"Antibacterial effect and possible mechanism of action of 1,3,4-oxadiazole in Staphylococcus aureus".

Staphylococcus aureus is one of the main etiological agents causing foodborne diseases, and the development of new antibacterial agents is urgent. This study evaluated the antibacterial activity and the possible mechanism of action of the 1,3,4-oxadiazole LMM6 against S. aureus. The minimum inhibitory concentration (MIC) of LMM6 ranged from 1.95 to 7.81 µg ml-1. The time-kill assay showed that 48-h treatment at 1× to 8× MIC reduced S. aureus by 4 log colony forming unit (CFU), indicating a bacteriostatic effect. Regarding the possible mechanism of action of LMM6, there was accumulation of reactive oxygen species (ROS) and an increase in the absorption of crystal violet (∼50%) by the cells treated with LMM6 at 1× and 2× MIC for 6-12 h. In addition, there was increased propidium iodide uptake (∼84%) after exposure to LMM6 for 12 h at 2× MIC. After 48 h of treatment, 100% of bacteria had been injured. Scanning electron microscopy observations demonstrated that LMM6-treated cells were smaller compared with the untreated group. LMM6 exhibited bacteriostatic activity and its mechanism of action involves increase of intracellular ROS and disturbance of the cell membrane, which can be considered a key target for controlling the growth of S. aureus.

Linear multi-scale modeling of diffusion MRI data: A framework for characterization of oriented structures across length scales.

Diffusion-weighted magnetic resonance imaging (DW-MRI) has evolved to provide increasingly sophisticated investigations of the human brain's structural connectome in vivo. Restriction spectrum imaging (RSI) is a method that reconstructs the orientation distribution of diffusion within tissues over a range of length scales. In its original formulation, RSI represented the signal as consisting of a spectrum of Gaussian diffusion response functions. Recent technological advances have enabled the use of ultra-high b-values on human MRI scanners, providing higher sensitivity to intracellular water diffusion in the living human brain. To capture the complex diffusion time dependence of the signal within restricted water compartments, we expand upon the RSI approach to represent restricted water compartments with non-Gaussian response functions, in an extended analysis framework called linear multi-scale modeling (LMM). The LMM approach is designed to resolve length scale and orientation-specific information with greater specificity to tissue microstructure in the restricted and hindered compartments, while retaining the advantages of the RSI approach in its implementation as a linear inverse problem. Using multi-shell, multi-diffusion time DW-MRI data acquired with a state-of-the-art 3 T MRI scanner equipped with 300 mT/m gradients, we demonstrate the ability of the LMM approach to distinguish different anatomical structures in the human brain and the potential to advance mapping of the human connectome through joint estimation of the fiber orientation distributions and compartment size characteristics.

A ubiquitin-specific protease functions in regulating cell death and immune responses in rice.

Ubiquitin-specific proteases (UBPs) process deubiquitination in eukaryotic organisms and are widely involved in plant development and responses to environmental stress. However, their role in cell death and plant immunity remains largely unknown. Here, we identified a rice lesion mimic mutant (LMM) and cloned its causative gene, LMM22. Both dysfunction and overexpression of LMM22 gave rise to the hypersensitive response-like cell death, reactive oxygen species bursts, and activated defence responses. LMM22 encodes an active UBP that is localised to the endoplasmic reticulum (ER) and displays a constitutive expression pattern in rice. LMM22 interacts with SPOTTED LEAF 35 (SPL35), a coupling of ubiquitin conjugation to ER degradation domain-containing protein that is known to participate in ubiquitination and the regulation of cell death and disease response in rice. Additional analyses suggest that LMM22 can positively regulate and stabilise the abundance of SPL35 protein likely through its deubiquitination activity. These data therefore improve our understanding of the function of UBP in rice innate immune responses by demonstrating that LMM22 functions as a critical regulator of SPL35 in cell death and disease resistance.

LESION MIMIC MUTANT 1 confers basal resistance to Sclerotinia sclerotiorum in rapeseed via a salicylic acid-dependent pathway.

Rapeseed (Brassica napus) is a major edible oilseed crop consumed worldwide. However, its yield is seriously affected by infection from the broad-spectrum non-obligate pathogen Sclerotinia sclerotiorum due to a lack of highly resistant germplasm. Here, we identified a Sclerotinia-resistant and light-dependent lesion mimic mutant from an ethyl methanesulfonate-mutagenized population of the rapeseed inbred Zhongshuang 11 (ZS11) named lesion mimic mutant 1 (lmm1). The phenotype of lmm1 is controlled by a single recessive gene, named LESION MIMIC MUTANT 1 (LMM1), which mapped onto chromosome C04 by bulked segregant analysis within a 2.71-Mb interval. Histochemical analysis indicated that H2O2 strongly accumulated and cell death occurred around the lesion mimic spots. Among 877 differentially expressed genes (DEGs) between ZS11 and lmm1 leaves, 188 DEGs were enriched in the defense response, including 95 DEGs involved in systemic acquired resistance, which is consistent with the higher salicylic acid levels in lmm1. Combining bulked segregant analysis and transcriptome analysis, we identified a significantly up-regulated gene, BnaC4.PR2, which encodes ß-1,3-glucanase, as the candidate gene for LMM1. Overexpression of BnaC4.PR2 may induce a reactive oxygen species burst to trigger partial cell death and systemic acquired resistance. Our study provides a new genetic resource for S. sclerotiorum resistance as well as new insights into disease resistance breeding in B. napus.

Genome-Wide Association Analysis and Genomic Prediction of Thyroglobulin Plasma Levels.

Thyroglobulin (Tg) is an iodoglycoprotein produced by thyroid follicular cells which acts as an essential substrate for thyroid hormone synthesis. To date, only one genome-wide association study (GWAS) of plasma Tg levels has been performed by our research group. Utilizing recent advancements in computation and modeling, we apply a Bayesian approach to the probabilistic inference of the genetic architecture of Tg. We fitted a Bayesian sparse linear mixed model (BSLMM) and a frequentist linear mixed model (LMM) of 7,289,083 variants in 1096 healthy European-ancestry participants of the Croatian Biobank. Meta-analysis with two independent cohorts (total n = 2109) identified 83 genome-wide significant single nucleotide polymorphisms (SNPs) within the ST6GAL1 gene (p<5×10-8). BSLMM revealed additional association signals on chromosomes 1, 8, 10, and 14. For ST6GAL1 and the newly uncovered genes, we provide physiological and pathophysiological explanations of how their expression could be associated with variations in plasma Tg levels. We found that the SNP-heritability of Tg is 17% and that 52% of this variation is due to a small number of 16 variants that have a major effect on Tg levels. Our results suggest that the genetic architecture of plasma Tg is not polygenic, but influenced by a few genes with major effects.

Variance Estimation and Confidence Intervals from Genome-wide Association Studies Through High-dimensional Misspecified Mixed Model Analysis.

We study variance estimation and associated confidence intervals for parameters characterizing genetic effects from genome-wide association studies (GWAS) in misspecified mixed model analysis. Previous studies have shown that, in spite of the model misspecification, certain quantities of genetic interests are consistently estimable, and consistent estimators of these quantities can be obtained using the restricted maximum likelihood (REML) method under a misspecified linear mixed model. However, the asymptotic variance of such a REML estimator is complicated and not ready to be implemented for practical use. In this paper, we develop practical and computationally convenient methods for estimating such asymptotic variances and constructing the associated confidence intervals. Performance of the proposed methods is evaluated empirically based on Monte-Carlo simulations and real-data application.

Modeling adaptive response profiles in a vaccine clinical trial.

BACKGROUND: Vaccine clinical studies typically provide time-resolved data on adaptive response read-outs in response to the administration of that particular vaccine to a cohort of individuals. However, modeling such data is challenged by the properties of these time-resolved profiles such as non-linearity, scarcity of measurement points, scheduling of the vaccine at multiple time points. Linear Mixed Models (LMM) are often used for the analysis of longitudinal data but their use in these time-resolved immunological data is not common yet. Apart from the modeling challenges mentioned earlier, selection of the optimal model by using information-criterion-based measures is far from being straight-forward. The aim of this study is to provide guidelines for the application and selection of LMMs that deal with the challenging characteristics of the typical data sets in the field of vaccine clinical studies. METHODS: We used antibody measurements in response to Hepatitis-B vaccine with five different adjuvant formulations for demonstration purposes. We built piecewise-linear, piecewise-quadratic and cubic models with transformations of the axes with pre-selected or optimized knot locations where time is a numerical variable. We also investigated models where time is categorical and random effects are shared intercepts between different measurement points. We compared all models by using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Deviance Information Criterion (DIC), variations of conditional AIC and by visual inspection of the model fit in the light of prior biological information. RESULTS: There are various ways of dealing with the challenges of the data which have their own advantages and disadvantages. We explain these in detail here. Traditional information-criteria-based measures work well for the coarse selection of the model structure and complexity, however are not efficient at fine tuning of the complexity level of the random effects. CONCLUSIONS: We show that common statistical measures for optimal model complexity are not sufficient. Rather, explicitly accounting for model purpose and biological interpretation is needed to arrive at relevant models. TRIAL REGISTRATION: Clinical trial registration number for this study: NCT00805389, date of registration: December 9, 2008 (pro-active registration).

Bidirectional associations between food groups and depressive symptoms: longitudinal findings from the Invecchiare in Chianti (InCHIANTI) study.

This study investigated bidirectional associations between intake of food groups and depressive symptoms in 1058 Italian participants (aged 20-102 years) of the Invecchiare in Chianti study. Dietary intake, assessed with a validated FFQ, and depressive symptoms, measured with the Center for Epidemiologic Studies Depression scale (CES-D), were assessed at baseline and after 3, 6 and 9 years. Associations of repeated measurements of intakes of thirteen food groups with 3-year changes in depressive symptoms, and vice versa, were analysed using linear mixed models and logistic generalised estimating equations. Fish intake was inversely (quartile (Q)4 v. Q1, B=-0·97, 95 % CI -1·74, -0·21) and sweet food intake positively (Q4 v. Q1, B=1·03, 95 % CI 0·25, 1·81) associated with subsequent CES-D score. In the other direction, higher CES-D scores were associated with decreases in intakes of vegetables (ratio: 0·995, 95 % CI 0·990, 0·999) and red and processed meat (B=-0·006, 95 % CI -0·010, -0·001), an increase in dairy product intake (ratio: 1·008, 95 % CI 1·004, 1·013), and increasing odds of eating savoury snacks (OR: 1·012, 95 % CI 1·000, 1·024). Fruit, nuts and legumes, potatoes, wholegrain bread, olive oil, sugar-sweetened beverages, and coffee and tea were not significantly associated in either direction. Our study confirmed bidirectional associations between food group intakes and depressive symptoms. Fish and sweet food intakes were associated with 3-year improvement and deterioration in depressive symptoms, respectively. Depressive symptoms were associated with 3-year changes in vegetable, meat, dairy product and savoury snack intakes. Trials are necessary to examine the causal associations between food groups and depression.

Vanadium: History, chemistry, interactions with α-amino acids and potential therapeutic applications.

In the last 30 years, since the discovery that vanadium is a cofactor found in certain enzymes of tunicates and possibly in mammals, different vanadium-based drugs have been developed targeting to treat different pathologies. So far, the in vitro studies of the insulin mimetic, antitumor and antiparasitic activity of certain compounds of vanadium have resulted in a great boom of its inorganic and bioinorganic chemistry. Chemical speciation studies of vanadium with amino acids under controlled conditions or, even in blood plasma, are essential for the understanding of the biotransformation of e.g. vanadium antidiabetic complexes at the physiological level, providing clues of their mechanism of action. The present article carries out a bibliographical research emphaticizing the chemical speciation of the vanadium with different amino acids and reviewing also some other important aspects such as its chemistry and therapeutical applications of several vanadium complexes.

Joint modeling of multivariate longitudinal data and survival data in several observational studies of Huntington's disease.

BACKGROUND: Joint modeling is appropriate when one wants to predict the time to an event with covariates that are measured longitudinally and are related to the event. An underlying random effects structure links the survival and longitudinal submodels and allows for individual-specific predictions. Multiple time-varying and time-invariant covariates can be included to potentially increase prediction accuracy. The goal of this study was to estimate a multivariate joint model on several longitudinal observational studies of Huntington's disease, examine external validity performance, and compute individual-specific predictions for characterizing disease progression. Emphasis was on the survival submodel for predicting the hazard of motor diagnosis. METHODS: Data from four observational studies was analyzed: Enroll-HD, PREDICT-HD, REGISTRY, and Track-HD. A Bayesian approach to estimation was adopted, and external validation was performed using a time-varying AUC measure. Individual-specific cumulative hazard predictions were computed based on a simulation approach. The cumulative hazard was used for computing predicted age of motor onset and also for a deviance residual indicating the discrepancy between observed diagnosis status and model-based status. RESULTS: The joint model trained in a single study had very good performance in discriminating among diagnosed and pre-diagnosed participants in the remaining test studies, with the 5-year mean AUC = .83 (range .77-.90), and the 10-year mean AUC = .86 (range .82-.92). Graphical analysis of the predicted age of motor diagnosis showed an expected strong relationship with the trinucleotide expansion that causes Huntington's disease. Graphical analysis of the deviance-type residual revealed there were individuals who converted to a diagnosis despite having relatively low model-based risk, others who had not yet converted despite having relatively high risk, and the majority falling between the two extremes. CONCLUSIONS: Joint modeling is an improvement over traditional survival modeling because it considers all the longitudinal observations of covariates that are predictive of an event. Predictions from joint models can have greater accuracy because they are tailored to account for individual variability. These predictions can provide relatively accurate characterizations of individual disease progression, which might be important in the timing of interventions, determining the qualification for appropriate clinical trials, and general genotypic analysis.

CpdA is involved in amino acid metabolism in Shewanella oneidensis MR-1.

Cyclic 3',5'-adenosine monophosphate (cAMP) phosphodiesterase (CPD) is an enzyme that catalyzes the hydrolysis of cAMP, a signaling molecule affecting diverse cellular and metabolic processes in bacteria. Some CPDs are also known to function in cAMP-independent manners, while their physiological roles remain largely unknown. Here, we investigated physiological roles of CPD in Shewanella oneidensis MR-1, a model environmental bacterium, and report that CPD is involved in amino-acid metabolism. We found that a CPD-deficient mutant of MR-1 (ΔcpdA) showed decreased expression of genes for the synthesis of methionine, S-adenosylmethionine, and histidine and required these three compounds to grow in minimal media. Interestingly, deletion of adenylate cyclases in ΔcpdA did not restore the ability to grow in minimal media, indicating that the amino acid requirements were not due to the accumulation of cAMP. These results suggest that CPD is involved in the regulation of amino acid metabolism in MR-1 in a cAMP-independent manner.

Unraveling the drivers of regional variation in healthcare spending by analyzing prevalent chronic diseases.

BACKGROUND: To indicate inefficiencies in health systems, previous studies examined regional variation in healthcare spending by analyzing the entire population. As a result, population heterogeneity is taken into account to a limited extent only. Furthermore, it clouds a detailed interpretation which could be used to inform regional budget allocation decisions to improve quality of care of one chronic disease over another. Therefore, we aimed to gain insight into the drivers of regional variation in healthcare spending by studying prevalent chronic diseases. METHODS: We used 2012 secondary health survey data linked with claims data, healthcare supply data and demographics at the individual level for 18 Dutch regions. We studied patients with diabetes (n = 10,767) and depression (n = 3,735), in addition to the general population (n = 44,694). For all samples, we estimated the cross-sectional relationship between spending, supply and demand variables and region effects using linear mixed models. RESULTS: Regions with above (below) average spending for the general population mostly showed above (below) average spending for diabetes and depression as well. Less than 1% of the a-priori total variation in spending was attributed to the regions. For all samples, we found that individual-level demand variables explained 62-63% of the total variance. Self-reported health status was the most prominent predictor (28%) of healthcare spending. Supply variables also explained, although a small part, of regional variation in spending in the general population and depression. Demand variables explained nearly 100% of regional variation in spending for depression and 88% for diabetes, leaving 12% of the regional variation left unexplained indicating differences between regions due to inefficiencies. CONCLUSIONS: The extent to which regional variation in healthcare spending can be considered as inefficiency may differ between regions and disease-groups. Therefore, analyzing chronic diseases, in addition to the traditional approach where the general population is studied, provides more insight into the causes of regional variation in healthcare spending, and identifies potential areas for efficiency improvement and budget allocation decisions.

In vivo functional and molecular characterization of the Penicillin-Binding Protein 4 (DacB) of Pseudomonas aeruginosa.

BACKGROUND: Community and nosocomial infections by Pseudomonas aeruginosa still create a major therapeutic challenge. The resistance of this opportunist pathogen to ß-lactam antibiotics is determined mainly by production of the inactivating enzyme AmpC, a class C cephalosporinase with a regulation system more complex than those found in members of the Enterobacteriaceae family. This regulatory system also participates directly in peptidoglycan turnover and recycling. One of the regulatory mechanisms for AmpC expression, recently identified in clinical isolates, is the inactivation of LMM-PBP4 (Low-Molecular-Mass Penicillin-Binding Protein 4), a protein whose catalytic activity on natural substrates has remained uncharacterized until now. RESULTS: We carried out in vivo activity trials for LMM-PBP4 of Pseudomonas aeruginosa on macromolecular peptidoglycan of Escherichia coli and Pseudomonas aeruginosa. The results showed a decrease in the relative quantity of dimeric, trimeric and anhydrous units, and a smaller reduction in monomer disaccharide pentapeptide (M5) levels, validating the occurrence of D,D-carboxypeptidase and D,D-endopeptidase activities. Under conditions of induction for this protein and cefoxitin treatment, the reduction in M5 is not fully efficient, implying that LMM-PBP4 of Pseudomonas aeruginosa presents better behaviour as a D,D-endopeptidase. Kinetic evaluation of the direct D,D-peptidase activity of this protein on natural muropeptides M5 and D45 confirmed this bifunctionality and the greater affinity of LMM-PBP4 for its dimeric substrate. A three-dimensional model for the monomeric unit of LMM-PBP4 provided structural information which supports its catalytic performance. CONCLUSIONS: LMM-PBP4 of Pseudomonas aeruginosa is a bifunctional enzyme presenting both D,D-carboxypeptidase and D,D-endopeptidase activities; the D,D-endopeptidase function is predominant. Our study provides unprecedented functional and structural information which supports the proposal of this protein as a potential hydrolase-autolysin associated with peptidoglycan maturation and recycling. The fact that mutant PBP4 induces AmpC, may indicate that a putative muropeptide-subunit product of the DD-EPase activity of PBP4 could be a negative regulator of the pathway. This data contributes to understanding of the regulatory aspects of resistance to ß-lactam antibiotics in this bacterial model.

A Mixed Model to Disentangle Variance and Serial Autocorrelation in Affective Instability Using Ecological Momentary Assessment Data.

Affective instability, the tendency to experience emotions that fluctuate frequently and intensively over time, is a core feature of several mental disorders including borderline personality disorder. Currently, affect is often measured with Ecological Momentary Assessment protocols, which yield the possibility to quantify the instability of affect over time. A number of linear mixed models are proposed to examine (diagnostic) group differences in affective instability. The models contribute to the existing literature by estimating simultaneously both the variance and serial dependency component of affective instability when observations are unequally spaced in time with the serial autocorrelation (or emotional inertia) declining as a function of the time interval between observations. In addition, the models can eliminate systematic trends, take between subject differences into account and test for (diagnostic) group differences in serial autocorrelation, short-term as well as long-term affective variability. The usefulness of the models is illustrated in a study on diagnostic group differences in affective instability in the domain of eating disorders. Limitations of the model are that they pertain to group (and not individual) differences and do not focus explicitly on circadian rhythms or cycles in affect.

Arctic plant responses to changing abiotic factors in northern Alaska.

PREMISE OF THE STUDY: Understanding the relationship between plants and changing abiotic factors is necessary to document and anticipate the impacts of climate change. METHODS: We used data from long-term research sites at Barrow and Atqasuk, Alaska, to investigate trends in abiotic factors (snow melt and freeze-up dates, air and soil temperature, thaw depth, and soil moisture) and their relationships with plant traits (inflorescence height, leaf length, reproductive effort, and reproductive phenology) over time. KEY RESULTS: Several abiotic factors, including increasing air and soil temperatures, earlier snowmelt, delayed freeze-up, drier soils, and increasing thaw depths, showed nonsignificant tendencies over time that were consistent with the regional warming pattern observed in the Barrow area. Over the same period, plants showed consistent, although typically nonsignificant tendencies toward increasing inflorescence heights and reproductive efforts. Air and soil temperatures, measured as degree days, were consistently correlated with plant growth and reproductive effort. Reproductive effort was best predicted using abiotic conditions from the previous year. We also found that varying the base temperature used to calculate degree days changed the number of significant relationships between temperature and the trait: in general, reproductive phenologies in colder sites were better predicted using lower base temperatures, but the opposite held for those in warmer sites. CONCLUSIONS: Plant response to changing abiotic factors is complex and varies by species, site, and trait; however, for six plant species, we have strong evidence that climate change will cause significant shifts in their growth and reproductive effort as the region continues to warm.

Unfolding of mono-energy neutron spectra using artificial neural network based on LMBP training algorithm.

In this work, neutron spectra are unfolded using artificial neural networks (ANNs). The neutron response of the NE213 scintillator detector, characterized by the pulse height distribution, is calculated to obtain the necessary data for unfolding the energy spectrum. This is achieved using both analytical response functions and response functions generated by the MCNPX-PHOTRACK code. In this query, the Levenberg-Marquardt method (LMM), which has a high computational speed in the learning method, is used to train the network. The performance of the ANN for unfolding the neutron energy spectrum of the NE213 scintillation detector was evaluated by comparing its results to the established Gravel method. The ANN method consistently produced spectra with a single peak closely matching the incident energy, while the Gravel method showed additional peaks and distortions. Quantitative analysis revealed a lower relative energy peak difference (indicating higher accuracy) for the ANN method compared to Gravel, particularly when noise was introduced into the data. These results suggest that ANNs offer a more robust and accurate approach for neutron spectrum unfolding.

Effect of nutrition education on the nutritional status of pregnant women in Robe and Goba Towns, Southeast Ethiopia, using a cluster randomized controlled trial.

Maternal malnutrition is pervasive throughout the world, notably in sub-Saharan Africa (SSA), including Ethiopia. This study aimed to assess the effect of nutrition education on the nutritional status of pregnant women in urban settings in Southeast Ethiopia. A community-based two-arm parallel cluster randomized controlled trial was conducted among 447 randomly selected pregnant women attending antenatal care (224 intervention and 223 control). We used a multistage cluster sampling technique followed by systematic sampling to select the pregnant women. Pregnant women who participated in the intervention arm received six nutrition education sessions. Women in the control group received standard care. A nonstretchable mid-upper arm circumference (MUAC) tape was used to measure the MUAC. A linear mixed effects model (LMM) was used to evaluate the effect of the intervention on MUAC, accounting for the clustering. The net mean ± standard error of MUAC between the intervention and control groups was 0.59 ± 0.05 (P < 0.0001). The multivariable LMM indicated that having received nutrition education interventions (ß = 0.85, 95% CI 0.60, 1.12, P < 0.0001) improved the MUAC measurement of pregnant women. Thus, nutrition education during pregnancy will combat undernutrition among pregnant women.Trial Registration: Clinicaltrials.gov (PACTR202201731802989), retrospectively registered on 24/01/2022.

Double Immunohistochemical Labelling of PRAME and Melan A in Slow Mohs Biopsy Margin Assessment of Lentigo Maligna and Lentigo Maligna Melanoma.

Introduction: Lentigo maligna (LM) and lentigo maligna melanoma (LMM) predominantly affect the head and neck areas in elderly patients, presenting as challenging ill-defined pigmented lesions with indistinct borders. Surgical margin determination for complete removal remains intricate due to these characteristics. Morphological examination of surgical margins is the key form of determining successful treatment in LM/LMM and underpin the greater margin control provided through the Slow Mohs micrographic surgery (SMMS) approach. Recent assessments have explored the use of immunohistochemistry (IHC) markers, such as Preferentially Expressed Antigen in Melanoma (PRAME), to aid in LM/LMM and margin evaluation, leveraging the selectivity of PRAME labelling in malignant melanocytic neoplasms. Methods: A Novel double-labelling (DL) method incorporating both PRAME and MelanA IHC was employed to further maximise the clinical applicability of PRAME in the assessment of LM/LMM in SMMS biopsies. The evaluation involved 51 samples, comparing the results of the novel DL with respective single-labelling (SL) IHC slides. Results: The findings demonstrated a significant agreement of 96.1% between the DL method and SL slides across the tested samples. The benchmark PRAME SL exhibited a sensitivity of 91.3% in the SMMS specimens and 67.9% in histologically confirmed positive margins. Discussion: This study highlights the utility of PRAME IHC and by extension PRAME DL as an adjunctive tool in the assessment of melanocytic tumours within staged excision margins in SMMS samples.

GPT-4 Vision: Multi-Modal Evolution of ChatGPT and Potential Role in Radiology.

GPT-4 Vision (GPT-4V) represents a significant advancement in multimodal artificial intelligence, enabling text generation from images without specialized training. This marks the transformation of ChatGPT as a large language model (LLM) into GPT-4's promised large multimodal model (LMM). As these AI models continue to advance, they may enhance radiology workflow and aid with decision support. This technical note explores potential GPT-4V applications in radiology and evaluates performance for sample tasks. GPT-4V capabilities were tested using images from the web, personal and institutional teaching files, and hand-drawn sketches. Prompts evaluated scientific figure analysis, radiologic image reporting, image comparison, handwriting interpretation, sketch-to-code, and artistic expression. In this limited demonstration of GPT-4V's capabilities, it showed promise in classifying images, counting entities, comparing images, and deciphering handwriting and sketches. However, it exhibited limitations in detecting some fractures, discerning a change in size of lesions, accurately interpreting complex diagrams, and consistently characterizing radiologic findings. Artistic expression responses were coherent. WhileGPT-4V may eventually assist with tasks related to radiology, current reliability gaps highlight the need for continued training and improvement before consideration for any medical use by the general public and ultimately clinical integration. Future iterations could enable a virtual assistant to discuss findings, improve reports, extract data from images, provide decision support based on guidelines, white papers, and appropriateness criteria. Human expertise remain essential for safe practice and partnerships between physicians, researchers, and technology leaders are necessary to safeguard against risks like bias and privacy concerns.

Mixed modeling and sample size calculations for identifying housekeeping genes.

Normalization of gene expression data using internal control genes that have biologically stable expression levels is an important process for analyzing reverse transcription polymerase chain reaction data. We propose a three-way linear mixed-effects model to select optimal housekeeping genes. The mixed-effects model can accommodate multiple continuous and/or categorical variables with sample random effects, gene fixed effects, systematic effects, and gene by systematic effect interactions. We propose using the intraclass correlation coefficient among gene expression levels as the stability measure to select housekeeping genes that have low within-sample variation. Global hypothesis testing is proposed to ensure that selected housekeeping genes are free of systematic effects or gene by systematic effect interactions. A gene combination with the highest lower bound of 95% confidence interval for intraclass correlation coefficient and no significant systematic effects is selected for normalization. Sample size calculation based on the estimation accuracy of the stability measure is offered to help practitioners design experiments to identify housekeeping genes. We compare our methods with geNorm and NormFinder by using three case studies. A free software package written in SAS (Cary, NC, U.S.A.) is available at http://d.web.umkc.edu/daih under software tab.