High-throughput, low-cost quantification of 11 therapeutic antibodies using caprylic acid precipitation and LC-MS/MS.

BACKGROUND: Therapeutic drug monitoring of treatment with therapeutic antibodies is hampered by the application of a wide range of different methods in the quantification of serum levels. LC-MS based methods could significantly improve comparability of results from different laboratories, but such methods are often considered complicated and costly. We developed a method for LC-MS/MS based quantification of 11 therapeutic antibodies concomitantly measured in a single run, with emphasis on simplicity in sample preparation and low cost. RESULTS: After a single-step sample purification using caprylic acid precipitation to remove interfering proteins, the sample underwent proteolysis followed by LC-MS/MS analysis. Infliximab is used as internal standard for sample preparation while isotope-labeled signature peptides identified for each analyte are internal standards for the LC-MS/MS normalization. The method was validated according to recognized guidelines, and pipetting steps can be performed by automated liquid handling systems. The total precision of the method ranged between 2.7 and 7.3 % (5.1 % average) across the quantification range of 4-256 µg/ml for all 11 drugs, with an average accuracy of 96.3 %. Matrix effects were xamined in 55 individual patient samples instead of the recommended 6, and 147 individual patient samples were screened for interfering compounds. SIGNIFICANCE AND NOVELTY: Our method for simultaneous quantification of 11 t-mAb in human serum allows an unprecedented integration of robustness, speed and reduced complexity, which could pave the way for uniform use in research projects and clinical settings alike. In addition, the first LC-MS protocol for signature peptide-based quantification of durvalumab is described. This high throughput method can be readily adapted to a drug panel of choice.

Modeling correlated uncertainties in stochastic compartmental models.

We consider compartmental models of communicable disease with uncertain contact rates. Stochastic fluctuations are often added to the contact rate to account for uncertainties. White noise, which is the typical choice for the fluctuations, leads to significant underestimation of the disease severity. Here, starting from reasonable assumptions on the social behavior of individuals, we model the contacts as a Markov process which takes into account the temporal correlations present in human social activities. Consequently, we show that the mean-reverting Ornstein-Uhlenbeck (OU) process is the correct model for the stochastic contact rate. We demonstrate the implication of our model on two examples: a Susceptibles-Infected-Susceptibles (SIS) model and a Susceptibles-Exposed-Infected-Removed (SEIR) model of the COVID-19 pandemic and compare the results to the available US data from the Johns Hopkins University database. In particular, we observe that both compartmental models with white noise uncertainties undergo transitions that lead to the systematic underestimation of the spread of the disease. In contrast, modeling the contact rate with the OU process significantly hinders such unrealistic noise-induced transitions. For the SIS model, we derive its stationary probability density analytically, for both white and correlated noise. This allows us to give a complete description of the model's asymptotic behavior as a function of its bifurcation parameters, i.e., the basic reproduction number, noise intensity, and correlation time. For the SEIR model, where the probability density is not available in closed form, we study the transitions using Monte Carlo simulations. Our modeling approach can be used to quantify uncertain parameters in a broad range of biological systems.

Mechanism of Guigan Longmu Decoction in the Treatment of Arrhythmias Based on Network Pharmacology and Untargeted Metabolomics Assays.

BACKGROUND: Guigan longmu decoction (GGLM), a traditional Chinese medicine compound, has demonstrated efficacy in treating rapid arrhythmia clinically. Nevertheless, its mechanism of action remains elusive. This study aims to elucidate the molecular mechanism underlying the efficacy of GGLM in treating arrhythmia utilizing non-targeted metabolomics, widely-targeted metabolomics, and network pharmacology, subsequently validated through animal experiments. METHODS: Initially, network pharmacology analysis and widely-targeted metabolomics were performed on GGLM. Subsequent to that, rats were administered GGLM intervention, and nontargeted metabolomics assays were utilized to identify metabolites in rat plasma postadministration. The primary signaling pathways, core targets, and key active ingredients of GGLM influencing arrhythmia were identified. Additionally, to validate the therapeutic efficacy of GGLM on arrhythmia rat models, a rat model of rapid arrhythmia was induced via subcutaneous injection of isoproterenol, and alterations in pertinent pathogenic pathways and proteins in the rat model were assessed through qRT-PCR and Western blot following GGLM administration. RESULTS: The results of network pharmacology showed that 99 active ingredients in GGLM acted on 249 targets and 201 signaling pathways, which may be key to treating arrhythmia. Widelytargeted metabolic quantification analysis detected a total of 448 active ingredients in GGLM, while non-targeted metabolomics identified 279 different metabolites and 10 major metabolic pathways in rats. A comprehensive analysis of the above results revealed that the core key active ingredients of GGLM in treating arrhythmia include calycosin, licochalcone B, glabridin, naringenin, medicarpin, formononetin, quercetin, isoliquiritigenin, and resveratrol. These active ingredients mainly act on the relevant molecules and proteins upstream and downstream of the MAPK pathway to delay the onset of arrhythmia. Animal experimental results showed that the heart rate of rats in the model group increased significantly, and the mRNA and protein expression of p38, MAPK, JNK, ERK, NF-kb, IL-1ß, and IL-12 in myocardial tissue also increased significantly. However, after intervention with GGLM, the heart rate of rats in the drug group decreased significantly, while the mRNA and protein expression of p38 MAPK, JNK, ERK1, NF-kb, IL-1ß, and IL-12 in myocardial tissue decreased significantly. CONCLUSION: GGLM, as an adjunctive therapy in traditional Chinese medicine, exhibits favorable therapeutic efficacy against arrhythmia. This can be attributed to the abundant presence of bioactive compounds in the formulation, including verminin, glycyrrhizin B, glabridine, naringenin, ononin, quercetin, isorhamnetin, and kaempferol. The metabolites derived from these active ingredients have the potential to mitigate myocardial inflammation and decelerate heart rate by modulating the expression of proteins associated with the MAPK signaling pathway in vivo.

Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review.

Purpose: This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach to efficiently assist clinicians in the manual review of predicted hypnograms, a necessity due to the notable inter-scorer variability inherent in polysomnography (PSG) databases. Our efforts target the extent of review required to achieve predefined agreement levels, examining both in-domain (ID) and out-of-domain (OOD) data, and considering subjects' diagnoses. Patients and Methods: A total of 19,578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical database of an additional 8832 PSGs, covering a full spectrum of ages (0-91 years) and sleep-disorders, to refine the U-Sleep, and to evaluate different uncertainty-quantification approaches, including our novel confidence network. The ID data consisted of PSGs scored by over 50 physicians, and the two OOD sets comprised recordings each scored by a unique senior physician. Results: U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID and 73.8-78.8% on OOD data. The confidence network excelled at identifying uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on OOD data. Independently of sleep-disorder status, statistical evaluations revealed significant differences in confidence scores between aligning vs discording predictions, and significant correlations of confidence scores with classification performance metrics. To achieve κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians' workload, and facilitating near-perfect agreement. Conclusion: Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools' usability in clinical settings.

Evaluation of two commercial diagnostic methods for HHV-8 viral load assessment.

Objectives: Human herpesvirus-8 (HHV-8) can cause Kaposi's sarcoma or B lymphoproliferative disorders such as multicentric Castleman disease. Patient follow-up is based on assessing the HHV-8 viral load, which is usually achieved using real-time polymerase chain reaction (PCR). The HHV-8 Premix r-gene kit (BioMérieux) was used by some laboratories in the past, but BioMérieux ceased the production and distribution of this kit in 2021-2022. Other kits need to be tested so that they can be used for diagnostic purposes. Here we evaluated two commercial kits: HHV8 ELITe MGB Kit (ELITech) and Quanty HHV-8 (Clonit) and compared them with the HHV-8 Premix r-gene kit. Methods: We used whole blood samples that had previously been tested with the HHV-8 Premix r-gene kit for diagnostic purposes. Overall, 46 samples (37 HHV-8-positive and 9 HHV-8-negative) were tested with the ELITe MGB Kit and 37 (29 HHV-8-positive and 8 HHV-8-negative) with the Quanty HHV-8 kit. The different methods were compared using Bland-Altman and Passing-Bablok tests with Analyse-it software. Results: Qualitative results were concordant except for one HHV-8 low-positive sample that was found to be negative by the ELITe MGB Kit. The quantitative results were also concordant; both kits showed mean differences of 0.58 log10 copies/ml and 0.73 log10 copies/ml, respectively, compared to the Premix r-gene kit. Conclusions: Both the methods tested produced acceptable results and could be used for diagnostic purposes. It should be remembered that there is no international standard for HHV-8 quantification and that patients should always be followed using the same method.

Recurrent attention U-Net for segmentation and quantification of breast arterial calcifications on synthesized 2D mammograms.

Breast arterial calcifications (BAC) are a type of calcification commonly observed on mammograms and are generally considered benign and not associated with breast cancer. However, there is accumulating observational evidence of an association between BAC and cardiovascular disease, the leading cause of death in women. We present a deep learning method that could assist radiologists in detecting and quantifying BAC in synthesized 2D mammograms. We present a recurrent attention U-Net model consisting of encoder and decoder modules that include multiple blocks that each use a recurrent mechanism, a recurrent mechanism, and an attention module between them. The model also includes a skip connection between the encoder and the decoder, similar to a U-shaped network. The attention module was used to enhance the capture of long-range dependencies and enable the network to effectively classify BAC from the background, whereas the recurrent blocks ensured better feature representation. The model was evaluated using a dataset containing 2,000 synthesized 2D mammogram images. We obtained 99.8861% overall accuracy, 69.6107% sensitivity, 66.5758% F-1 score, and 59.5498% Jaccard coefficient, respectively. The presented model achieved promising performance compared with related models.

Using Publicly Available RNA-seq Data for Expression Analysis of Genes of Interest.

RNA-seq data in publicly available repositories enable the efficient reanalysis of transcript abundances in existing experiments. Graphical user interfaces usually only allow the visual inspection of a single gene and of predefined experiments. Here, we describe how experiments are selected from the Sequence Read Archive or the European Nucleotide Archive, how data is efficiently mapped onto a reference transcriptome, and how global transcript abundances and patterns are inspected. We exemplarily apply this analysis pipeline to study the expression of photorespiration-related genes in photosynthetic organisms, such as cyanobacteria, and to identify conditions under which photorespiratory transcript abundances are enhanced.

A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture.

Hip fractures present a significant healthcare challenge, especially within aging populations, where they are often caused by falls. These fractures lead to substantial morbidity and mortality, emphasizing the need for timely surgical intervention. Despite advancements in medical care, hip fractures impose a significant burden on individuals and healthcare systems. This paper focuses on the prediction of hip fracture risk in older and middle-aged adults, where falls and compromised bone quality are predominant factors. We propose a novel staged model that combines advanced imaging and clinical data to improve predictive performance. By using convolutional neural networks (CNNs) to extract features from hip DXA images, along with clinical variables, shape measurements, and texture features, our method provides a comprehensive framework for assessing fracture risk. The study cohort included 547 patients, with 94 experiencing hip fracture. A staged machine learning-based model was developed using two ensemble models: Ensemble 1 (clinical variables only) and Ensemble 2 (clinical variables and DXA imaging features). This staged approach used uncertainty quantification from Ensemble 1 to decide if DXA features are necessary for further prediction. Ensemble 2 exhibited the highest performance, achieving an Area Under the Curve (AUC) of 0.9541, an accuracy of 0.9195, a sensitivity of 0.8078, and a specificity of 0.9427. The staged model also performed well, with an AUC of 0.8486, an accuracy of 0.8611, a sensitivity of 0.5578, and a specificity of 0.9249, outperforming Ensemble 1, which had an AUC of 0.5549, an accuracy of 0.7239, a sensitivity of 0.1956, and a specificity of 0.8343. Furthermore, the staged model suggested that 54.49% of patients did not require DXA scanning. It effectively balanced accuracy and specificity, offering a robust solution when DXA data acquisition is not always feasible. Statistical tests confirmed significant differences between the models, highlighting the advantages of the advanced modeling strategies. Our staged approach offers a cost-effective holistic view of patients' health. It could identify individuals at risk with a high accuracy but reduce the unnecessary DXA scanning. Our approach has great promise to guide interventions to prevent hip fractures with reduced cost and radiation.

Bioprocess monitoring applications of an innovative ATR-FTIR spectroscopy platform.

Pharmaceutical manufacturing is reliant upon bioprocessing approaches to generate the range of therapeutic products that are available today. The high cost of production, susceptibility to process failure, and requirement to achieve consistent, high-quality product means that process monitoring is paramount during manufacturing. Process analytic technologies (PAT) are key to ensuring high quality product is produced at all stages of development. Spectroscopy-based technologies are well suited as PAT approaches as they are non-destructive and require minimum sample preparation. This study explored the use of a novel attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy platform, which utilises disposable internal reflection elements (IREs), as a method of upstream bioprocess monitoring. The platform was used to characterise organism health and to quantify cellular metabolites in growth media using quantification models to predict glucose and lactic acid levels both singularly and combined. Separation of the healthy and nutrient deficient cells within PC space was clearly apparent, indicating this technique could be used to characterise these classes. For the metabolite quantification, the binary models yielded R 2 values of 0.969 for glucose, 0.976 for lactic acid. When quantifying the metabolites in tandem using a multi-output partial least squares model, the corresponding R 2 value was 0.980. This initial study highlights the suitability of the platform for bioprocess monitoring and paves the way for future in-line developments.

Quantification of multiple steroid hormones in serum and human breast cancer tissue by liquid chromatography-tandem mass spectrometry analysis.

Introduction: Systemic and local steroid hormone levels may function as novel prognostic and predictive biomarkers in breast cancer patients. We aimed at developing a novel liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the simultaneous measurement of multiple, biologically pivotal steroid hormones in human serum and breast cancer tissue. Methods: The quantitative method consisted of liquid-liquid extraction, Sephadex LH-20 chromatography for tissue extracts, and analysis of steroid hormones by liquid-chromatography-tandem mass spectrometry. We analyzed serum and tissue steroid hormone levels in 16 and 40 breast cancer patients, respectively, and assessed their correlations with clinical parameters. Results: The method included quantification of nine steroid hormones in serum [including cortisol, cortisone, corticosterone, estrone (E1), 17ß-estradiol (E2), 17α-hydroxyprogesterone, androstenedione (A4), testosterone and progesterone) and six (including cortisone, corticosterone, E1, E2, A4, and testosterone) in cancer tissue. The lower limits of quantification were between 0.003-10 ng/ml for serum (250 µl) and 0.038-125 pg/mg for tissue (20 mg), respectively. Accuracy was between 98%-126%, intra-assay coefficient of variations (CV) was below 15%, and inter-assay CV were below 11%. The analytical recoveries for tissue were between 76%-110%. Tissue levels of E1 were positively correlated with tissue E2 levels (p<0.001), and with serum levels of E1, E2 and A4 (p<0.01). Tissue E2 levels were positively associated with serum E1 levels (p=0.02), but not with serum E2 levels (p=0.12). The levels of tissue E2 and ratios of E1 to A4 levels (an index for aromatase activity) were significantly higher in patients with larger tumors (p=0.03 and p=0.02, respectively). Conclusions: The method was convenient and suitable for a specific and accurate profiling of clinically important steroid hormones in serum. However, the sensitivity of the profile method in steroid analysis in tissue samples is limited, but it can be used for the analysis of steroids in breast cancer tissues if the size of the sample or its steroid content is sufficient.

Quantifying fetal heart health in gestational diabetes: a new approach with fetal heart quantification technology.

Objective: This study aimed to assess the impact of gestational diabetes mellitus (GDM) on fetal heart structure and function using a technique called fetal heart quantification (Fetal HQ), with a focus on mitochondrial dynamics, which employs advanced imaging technology for comprehensive analysis. Methods: A total of 180 fetuses with normal heart structures, aged 24-40 weeks of gestation, were examined. A 2-3 s cine loop in the standard four-chamber oblique view was captured and analyzed using the speckle-tracking technique with Fetal HQ. Various echocardiographic parameters were evaluated, including four-chamber view (4CV), global spherical index (GSI), global longitudinal strain (GLS), 24-segment spherical index (SI), ventricular fractional area change (FAC), cardiac output (CO), and stroke volume (SV). These parameters were compared between the GDM group and the control group during two gestational periods: 24+0 to 28+0 weeks and 28+1 to 40+1 weeks. Statistical analysis was performed using independent samples t-tests and Mann-Whitney U tests to identify significant differences. Results: Twenty fetuses from mothers with GDM and 40 from the control group were recruited at 24+0 to 28+0 weeks. At 28+1 to 40+1 weeks, 40 fetuses from mothers with GDM and 80 from the control group were recruited. The fetal left ventricular global longitudinal function was similar between the GDM and control groups. However, compared to the controls, right ventricular function in the GDM group was lower only at 28+1 to 40+1 weeks. In the GDM group, the global spherical index (GSI) was lower than in the control group at 28+1 to 40+1 weeks (1.175 vs. 1.22; p = 0.001). There were significant decreases in ventricular FAC (38.74% vs. 42.83%; p < 0.0001) and 4CV GLS for the right ventricle (-22.27% vs. -26.31%; p = 0.005) at 28+1 to 40+1 weeks. Conclusion: Our findings suggest that GDM is associated with decreased right ventricular function in the fetal heart, particularly during the later stages of pregnancy (28+1 to 40+1 weeks), compared to fetuses from healthy pregnancies. The Fetal HQ technique represents a valuable tool for evaluating the structure and function of fetal hearts affected by GDM during the advanced stages of pregnancy.

Assessing the Progress of the Performance of Continuous Monitoring Solutions under a Single-Blind Controlled Testing Protocol.

The recent regulatory spotlight on continuous monitoring (CM) solutions and the rapid development of CM solutions have demanded the characterization of solution performance through regular, rigorous testing using consensus test protocols. This study is the second known implementation of such a protocol involving single-blind controlled testing of 9 CM solutions. Controlled releases of rates (6-7100 g) CH4/h over durations (0.4-10.2 h) under a wind speed range of (0.7-9.9 m/s) were conducted for 11 weeks. Results showed that 4 solutions achieved method detection limits (DL90s) within the tested emission rate range, with all 4 solutions having both the lowest DL90s (3.9 [3.0, 5.5] kg CH4/h to 6.2 [3.7, 16.7] kg CH4/h) and false positive rates (6.9-13.2%), indicating efforts at balancing low sensitivity with a low false positive rate. These results are likely best-case scenario estimates since the test center represents a near-ideal upstream field natural gas operation condition. Quantification results showed wide individual estimate uncertainties, with emissions underestimation and overestimation by factors up to >14 and 42, respectively. Three solutions had >80% of their estimates within a quantification factor of 3 for controlled releases in the ranges of [0.1-1] kg CH4/h and > 1 kg CH4/h. Relative to the study by Bell et al., current solutions performance, as a group, generally improved, primarily due to solutions from the study by Bell et al. that were retested. This result highlights the importance of regular quality testing to the advancement of CM solutions for effective emissions mitigation.

Distribution of polyelectrolytes and counterions upon polyelectrolyte complexation.

HYPOTHESIS: Understanding polyelectrolyte complexation remains limited due to the absence of a systematic methodology for analyzing the distribution of components between the polyelectrolyte complex (PEC) and the dilute phases. EXPERIMENTS: We developed a methodology based on NMR to quantify all components of solid-like PECs and their supernatant phases formed by mixing different ratios of poly(allylamine hydrochloride) (PAH) and poly(acrylic acid)-sodium salt (PAA). This approach allowed for determining relative and absolute concentrations of polyelectrolytes in both phases by 1H NMR studies. Using 23Na and 35Cl NMR spectroscopy we measured the concentration of counterions in both phases. FINDINGS: Regardless of the mixing ratio of the polyelectrolytes the PEC is charge-stoichiometric, and any excess polyelectrolytes to achieve charge stoichiometry remains in the supernatant phase. The majority of counterions were found in the supernatant phase, confirming counterion release being a major thermodynamic driving force for PEC formation. The counterion concentrations in the PEC phase were approximately twice as high as in the supernatant phase. The complete mass balance of PEC formation could be determined and translated into a molecular picture. It appears that PAH is fully charged, while PAA is more protonated, so less charged, and some 10% extrinsic PAH-Cl- pairs are present in the complex.

Optimization of QuEChERS cleanup for quantification of γ-oryzanol in vegetable oils by UHPLC-MS/MS.

This study was based on QuEChERS cleanup coupled with UHPLC-MS/MS for the determination of γ-oryzanol compounds in vegetable oils. Several parameters of QuEChERS and UHPLC-MS/MS were studied for purification and detection of γ-oryzanol compounds in oil samples. Under the optimized conditions, the whole pretreatment procedure could be accomplished within 10 min without tedious procedure, larger volume of organic solvent and complicated apparatus. The limit of detections and the limit of quantifications for γ-oryzanol compounds were ranging from 0.1-0.3 µg kg-1 and 0.4-1.0 µg kg-1, respectively. Satisfactory recoveries of all analyts were ranging from 72.2 % to 101.3 %, and the intra-day and inter-day precision were less than 10.6 %. The validation indicated that rice band oil and corn oil were rich in 24-mCAF, CAF, ß-SIF, CMF and STF. The QuEChERS-UHPLC-MS/MS simultaneously quantified five γ-oryzanol compounds in lipid matrices and assessed the nutritional and functional substances of vegetable oils.

Rapid determination of germanium in lignite coal and coal-related solid byproducts by graphite furnace digestion inductively coupled plasma emission spectroscopy.

This study developed a rapid and efficient graphite furnace digestion combined with inductively coupled plasma emission spectrometry (ICP-OES) method, enabling precise quantification of germanium (Ge) in coal and various coal-derived metallurgical byproducts across a broad concentration level (∼ppm-n%). The graphite furnace digestion conditions were examined intensively as a function of the acid amounts of HNO3 and HF (5-10 mL), temperature (80-180 °C), time (1-5 h), and acid drive methods (H3BO3 neutralization versus heating). Coal references including SARM 19, SARM 20, NIST SRM 1632e, and fly ash standard NIST SRM 2689 were tested. The optimum recovery of germanium was obtained when the HNO3 dosage, HF dosage, solid sample mass, temperature, and duration time were 10 mL, 5 mL, 0.1 g, 80 °C and 1 h. Agreement of 95.15-96.54 % between the measured and certified value was obtained under the optimum conditions. The spiked recovery was 103.23-103.54 %, indicating the matrix-analytes interactions were negligible. Boric acid neutralization reduced the recovery rates to 47.2-49.3 % and was not be appropriate for driving HF. The optimal spectral line for determining Ge is at a wavelength of 265.117 nm, at which the limit of detect and the limit of quantification were 0.46 µg L-1 and 1.53 µg L-1, respectively. The applicability of the method was validated by quantifying Ge in Ge-rich lignite, fly ashes (FA), and chlorinated distillation residue (CR) samples. The concentration of Ge in coals was 44.75-225.41 µg g-1, the content in FA was 0.68%-2.3 %, and the content in CR was 0.18 %, with the uncertainty of the method obtained being less than 0.5 %. X-ray fluorescence spectrometer (XRF) was used to verify the results. The difference between XRF data and ICP-OES data was less than 5 %, confirming the accuracy and reproductivity of the analytical method.

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.

Biotechnological approaches for the production of camptothecin.

Camptothecin (CPT), an indole alkaloid popular for its anticancer property, is considered the third most promising drug after taxol and famous alkaloids from Vinca for the treatment of cancer in humans. Camptothecin was first identified in Camptotheca acuminata followed by several other plant species and endophytic fungi. Increased harvesting driven by rising global demand is depleting the availability of elite plant genotypes, such as Camptotheca acuminata and Nothapodytes nimmoniana, crucial for producing alkaloids used in treating diseases like cancer. Conservation of these genotypes for the future is imperative. Therefore, research on different plant tissue culture techniques such as cell suspension culture, hairy roots, adventitious root culture, elicitation strategies, and endophytic fungi has been adopted for the production of CPT to meet the increasing demand without affecting the source plant's existence. Currently, another strategy to increase camptothecin yield by genetic manipulation is underway. The present review discusses the plants and endophytes that are employed for camptothecin production and throws light on the plant tissue culture techniques for the regeneration of plants, callus culture, and selection of cell lines for the highest camptothecin production. The review further explains the simple, accurate, and cost-effective extraction and quantification methods. There is enormous potential for the sustainable production of CPT which could be met by culturing of suitable endophytes or plant cell or organ culture in a bioreactor scale production. Also, different gene editing tools provide opportunities for engineering the biosynthetic pathway of CPT, and the overall CPT production can be improved . KEY POINTS: • Camptothecin is a naturally occurring alkaloid with potent anticancer properties, primarily known for its ability to inhibit DNA topoisomerase I. • Plants and endophytes offer a potential approach for camptothecin production. • Biotechnology approaches like plant tissue culture techniques enhanced camptothecin production.

Revolutionizing disease diagnosis and management: Open-access magnetic resonance imaging datasets a challenge for artificial intelligence driven liver iron quantification.

Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL) techniques, such as convolutional neural networks (CNNs), have emerged as transformative technologies with vast potential in healthcare. Body iron load is usually assessed using slightly invasive blood tests (serum ferritin, serum iron, and serum transferrin). Serum ferritin is widely used to assess body iron and drive medical management; however, it is an acute phase reactant protein offering wrong interpretation in the setting of inflammation and distressed patients. Magnetic resonance imaging is a non-invasive technique that can be used to assess liver iron. The ML and DL algorithms can be used to enhance the detection of minor changes. However, a lack of open-access datasets may delay the advancement of medical research in this field. In this letter, we highlight the importance of standardized datasets for advancing AI and CNNs in medical imaging. Despite the current limitations, embracing AI and CNNs holds promise in revolutionizing disease diagnosis and treatment.

Bioinspired amino acid-functionalized cobalt ferrite nanocomposite: A nanozyme-based colorimetric sensor for sensitive and selective quantification of phenolic compounds and ascorbic acid antioxidant capacity.

The escalating oxidative stress has heightened the daily human demand for diverse antioxidants. Therefore, development of the novel approaches to assess the total antioxidant capacity (TAC) of various nutrients is essential. In this study, drawing inspiration from the active site of native peroxidase enzymes, a novel peroxidase (POD)-like nanozyme was developed based on the cobalt ferrite (CoFe2O4) nanoparticles functionalized with different catalytic amino acids. Based on the TMB/H2O2 colorimetric system, the most substantial enhancement in POD-like activity was obtained by the glutamic acid coating among different charged amino acids studied, with more than 74% increase in specific activity compared to the bare CoFe2O4. A signal-off colorimetric sensing platform based on the obtained nanobiocatalyst was developed for the accurate quantification of the antioxidant capacity of phenolic compounds and vitamin C. The sensitive and selective quantification of ascorbic acid, tannic acid, gallic acid, cyanidin-3-glucoside, and quercetin was obtained by this colorimetric method.

Development and evaluation of one-step RT-qPCR TaqMan multiplex panels applied to six viruses occurring in lily and tulip bulbs.

One-step RT-qPCR TaqMan assays have been developed for six plant viruses with considerable economic impact in the growing of tulip and lily bulbs: lily mottle virus, lily symptomless virus, lily virus X, Plantago asiatica mosaic virus, tulip breaking virus and tulip virus X. To enhance efficacy and cost-efficiency these assays were combined into multiplex panels. Four different multiplex panels were designed, each consisting of three virus assays and an adapted assay for the housekeeping gene nad5 of lilies and tulips, that acts as an internal amplification control. To eliminate false negative results due to variation in the viral genome sequences, for each target virus two assays were developed on distinct conserved genomic regions. Specificity, PCR efficiency and compatibility of primers and probes were tested using gBlock constructions. Diagnostic samples were used to evaluate the strategy. High Throughput Sequencing of a set of the diagnostic samples, further verified the presence or absence of the viruses in the RNA samples and sequence variations in the target genes. This interchangeable multiplex panel strategy may be a valuable tool for the detection of viruses in certification, surveys and virus diagnostics.