Super-resolution Multispectral Imaging Nanoimmunosensor for Simultaneous Detection of Diverse Early Cancer Biomarkers.

In medical diagnosis, relying on only one type of biomarker is insufficient to accurately identify cancer. Blood-based multicancer early detection can help identify more than one type of cancer from a single blood sample. In this study, a super-resolution multispectral imaging nanoimmunosensor (srMINI) based on three quantum dots (QDs) of different color conjugated with streptavidin was developed for the simultaneous screening of various cancer biomarkers in blood at the single-molecule level. In the experiment, the srMINI chip was used to simultaneously detect three key cancer biomarkers: carcinoembryonic antigen (CEA), C-reactive protein (CRP), and alpha-fetoprotein (AFP). The srMINI chip exhibited 108 times higher detection sensitivity of 0.18-0.5 ag/mL (1.1-2.6 zM) for these cancer biomarkers than commercial enzyme-linked immunosorbent assay kits because of the absence of interfering signals from the substrate, establishing considerable potential for multiplex detection of cancer biomarkers in blood. Therefore, the simultaneous detection of various cancer biomarkers using the developed srMINI chip with high diagnostic precision and accuracy is expected to play a decisive role in early diagnosis or community screening as a single-molecule biosensor.

Dynamic changes of gut microbiota in mouse models of metabolic dysfunction-associated steatohepatitis and its transition to hepatocellular carcinoma.

Dysbiosis of gut microbiota may account for pathobiology in simple fatty liver (SFL), metabolic dysfunction-associated steatohepatitis (MASH), fibrotic progression, and transformation to MASH-associated hepatocellular carcinoma (MASH-HCC). The aim of the present study is to investigate gut dysbiosis in this progression. Fecal microbial rRNA-16S sequencing, absolute quantification, histopathologic, and biochemical tests were performed in mice fed high fat/calorie diet plus high fructose and glucose in drinking water (HFCD-HF/G) or control diet (CD) for 2, 16 weeks, or 14 months. Histopathologic examination verified an early stage of SFL, MASH, fibrotic, or MASH-HCC progression with disturbance of lipid metabolism, liver injury, and impaired gut mucosal barrier as indicated by loss of occludin in ileum mucosa. Gut dysbiosis occurred as early as 2 weeks with reduced α diversity, expansion of Kineothrix, Lactococcus, Akkermansia; and shrinkage in Bifidobacterium, Lactobacillus, etc., at a genus level. Dysbiosis was found as early as MAHS initiation, and was much more profound through the MASH-fibrotic and oncogenic progression. Moreover, the expansion of specific species, such as Lactobacillus johnsonii and Kineothrix alysoides, was confirmed by an optimized method for absolute quantification. Dynamic alterations of gut microbiota were characterized in three stages of early SFL, MASH, and its HCC transformation. The findings suggest that the extent of dysbiosis was accompanied with MASH progression and its transformation to HCC, and the shrinking or emerging of specific microbial species may account at least in part for pathologic, metabolic, and immunologic alterations in fibrogenic progression and malignant transition in the liver.

NMR analysis, cytotoxic activity and theoretical study of a complex between SRPIN340 and p-sulfonic acid calix[6]arene.

Aim: This study aimed to enhance the aqueous dissolution of SRPK inhibitor N-(2-(piperidin-1-yl)-5-(trifluoromethyl)phenyl)isonicotinamide (SRPIN340). Materials & Methods: A complex with p-sulfonic calix[6]arene (Host) and SRPIN340 (Guest) was prepared, studied via 1H nuclear magnetic resonance (NMR) and theoretical calculations and biologically evaluated on cancer cell lines. Results & conclusion: The 1:1 host (H)/guest (G) complex significantly enhanced the aqueous dissolution of SRPIN340, achieving 64.8% water solubility as determined by 1H NMR quantification analysis. The H/G complex reduced cell viability by 75% for HL60, ∼50% for Nalm6 and Jurkat, and ∼30% for B16F10 cells. It exhibited greater cytotoxicity than free SRPIN340 against Jurkat and B16F10 cells. Theoretical studies indicated hydrogen bond stabilization of the complex, suggesting broader applicability of SRPIN340 across diverse biological systems.

[Box: see text].

Ultrasensitive detection of 5-hydroxymethylcytosine in genomic DNA using a graphene-based sensor modified with biotin and gold nanoparticles.

Ten-eleven translocation (TET) proteins orchestrate deoxyribonucleic acid (DNA) methylation-demethylation dynamics by oxidizing 5-methylcytosine to 5-hydroxymethylcytosine (5hmC) and are frequently inactivated in various cancers. Due to the significance of 5hmC as an epigenetic biomarker for cancer diagnosis, pathogenesis, and treatment, its rapid and precise quantification is essential. Here, we report a highly sensitive electrochemical method for quantifying genomic 5hmC using graphene sheets that were electrochemically exfoliated and functionalized with biotin and gold nanoparticles (Bt-AuNPs) through a single-step electrical method. The attachment of Bt-AuNPs to graphene enhances the specificity of 5hmC-containing DNA and augments the oxidation of 5hmC to 5-formylcytosine in DNA. When coupled to a gold electrode, the Bt-AuNP-graphene-based sensor exhibits exceptional sensitivity and specificity for detecting 5hmC, with a detection limit of 63.2 fM. Furthermore, our sensor exhibits a remarkable capacity to measure 5hmC levels across a range of biological samples, including preclinical mouse tissues with varying 5hmC levels due to either TET gene disruption or oncogenic transformation, as well as human prostate cancer cell lines. Therefore, our sensing strategy has substantial potential for cancer diagnostics and prognosis.

Methane Quantification Performance of the Quantitative Optical Gas Imaging (QOGI) System Using Single-Blind Controlled Release Assessment.

Quantitative optical gas imaging (QOGI) system can rapidly quantify leaks detected by optical gas imaging (OGI) cameras across the oil and gas supply chain. A comprehensive evaluation of the QOGI system's quantification capability is needed for the successful adoption of the technology. This study conducted single-blind experiments to examine the quantification performance of the FLIR QL320 QOGI system under near-field conditions at a pseudo-realistic, outdoor, controlled testing facility that mimics upstream and midstream natural gas operations. The study completed 357 individual measurements across 26 controlled releases and 71 camera positions for release rates between 0.1 kg Ch4/h and 2.9 kg Ch4/h of compressed natural gas (which accounts for more than 90% of typical component-level leaks in several production facilities). The majority (75%) of measurements were within a quantification factor of 3 (quantification error of -67% to 200%) with individual errors between -90% and 831%, which reduced to -79% to +297% when the mean of estimates of the same controlled release from multiple camera positions was considered. Performance improved with increasing release rate, using clear sky as plume background, and at wind speeds ≤1 mph relative to other measurement conditions.

Deep Residual Learning-Based Classification with Identification of Incorrect Predictions and Quantification of Cellularity and Nuclear Morphological Features in Digital Pathological Images of Common Astrocytic Tumors.

Interobserver variations in the pathology of common astrocytic tumors impact diagnosis and subsequent treatment decisions. This study leveraged a residual neural network-50 (ResNet-50) in digital pathological images of diffuse astrocytoma, anaplastic astrocytoma, and glioblastoma to recognize characteristic pathological features and perform classification at the patch and case levels with identification of incorrect predictions. In addition, cellularity and nuclear morphological features, including axis ratio, circularity, entropy, area, irregularity, and perimeter, were quantified via a hybrid task cascade (HTC) framework and compared between different characteristic pathological features with importance weighting. A total of 95 cases, including 15 cases of diffuse astrocytoma, 11 cases of anaplastic astrocytoma, and 69 cases of glioblastoma, were collected in Taiwan Hualien Tzu Chi Hospital from January 2000 to December 2021. The results revealed that an optimized ResNet-50 model could recognize characteristic pathological features at the patch level and assist in diagnosis at the case level with accuracies of 0.916 and 0.846, respectively. Incorrect predictions were mainly due to indistinguishable morphologic overlap between anaplastic astrocytoma and glioblastoma tumor cell area, zones of scant vascular lumen with compact endothelial cells in the glioblastoma microvascular proliferation area mimicking the glioblastoma tumor cell area, and certain regions in diffuse astrocytoma with too low cellularity being misrecognized as the glioblastoma necrosis area. Significant differences were observed in cellularity and each nuclear morphological feature among different characteristic pathological features. Furthermore, using the extreme gradient boosting (XGBoost) algorithm, we found that entropy was the most important feature for classification, followed by cellularity, area, circularity, axis ratio, perimeter, and irregularity. Identifying incorrect predictions provided valuable feedback to machine learning design to further enhance accuracy and reduce errors in classification. Moreover, quantifying cellularity and nuclear morphological features with importance weighting provided the basis for developing an innovative scoring system to achieve objective classification and precision diagnosis among common astrocytic tumors.

Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (FISH) testing. The development of new anti-HER2 antibody-drug conjugates (ADCs) for treating HER2-low breast cancers illustrates the importance of accurately assessing HER2 status, particularly HER2-low breast cancer. In this study we evaluated the performance of a deep-learning (DL) model for the assessment of HER2, including an assessment of the causes of discordances of HER2-Null between a pathologist and the DL model. We specifically focussed on aligning the DL model rules with the ASCO/CAP guidelines, including stained cells' staining intensity and completeness of membrane staining. METHODS AND RESULTS: We trained a DL model on a multicentric cohort of breast cancer cases with HER2-IHC scores (n = 299). The model was validated on two independent multicentric validation cohorts (n = 369 and n = 92), with all cases reviewed by three senior breast pathologists. All cases underwent a thorough review by three senior breast pathologists, with the ground truth determined by a majority consensus on the final HER2 score among the pathologists. In total, 760 breast cancer cases were utilized throughout the training and validation phases of the study. The model's concordance with the ground truth (ICC = 0.77 [0.68-0.83]; Fisher P = 1.32e-10) is higher than the average agreement among the three senior pathologists (ICC = 0.45 [0.17-0.65]; Fisher P = 2e-3). In the two validation cohorts, the DL model identifies 95% [93% - 98%] and 97% [91% - 100%] of HER2-low and HER2-positive tumours, respectively. Discordant results were characterized by morphological features such as extended fibrosis, a high number of tumour-infiltrating lymphocytes, and necrosis, whilst some artefacts such as nonspecific background cytoplasmic stain in the cytoplasm of tumour cells also cause discrepancy. CONCLUSION: Deep learning can support pathologists' interpretation of difficult HER2-low cases. Morphological variables and some specific artefacts can cause discrepant HER2-scores between the pathologist and the DL model.

One-Step Reverse Transcriptase qPCR Method for Serum Hepatitis B Virus RNA Quantification.

In recent years, serum hepatitis B virus (HBV) RNA has been identified as a promising noninvasive surrogate biomarker of intrahepatic covalently closed circular DNA (cccDNA), detection of which requires an invasive liver biopsy in patients with chronic HBV infection. It is impractical to detect intrahepatic cccDNA as a routine diagnosis for chronic hepatitis B (CHB) patients in clinical management. Here, we describe a detailed protocol for serum HBV RNA quantification, which can reflect the activity of intrahepatic cccDNA. The procedure includes three major steps: (1) Simultaneous isolation of HBV DNA and RNA from patients' serum, (2) DNase I digestion for removing HBV DNA contamination, and (3) HBV RNA quantification by one-step reverse transcription qPCR.

Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.

BACKGROUND: Residual image noise is substantial in positron emission tomography (PET) and one of the factors limiting lesion detection, quantification, and overall image quality. Thus, improving noise reduction remains of considerable interest. This is especially true for respiratory-gated PET investigations. The only broadly used approach for noise reduction in PET imaging has been the application of low-pass filters, usually Gaussians, which however leads to loss of spatial resolution and increased partial volume effects affecting detectability of small lesions and quantitative data evaluation. The bilateral filter (BF) - a locally adaptive image filter - allows to reduce image noise while preserving well defined object edges but manual optimization of the filter parameters for a given PET scan can be tedious and time-consuming, hampering its clinical use. In this work we have investigated to what extent a suitable deep learning based approach can resolve this issue by training a suitable network with the target of reproducing the results of manually adjusted case-specific bilateral filtering. METHODS: Altogether, 69 respiratory-gated clinical PET/CT scans with three different tracers ( [ 18 F ] FDG, [ 18 F ] L-DOPA, [ 68 Ga ] DOTATATE) were used for the present investigation. Prior to data processing, the gated data sets were split, resulting in a total of 552 single-gate image volumes. For each of these image volumes, four 3D ROIs were delineated: one ROI for image noise assessment and three ROIs for focal uptake (e.g. tumor lesions) measurements at different target/background contrast levels. An automated procedure was used to perform a brute force search of the two-dimensional BF parameter space for each data set to identify the "optimal" filter parameters to generate user-approved ground truth input data consisting of pairs of original and optimally BF filtered images. For reproducing the optimal BF filtering, we employed a modified 3D U-Net CNN incorporating residual learning principle. The network training and evaluation was performed using a 5-fold cross-validation scheme. The influence of filtering on lesion SUV quantification and image noise level was assessed by calculating absolute and fractional differences between the CNN, manual BF, or original (STD) data sets in the previously defined ROIs. RESULTS: The automated procedure used for filter parameter determination chose adequate filter parameters for the majority of the data sets with only 19 patient data sets requiring manual tuning. Evaluation of the focal uptake ROIs revealed that CNN as well as BF based filtering essentially maintain the focal SUV max values of the unfiltered images with a low mean ± SD difference of δ SUV max CNN , STD = (-3.9 ± 5.2)% and δ SUV max BF , STD = (-4.4 ± 5.3)%. Regarding relative performance of CNN versus BF, both methods lead to very similar SUV max values in the vast majority of cases with an overall average difference of δ SUV max CNN , BF = (0.5 ± 4.8)%. Evaluation of the noise properties showed that CNN filtering mostly satisfactorily reproduces the noise level and characteristics of BF with δ Noise CNN , BF = (5.6 ± 10.5)%. No significant tracer dependent differences between CNN and BF were observed. CONCLUSIONS: Our results show that a neural network based denoising can reproduce the results of a case by case optimized BF in a fully automated way. Apart from rare cases it led to images of practically identical quality regarding noise level, edge preservation, and signal recovery. We believe such a network might proof especially useful in the context of improved motion correction of respiratory-gated PET studies but could also help to establish BF-equivalent edge-preserving CNN filtering in clinical PET since it obviates time consuming manual BF parameter tuning.

Cell-Free Translation Quantification via a Fluorescent Minihelix.

Cell-free gene expression systems are used in numerous applications, including medicine making, diagnostics, and educational kits. Accurate quantification of nonfluorescent proteins in these systems remains a challenge. To address this challenge, we report the adaptation and use of an optimized tetra-cysteine minihelix both as a fusion protein and as a standalone reporter with the FlAsH dye. The fluorescent reporter helix is short enough to be encoded on a primer pair to tag any protein of interest via PCR. Both the tagged protein and the standalone reporter can be detected quantitatively in real time or at the end of cell-free expression reactions with standard 96/384-well plate readers, an RT-qPCR system, or gel electrophoresis without the need for staining. The fluorescent signal is stable and correlates linearly with the protein concentration, enabling product quantification. We modified the reporter to study cell-free expression dynamics and engineered ribosome activity. We anticipate that the fluorescent minihelix reporter will facilitate efforts in engineering in vitro transcription and translation systems.

Uncertainty quantification via localized gradients for deep learning-based medical image assessments.

Objective.Deep learning models that aid in medical image assessment tasks must be both accurate and reliable to be deployed within clinical settings. While deep learning models have been shown to be highly accurate across a variety of tasks, measures that indicate the reliability of these models are less established. Increasingly, uncertainty quantification (UQ) methods are being introduced to inform users on the reliability of model outputs. However, most existing methods cannot be augmented to previously validated models because they are not post hoc, and they change a model's output. In this work, we overcome these limitations by introducing a novel post hoc UQ method, termedLocal Gradients UQ, and demonstrate its utility for deep learning-based metastatic disease delineation.Approach.This method leverages a trained model's localized gradient space to assess sensitivities to trained model parameters. We compared the Local Gradients UQ method to non-gradient measures defined using model probability outputs. The performance of each uncertainty measure was assessed in four clinically relevant experiments: (1) response to artificially degraded image quality, (2) comparison between matched high- and low-quality clinical images, (3) false positive (FP) filtering, and (4) correspondence with physician-rated disease likelihood.Main results.(1) Response to artificially degraded image quality was enhanced by the Local Gradients UQ method, where the median percent difference between matching lesions in non-degraded and most degraded images was consistently higher for the Local Gradients uncertainty measure than the non-gradient uncertainty measures (e.g. 62.35% vs. 2.16% for additive Gaussian noise). (2) The Local Gradients UQ measure responded better to high- and low-quality clinical images (p< 0.05 vsp> 0.1 for both non-gradient uncertainty measures). (3) FP filtering performance was enhanced by the Local Gradients UQ method when compared to the non-gradient methods, increasing the area under the receiver operating characteristic curve (ROC AUC) by 20.1% and decreasing the false positive rate by 26%. (4) The Local Gradients UQ method also showed more favorable correspondence with physician-rated likelihood for malignant lesions by increasing ROC AUC for correspondence with physician-rated disease likelihood by 16.2%.Significance. In summary, this work introduces and validates a novel gradient-based UQ method for deep learning-based medical image assessments to enhance user trust when using deployed clinical models.

Quantitative PCR-based high-sensitivity detection of HBV-DNA levels reflects liver function deterioration in patients with hepatitis B virus-related cirrhosis.

OBJECTIVES: To investigate the clinical implication of quantitative polymerase chain reaction (PCR)-based high-sensitivity detection of hepatitis B virus (HBV)-DNA levels in patients with HBV-related liver cirrhosis (LC). METHODS: From January 2020 to December 2022, 100 fasting serum samples were collected and retrospectively analyzed from patients with treated HBV-related LC attending the Suzhou Hospital of Integrated Traditional Chinese and Western Medicine and Suzhou Guangci Cancer Hospital. Patients were divided into a negative group (HBV-DNA < 20 IU/mL) and a positive group (HBV-DNA ≥ 20 IU/mL) according to their high-sensitivity HBV-DNA test results. The clinical characteristics and serological indicators of the two groups were compared, mainly including gender, age, liver function [total protein (TP), albumin (ALB), alanine aminotransferase (ALT), aspartate aminotransferase (AST), γ-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), total bilirubin (TBIL), direct bilirubin (DBIL), and indirect bilirubin (IBIL)], lipids [total cholesterol (TC) and triglycerides (TG)], platelets (PLT), five serum liver fibrosis markers [cholyglycine (CG), hyaluronic acid (HA), laminin (LN), precollagen type III (PCIII), and type IV collagen (IV-C)], serum gastrointestinal tumor markers [α-fetoprotein (AFP) and carcinoembryonic antigen (CEA)], and hepatitis B surface antigen (HBsAg). The differences between the two groups in terms of liver function Child-Pugh grades and the incidence of hepatocellular carcinoma (HCC) were also compared. RESULTS: There were 39 patients in the positive group, including 29 males and 10 females, and 61 patients in the negative group, including 38 males and 23 females, with no statistically significant differences in gender and age distribution between the two groups (P > 0.05). The levels of serological indicators (TP, ALB, AST, GGT, ALP, TBIL, DBIL, IBIL, TC, TG, PLT, CG, HA, LN, PCIII, IV-C, AFP, CEA, and HBsAg) in both groups showed no significant differences (P > 0.05), but the ALT level in the positive group was higher than that in the negative group (P < 0.0001). The positive group had worse Child-Pugh grades and higher HCC incidence compared to the negative group (P < 0.0001, P = 0.028). CONCLUSIONS: Patients with HBV-related LC and HBV-DNA ≥ 20 IU/mL have higher serum ALT levels, worse liver function Child-Pugh grades, and higher HCC incidence than those with HBV-DNA < 20 IU/mL. High-sensitivity HBV-DNA quantification can reflect the deterioration of liver function in patients with HBV-related LC to some extent.

High-Throughput Fluorometric Assay For Quantifying Polysorbate In Biopharmaceutical Products Using Micelle Activated Fluorescence Probe N-Phenyl-1-Naphthylamine.

PURPOSE: Polysorbates are among the most used surfactants in biopharmaceutical products containing proteins. Our work aims to develop a high-throughput fluorometric assay to further diversify the analytical toolbox for quantification of PSs. METHOD: The assay leverages the micelle activated fluorescence signal from N-Phenyl-1-Naphthylamine (NPN). The development and optimization of assay parameters were guided by the pre-defined analytical target profile. Furthermore, NMR was used to probe the interaction between protein, PS80 and NPN in the measurement system and understand protein interference. RESULTS: All assay parameters including excitation and emission wavelengths, standard curve, NPN concentration, and incubation time have been optimized and adapted to a microplate format, making it compatible with automated solutions that will be pursued in the near future to drive consistency and efficiency in our workflows. The specificity, accuracy, and precision of the assay have been demonstrated through a case study. Furthermore, NMR results provided additional insight into the change of the interaction dynamics between PS80 and NPN as the protein concentration increases. The results indicate minimal interaction between the protein and PS80 at lower concentration. However, when the concentration exceeds 75 mg/mL, there is a significant interaction between the protein and PS-80 micelle and monomer. CONCLUSION: A high-throughput fluorometric assay has been developed for quantification of polysorbates in biopharmaceutical samples including in-process samples, drug substance and drug product. The assay reported herein could serve as a powerful analytical tool for polysorbate quantification and control, complementing the widely used liquid chromatography with charged aerosol detection method.

Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing.

BACKGROUND: Identification of human leukocyte antigen (HLA) types from DNA-sequenced human samples is important in organ transplantation and cancer immunotherapy and remains a challenging task considering sequence homology and extreme polymorphism of HLA genes. RESULTS: We present Orthanq, a novel statistical model and corresponding application for transparent and uncertainty-aware quantification of haplotypes. We utilize our approach to perform HLA typing while, for the first time, reporting uncertainty of predictions and transparently observing mutations beyond reported HLA types. Using 99 gold standard samples from 1000 Genomes, Illumina Platinum Genomes and Genome In a Bottle projects, we show that Orthanq can provide overall superior accuracy and shorter runtimes than state-of-the-art HLA typers. CONCLUSIONS: Orthanq is the first approach that allows to directly utilize existing pangenome alignments and type all HLA loci. Moreover, it can be generalized for usages beyond HLA typing, e.g. for virus lineage quantification. Orthanq is available under https://orthanq.github.io .

Quantification of volumetric thigh and paravertebral muscle fat content: comparison of quantitative Dixon (Q-Dixon) magnetic resonance imaging (MRI) with high-speed T2-corrected multiecho MR spectroscopy.

Background: Muscle fat infiltration (MFI) is increasingly recognized as a critical factor influencing muscle function and metabolic health. Accurate quantification of MFI is essential for diagnosing and monitoring various muscular and metabolic disorders. Quantitative Dixon (Q-Dixon) magnetic resonance imaging (MRI) and high-speed T2-corrected multi-echo (HISTO) magnetic resonance spectroscopy (MRS) are both advanced imaging techniques that offer potential for detailed assessment of MFI. However, the validity and reliability of these methods in measuring volumetric changes in muscle composition, particularly in both thigh and paravertebral muscles, have not been thoroughly compared. This study aims to validate volumetric measurements using Q-Dixon MRI against HISTO MRS in thigh and paravertebral muscles, taking into account the heterogeneity of MFI. Methods: A retrospective study was conducted with 54 subjects [mean age, 60 years; 38 male (M)/16 female (F)] for thigh muscle and 56 subjects (mean age, 50 years; 22 M/34 F) for paravertebral muscle assessment using a 3-Tesla MRI. The proton density fat fraction (PDFF) was measured with Q-Dixon MRI and HISTO MRS within the upper-middle part of quadriceps femoris and paravertebral muscles at L4/5 level in volumes-of-interest (VOIs). The corresponding volumetric Q-Dixon freehand VOI PDFF was measured. Scatterplots, Bland-Altman plots, Spearman correlation coefficients, and Wilcoxon signed rank test with Bonferroni correction were employed. The Kruskal-Wallis H tests followed by Bonferroni-corrected post hoc tests were analyzed to compare parameter differences with visual MFI grades. Results: Q-Dixon cubic VOI PDFF correlated positively with HISTO MRS PDFF in thigh (r=0.96, P<0.001) and paravertebral groups (r=0.98, P<0.001), with insignificant differences (P=0.29, 0.82, respectively). Both PDFF values from cubic VOIs in Q-Dixon and HISTO MRS differed from the freehand Q-Dixon PDFF (all P<0.001). Only for <5% HISTO MRS PDFF, there was a difference between HISTO MRS PDFF and Q-Dixon cubic VOI PDFF (P=0.002). Conclusions: Volumetric Q-Dixon cubic VOI PDFF exhibited good correlation and consistency with HISTO MRS PDFF for quantitative fat assessment in thigh and paravertebral muscles except for muscles with fat fraction <5%, and the Q-Dixon freehand VOI PDFF offers a more comprehensive assessment of the actual MFI compared to cubic VOI.

Portable Near-Infrared to Near-Infrared Platform for Homogeneous Quantification of Biomarkers in Complex Biological Samples.

Förster resonance energy transfer (FRET)-based homogeneous immunoassay obviates tedious washing steps and thus is a promising approach for immunoassays. However, a conventional FRET-based homogeneous immunoassay operating in the visible region is not able to overcome the interference of complex biological samples, thus resulting in insufficient detection sensitivity and poor accuracy. Here, we develop a near-infrared (NIR)-to-NIR FRET platform (Ex = 808 nm, Em = 980 nm) that enables background-free high-throughput homogeneous quantification of various biomarkers in complex biological samples. This NIR-to-NIR FRET platform is portable and easy to operate and is mainly composed of a high-performance NIR-to-NIR FRET pair based on lanthanide-doped nanoparticles (LnNPs) and a custom-made microplate reader for readout of NIR luminescence signals. We demonstrate that this NIR-to-NIR FRET platform is versatile and robust, capable of realizing highly sensitive and accurate detection of various critical biomarkers, including small molecules (morphine and 1,25-dihydroxyvitamin D), proteins (human chorionic gonadotropin), and viral particles (adenovirus) in unprocessed complex biological samples (urine, whole blood, and feces) within 5-10 min. We expect this NIR-to-NIR FRET platform to provide low-cost healthcare for populations living in resource-limited areas and be widely used in many other fields, such as food safety and environmental monitoring.

Non-complete recovery of temporal lobe white matter diffusion metrics at one year Post-Radiotherapy: Implications for Radiation-Induced necrosis risk.

BACKGROUND: Temporal lobe (TL) white matter (WM) injuries are often seen early after radiotherapy (RT) in nasopharyngeal carcinoma patients (NPCs), which fail to fully recover in later stages, exhibiting a "non-complete recovery pattern". Herein, we explored the correlation between non-complete recovery WM injuries and TL necrosis (TLN), identifying dosimetric predictors for TLN-related high-risk WM injuries. METHODS: We longitudinally examined 161 NPCs and 19 healthy controls employing multi-shell diffusion MRI. Automated fiber-tract quantification quantified diffusion metrics within TL WM tracts segments. ANOVA identified non-complete recovery WM tract segments one-year post-RT. Cox regression models discerned TLN risk factors utilizing non-complete recovery diffusion metrics. Normal tissue complication probability (NTCP) models and dose-response analysis further scrutinized RT-related toxicity to high-risk WM tract segments. RESULTS: Seven TL WM tract segments exhibited a " non-complete recovery pattern ". Cox regression analysis identified mean diffusivity of left uncinate fasciculus segment 1, neurite density index (NDI) of the left cingulum hippocampus segment 1, and NDI of the right inferior longitudinal fasciculus segment 1 as TLN risk predictors (hazard ratios [HRs] with confidence interval [CIs] 1.45 [1.17-1.81], 1.07 [1.00-1.15], 1.15 [1.03-1.30], respectively; all P-values < 0.05). In NTCP models, D10cc.L, D20cc.L and D10cc.R demonstrated superior performance, with TD50 of 37.22 Gy, 24.96 Gy and 37.28 Gy, respectively. CONCLUSIONS: Our findings underscore the significance of the "non-complete recovery pattern" in TL WM tract segment injuries during TLN development. Understanding TLN-related high-risk WM tract segments and their tolerance doses could facilitate early intervention in TLN and improve RT protocols.

A Bayesian convolutional neural network-based generalized linear model.

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.

An Optimized Method for LC-MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer.

Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC-MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Multiplexed bacterial pathogen detection and clinical characteristics of orthopedic infection in hospitalized patients.

Introduction: Accurate identification of the etiology of orthopedic infection is very important for correct and timely clinical management, but it has been poorly studied. In the current study we explored the association of multiple bacterial pathogens with orthopedic infection. Methods: Hospitalized orthopedic patients were enrolled in a rural hospital in Qingdao, China. Wound or exudate swab samples were collected and tested for twelve bacterial pathogens with both culture and multiplex real time PCR. Results and discussion: A total of 349 hospitalized orthopedic patients were enrolled including 193 cases presenting infection manifestations upon admission and 156 with no sign of infection. Orthopedic infection patients were mainly male (72.5%) with more lengthy hospital stay (median 15 days). At least one pathogen was detected in 42.5% (82/193) of patients with infection while 7.1% (11/156) in the patients without infection (P < 0.001). S. aureus was the most prevalent causative pathogen (15.5%). Quantity dependent pathogen association with infection was observed, particularly for P. aeruginosa and K. pneumoniae, possibly indicating subclinical infection. Most of the patients with detected pathogens had a previous history of orthopedic surgery (odds ratio 2.8, P = 0.038). Pathogen specific clinical manifestations were characterized. Multiplex qPCR, because of its high sensitivity, superior specificity, and powerful quantification could be utilized in combination with culture to guide antimicrobial therapy and track the progression of orthopedic infection during treatment.