RipViz, to extract rip currents from stationary videos. Rip currents are dangerous strong currents that can drag beachgoers out to sea. Most people are either unaware of them or do not know what they look like. In some instances, even trained personnel such as lifeguards have difficulty identifying them. RipViz produces a simple, easy to understand visualization of rip location overlaid on the source video. With RipViz, we first obtain an unsteady 2D vector field from the stationary video using optical flow. Movement at each pixel is analyzed over time. At each seed point, sequences of short pathlines, rather a single long pathline, are traced across the frames of the video to better capture the quasi-periodic flow behavior of wave activity. Because of the motion on the beach, the surf zone, and the surrounding areas, these pathlines may still appear very cluttered and incomprehensible. Furthermore, lay audiences are not familiar with pathlines and may not know how to interpret them. To address this, we treat rip currents as a flow anomaly in an otherwise normal flow. To learn about the normal flow behavior, we train an LSTM autoencoder with pathline sequences from normal ocean, foreground, and background movements. During test time, we use the trained LSTM autoencoder to detect anomalous pathlines (i.e., those in the rip zone). The origination points of such anomalous pathlines, over the course of the video, are then presented as points within the rip zone. RipViz is fully automated and does not require user input. Feedback from domain expert suggests that RipViz has the potential for wider use.

Novel definition of local baseline values for potentially toxic elements in Czech farmland using adaptive spatial weighting

Exploratory data analysis is commonly used in geoscientific research to identify various data populations within datasets. Frequently, the distribution-wise metrics for the statistical centre and spread are combined to define normal (background) ranges of topsoil contents of potentially toxic elements. When decreasing the geographical scale, the survey areas turn too heterogeneous for the statistical definition of a single background range. The traditional solution is the domain approach wherein various data populations (and their statistical parameters) can be attributed to contextual (geological, ecological) information. Nevertheless, summarising the entire data set from large areas as a single statistical entity would provide too much data reduction which would decrease sensitivity of detecting localised anthropogenic contamination and work wrong in areas of geogenic anomalies spatially larger than contamination. In this paper, we tested a novel numerical solution for deriving local distribution-wise baseline values via spatially limited sliding window combined with geographical weighting. Considering environmental variables (soil and topographical properties) at every analysed soil sample point, we extended the geographical kernel weighting approach which considers only spatial dimension (given by geographical coordinates). The advanced version combines two similarity modes to assign highest weights to the nearest points expected to share similar environmental contexts within the user-defined moving kernel. The method was implemented for data-mining in the Czech high-density monitoring data for agricultural soils which had to be firstly regressed to achieve analytical harmony between two distinct extraction methods employed in that monitoring, in particular cold diluted nitric acid and hot aqua regia. After the reliable harmonisation, local baseline values were delivered as the localised outer limits of variation using the proposed double-weighted kernel approach. We compared the estimated localised background ranges for 10 potentially toxic elements (As, Be, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) with those based on the conventional substrate-wise domain approach and single nationwide legislation thresholds. The comparison was also efficient in identifying an inappropriate aggregation of some geological units. Finally, the kernel approach delivered regional outer limits of variability sensitive to subtle regional variations of topsoil geochemistry.

A highly sensitive, low input, and automated assay for molecular residual disease detection (MRD) using whole-genome sequencing (WGS).

3060 Background: Molecular residual disease (MRD) detection using cell-free DNA (cfDNA) for solid tumors requires a highly sensitive and specific assay that can overcome the limitation of low abundance circulating-tumor DNA (ctDNA) in a standard blood draw with fast sample-to-answer turn-around time (TAT). Here we describe the development and analytical performance of Illumina’s TruSight Oncology MRD WGS assay (TSO MRD, for research use only), for detecting MRD using tumor-informed whole-genome sequencing (WGS) of cfDNA extracted from plasma samples across multiple cancer types. Methods: The TSO MRD assay was built using Illumina’s best-in-class WGS library preparation, informatics and sequencing technologies. The assay is composed of two major steps, fingerprinting and ctDNA detection. At the fingerprinting step, the DNA from tumor tissue and matched normal samples is analyzed through WGS to generate a sample specific somatic variant list. At the ctDNA detection step, the cfDNA extracted from plasma is analyzed through shallow WGS and the presence of ctDNA is detected using the tumor specific fingerprint as an input in the MRD analysis pipeline. The plasma extraction and WGS library preparation workflows are fully automated for high-throughput sample processing with minimum hands-on time and touch points. Results: Testing of commercial MRD reference materials and ctDNA-positive samples serially diluted into normal cfDNA background demonstrated a limit of detection of down to 0.001% VAF or 10 parts per million (PPM) in multiple cancer types. Testing of a panel of normal plasma samples from healthy individuals assessed against 20 sets of fingerprints from multiple cancer types demonstrated an analytical specificity of 99.7%. Testing of ctDNA samples with multiple operators, instruments and library prep start days demonstrated high reproducibility in MRD detection. The ctDNA testing works at an optimal input amount of 5 ng and a minimum input amount down to 2 ng, which yields a high sample success rate from one standard 10 ml of blood draw. The TAT of the fingerprinting and the ctDNA detection steps is 5-7 days, respectively. Conclusions: The TSO MRD assay demonstrated high analytical sensitivity and specificity in detecting the presence of ctDNA from low input plasma samples with automated workflow and fast TAT. This assay enables a single streamlined solid tumor MRD platform for multiple cancer types.

Defining the optimal segmentation method for measuring somatostatin receptor expressing tumor volume on 68 Ga-DOTATATE positron emission tomography/computed tomography to predict prognosis in patients with gastroenteropancreatic neuroendocrine tumors.

We aimed to compare different segmentation methods used to calculate prognostically valuable volumetric parameters, somatostatin receptor expressing tumor volume (SRETV), and total lesion somatostatin receptor expression (TLSRE), measured by 68 Ga-DOTATATE PET/CT and to find the optimal segmentation method to predict prognosis. Images of 34 patients diagnosed with gastroenteropancreatic neuroendocrine tumor (GEPNET) who underwent 68 Ga-DOTATATE PET/CT imaging were reanalyzed. Four different threshold-based methods (fixed relative threshold method, normal liver background threshold method, fixed absolute standardized uptake value (SUV) threshold method, and adaptive threshold method) were used to calculate SRETV and TLSRE values. SRETV of all lesions of a patient was summarized as whole body SRETV (WB-SRETV) and TLSRE of all lesions of a patient was computed as whole body TLSRE (WB-TLSRE). WB-SRETVs calculated with all segmentation methods were statistically significantly associated with progression-free survival except WB-SRETV at which was calculated using adaptive threshold method. The fixed relative threshold methods calculated by using 45% (WB-SRETV 45% ) and 60% (WB-SRETV 60% ) of the SUV value as threshold respectively, were found to have statistically significant highest prognostic value (C-index = 0.704, CI = 0.622-0.786, P = 0.007). Among WB-TLSRE parameters, WB-TLSRE 35% , WB-TLSRE 40% , and WB-TLSRE 50% had the highest prognostic value (C-index = 0.689, CI = 0.604-0.774, P = 0.008). The fixed relative threshold method was found to be the most effective and easily applicable method to measure SRETV on pretreatment 68 Ga-DOTATATE PET/CT to predict prognosis in GEPNET patients. WB-SRETV 45% (cutoff value of 11.8 cm 3 ) and WB-SRETV 60% (cutoff value of 6.3 cm 3 ) were found to be the strongest predictors of prognosis in GEPNET patients.

When Two Eyes Don't Suffice-Learning Difficult Hyperfluorescence Segmentations in Retinal Fundus Autofluorescence Images via Ensemble Learning.

Hyperfluorescence (HF) and reduced autofluorescence (RA) are important biomarkers in fundus autofluorescence images (FAF) for the assessment of health of the retinal pigment epithelium (RPE), an important indicator of disease progression in geographic atrophy (GA) or central serous chorioretinopathy (CSCR). Autofluorescence images have been annotated by human raters, but distinguishing biomarkers (whether signals are increased or decreased) from the normal background proves challenging, with borders being particularly open to interpretation. Consequently, significant variations emerge among different graders, and even within the same grader during repeated annotations. Tests on in-house FAF data show that even highly skilled medical experts, despite previously discussing and settling on precise annotation guidelines, reach a pair-wise agreement measured in a Dice score of no more than 63-80% for HF segmentations and only 14-52% for RA. The data further show that the agreement of our primary annotation expert with herself is a 72% Dice score for HF and 51% for RA. Given these numbers, the task of automated HF and RA segmentation cannot simply be refined to the improvement in a segmentation score. Instead, we propose the use of a segmentation ensemble. Learning from images with a single annotation, the ensemble reaches expert-like performance with an agreement of a 64-81% Dice score for HF and 21-41% for RA with all our experts. In addition, utilizing the mean predictions of the ensemble networks and their variance, we devise ternary segmentations where FAF image areas are labeled either as confident background, confident HF, or potential HF, ensuring that predictions are reliable where they are confident (97% Precision), while detecting all instances of HF (99% Recall) annotated by all experts.

Autoencoder-Based Unsupervised Surface Defect Detection Using Two-Stage Training.

Accurately detecting defects while reconstructing a high-quality normal background in surface defect detection using unsupervised methods remains a significant challenge. This study proposes an unsupervised method that effectively addresses this challenge by achieving both accurate defect detection and a high-quality normal background reconstruction without noise. We propose an adaptive weighted structural similarity (AW-SSIM) loss for focused feature learning. AW-SSIM improves structural similarity (SSIM) loss by assigning different weights to its sub-functions of luminance, contrast, and structure based on their relative importance for a specific training sample. Moreover, it dynamically adjusts the Gaussian window's standard deviation (σ) during loss calculation to balance noise reduction and detail preservation. An artificial defect generation algorithm (ADGA) is proposed to generate an artificial defect closely resembling real ones. We use a two-stage training strategy. In the first stage, the model trains only on normal samples using AW-SSIM loss, allowing it to learn robust representations of normal features. In the second stage of training, the weights obtained from the first stage are used to train the model on both normal and artificially defective training samples. Additionally, the second stage employs a combined learned Perceptual Image Patch Similarity (LPIPS) and AW-SSIM loss. The combined loss helps the model in achieving high-quality normal background reconstruction while maintaining accurate defect detection. Extensive experimental results demonstrate that our proposed method achieves a state-of-the-art defect detection accuracy. The proposed method achieved an average area under the receiver operating characteristic curve (AuROC) of 97.69% on six samples from the MVTec anomaly detection dataset.

Development and evaluation of test methods for the detection and enumeration of opportunistic waterborne pathogens from the hospital environment

Many Gram-negative bacteria other than Pseudomonas aeruginosa have been implicated in waterborne outbreaks, but standardized laboratory detection methods for these organisms have not been established. This study aimed to establish laboratory testing methodologies for six waterborne pathogens: Acinetobacter spp., Burkholderia spp., Cupriavidus spp., Delftia acidovorans, Elizabethkingia spp. and Stenotrophomonas maltophilia. Water samples were spiked by UK Health Security Agency laboratories and sent to the Glasgow Royal Infirmary laboratory for analysis. Water samples were spiked with either a pure culture of target organism or the target organism in water containing normal background flora, to ensure that the methodology could identify organisms from a mixed culture. Volumes of 100mL were filtered under negative pressure on to culture media and incubated at 30°C and 37°C. The incubation time was 7 days, with plates read on days 2, 5 and 7. Further identification of colonies was undertaken using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). Optimal recovery of organisms was obtained by culturing water samples on tryptic soy agar, chocolate bacitracin agar and pseudomonas selective agar. The optimal temperature for isolation was 30°C. The optimal incubation time was 5 days, and MALDI-TOF MS identified all test species reliably. The methodology described was able to detect the six tested waterborne pathogens reliably, and can be utilized by laboratories involved in testing water samples during outbreak investigations.