What factors influence cellular pathologists' confidence in case reporting?

Histopathology is a challenging interpretive discipline, and the level of confidence a pathologist has in their diagnosis is known to vary, which is conveyed descriptively in pathology reports. There has been little study to accurately quantify pathologists' diagnostic confidence or the factors that influence it. In this study involving sixteen pathologists from six NHS trusts, we assessed diagnostic confidence across multiple variables and four specialties. Each case was reported by four pathologists, with each pathologist reporting each case twice (on light microscopy (LM) and digital pathology (DP)). For each diagnosis, pathologists recorded their confidence on a 7-point Likert scale. This provided 16,187 diagnoses and associated confidence scores for analysis. All variables investigated were found to be significantly predictive of diagnostic confidence, except level of pathologist experience. Confidence was lower for difficult to report cases, cases where there was inter- and intra-pathologist variation in the diagnosis, and cases where the pathologist made an incorrect diagnosis. Confidence was higher, although nominally, for LM diagnoses than DP (rate ratio 1.09 (95% CI 1.01-1.18), p = 0.035), although results indicate pathologists are confident to report on DP. Lowest confidence scores were seen in areas of known diagnostic complexity and cases with quality issues. High confidence in incorrect diagnoses were almost invariably attributed to interpretive diagnostic differences which occurred across both rare and common lesions. The results highlight the value of external quality control schemes and the benefits of selective peer review when reporting.

Bacterial aggregation facilitates internalin-mediated invasion of Listeria monocytogenes.

Dissemination of food-borne L. monocytogenes in the host relies on internalin-mediated invasion, but the underlying invasion strategies remain elusive. Here we use live-cell microscopy to follow single cell interactions between individual human cells and L. monocytogenes and elucidate mechanisms associated with internalin B (InlB)-mediated invasion. We demonstrate that whilst a replicative invasion of nonphagocytic cells is a rare event even at high multiplicities of invasion, L. monocytogenes overcomes this by utilising a strategy relaying on PrfA-mediated ActA-based aggregation. We show that L. monocytogenes forms aggregates in extracellular host cell environment, which promote approximately 5-fold more host cell adhesions than the non-aggregating actA-ΔC mutant (which lacks the C-terminus coding region), with the adhering bacteria inducing 3-fold more intracellular invasions. Aggregation is associated with robust MET tyrosine kinase receptor clustering in the host cells, a hallmark of InlB-mediated invasion, something not observed with the actA-ΔC mutant. Finally, we show via RNA-seq analyses that aggregation involves a global adaptive response to host cell environment (including iron depletion), resulting in metabolic changes in L. monocytogenes and upregulation of the PrfA virulence regulon. Overall, our analyses provide new mechanistic insights into internalin-mediated host-pathogen interactions of L. monocytogenes.

An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs.

Explaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility. We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug's therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast. Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson's disease.

How bad becomes good: A neurocomputational model of affect-informed choice.

People often draw on their current affective experience to inform their decisions, yet little is known about the underlying mechanisms of this process. Understanding them has important implications for many big questions in both the affective and decision sciences. Do the same neural circuits that generate affect generate value? What differentiates people who have greater contextual flexibility in their reliance on affect? Do affective choices invoke processes that are distinct from less affective choices? To investigate these questions, we developed a neurocomputational model of affect-informed choice, in which people convert subjective affect into context-sensitive decision value through a process of weighted evidence accumulation. We then tested model predictions by recording electroencephalography and facial electromyography during a novel affective choice paradigm in a sample of racially diverse undergraduate participants (data collected in 2018-2019). In addition to validating our model, we found that generation of affective responses occurs earlier than, and is neurally distinct from, valuation of that affect. Moreover, individual differences in contextual flexibility of affective weighting correlated only with later valuation processes, not earlier affect generation processes. Our results have important theoretical implications for emotion, emotion regulation, and decision making. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Electrical signaling in three-dimensional bacterial biofilms using an agent-based fire-diffuse-fire model.

Agent-based models were used to describe electrical signaling in bacterial biofilms in three dimensions. Specifically, wavefronts of potassium ions in Escherichia coli biofilms subjected to stress from blue light were modeled from experimental data. Electrical signaling occurs only when the biofilms grow beyond a threshold size, which we have shown to vary with the K^{+} ion diffusivity, and the K^{+} ion threshold concentration, which triggered firing in the fire-diffuse-fire model. The transport of the propagating wavefronts shows superdiffusive scaling on time. K^{+} ion diffusivity is the main factor that affects the wavefront velocity. The K^{+} ion diffusivity and the firing threshold also affect the anomalous exponent for the propagation of the wavefront determining whether the wavefront is subdiffusive or superdiffusive. The geometry of the biofilm and its relation to the mean-square displacement (MSD) of the wavefront as a function of time was investigated for spherical, cylindrical, cubical, and mushroom-like structures. The MSD varied significantly with geometry; an additional regime to the kinetics occurred when the potassium wavefront leaves the biofilm. Adding cylindrical defects to the biofilm, which are known to occur in E. coli biofilms, also gave an extra kinetic regime to the wavefront MSD for the propagation through the defect.

When Are Pregnant Patients Receiving Tranexamic Acid during Delivery Hospitalization in the United States?

OBJECTIVE: The World Health Organization recommends tranexamic acid (TXA) in the management of postpartum hemorrhage (PPH). However, the role of TXA in PPH prevention and the optimal timing of TXA administration remain unknown. Our objective was to describe the timing of TXA administration, differences in timing of TXA administration by mode of delivery, and current trends in TXA administration in the United States. STUDY DESIGN: We conducted a descriptive study of trends in TXA administration using the Cerner Real-World Database. We identified 1,544,712 deliveries occurring at greater than 24 weeks' gestation from January 1, 2016, to February 21, 2023. Demographic data were collected including gestational age, mode of delivery, and comorbidities. The timing of TXA administration and differences in TXA timing by mode of delivery were also collected. RESULTS: In our cohort, 21,433 patients (1.39%) received TXA. The majority of patients who received TXA were between ages 25 and 34 years old (55.3%), White (60.7%), and delivered between 37 and 416/7 weeks (81.4%). The TXA group had a higher prevalence of medical comorbidities including obesity (32.9 vs. 19.0%, p < 0.00001), preeclampsia (19.6 vs. 6.81%, p < 0.00001), and pregestational diabetes (3.27 vs. 1.36%, p < 0.00001). Among women who received TXA, 15.4% received it within 3 hours before delivery. Among patients who received TXA after delivery, 23.6% received TXA within 3 hours after delivery, whereas 35.7% received TXA between 10 and 24 hours after delivery. A total of 80.4% of patients who received TXA before delivery had a cesarean delivery. CONCLUSION: While TXA is most commonly administered after delivery, many patients are receiving TXA prior to delivery in the United States without clear evidence to guide the timing of administration. A randomized trial is urgently needed to determine the safety and efficacy of TXA when administered prior to delivery. KEY POINTS: · TXA is used in the treatment of PPH.. · The role of TXA in prevention of PPH is unclear.. · Fewer than 2% of patients in the United States receive TXA at delivery.. · TXA administration before delivery in the United States is rising..

Population pharmacokinetic modelling and simulation of tranexamic acid in adult trauma patients.

AIMS: The aim of this study is to describe the disposition of tranexamic acid (TXA) in adult trauma patients and derive a dosing regimen that optimizes exposure based on a predefined exposure target. METHODS: We performed a population pharmacokinetic (popPK) analysis of participants enrolled in the Tranexamic Acid Mechanisms and Pharmacokinetics in Traumatic Injury (TAMPITI) trial (≥18 years with traumatic injury, given ≥1 blood product and/or requiring immediate transfer to the operating room) who were randomized to a single dose of either 2 or 4 g of TXA ≤2 h from time of injury. PopPK analysis was conducted using nonlinear mixed-effects modelling (NONMEM). Simulations were then performed using the final model to generate estimated plasma TXA concentrations in 1000 simulated participants. Dosing schemes were evaluated to determine maintenance of TXA plasma concentrations >10 mg/L for ≥8 h after administration of the initial dose. RESULTS: TXA PK was best described by a two-compartment model with proportional residual error and allometric scaling on all parameters. Platelet count, skeletal muscle oxygen saturation measured by near-infrared spectroscopy and interleukin-8 concentration were significant covariates on TXA clearance. Based on simulations, a 2 g IV bolus dose, repeated 3 h later, best achieved the target exposure. CONCLUSIONS: According to simulations from a popPK model of TXA, a 2 g IV bolus with a repeated dose 3 h later would be most likely to maintain concentrations >10 mg/L for 8 h in >95% of adult trauma patients and should be considered for patients with ongoing haemorrhage.

Severe acute myositis and myocarditis on initiation of 6-weekly pembrolizumab post-COVID-19 mRNA vaccination.

We describe three cases of critical acute myositis with myocarditis occurring within 22 days of each other at a single institution, all within 1 month of receiving the initial cycle of the anti-PD-1 drug pembrolizumab. Analysis of T cell receptor repertoires from peripheral blood and tissues revealed a high degree of clonal expansion and public clones between cases, with several T cell clones expanded within the skeletal muscle putatively recognizing viral epitopes. All patients had recently received a COVID-19 mRNA booster vaccine prior to treatment and were positive for SARS-CoV2 Spike antibody. In conclusion, we report a series of unusually severe myositis and myocarditis following PD-1 blockade and the COVID-19 mRNA vaccination.

Do machine learning methods lead to similar individualized treatment rules? A comparison study on real data.

Identifying patients who benefit from a treatment is a key aspect of personalized medicine, which allows the development of individualized treatment rules (ITRs). Many machine learning methods have been proposed to create such rules. However, to what extent the methods lead to similar ITRs, that is, recommending the same treatment for the same individuals is unclear. In this work, we compared 22 of the most common approaches in two randomized control trials. Two classes of methods can be distinguished. The first class of methods relies on predicting individualized treatment effects from which an ITR is derived by recommending the treatment evaluated to the individuals with a predicted benefit. In the second class, methods directly estimate the ITR without estimating individualized treatment effects. For each trial, the performance of ITRs was assessed by various metrics, and the pairwise agreement between all ITRs was also calculated. Results showed that the ITRs obtained via the different methods generally had considerable disagreements regarding the patients to be treated. A better concordance was found among akin methods. Overall, when evaluating the performance of ITRs in a validation sample, all methods produced ITRs with limited performance, suggesting a high potential for optimism. For non-parametric methods, this optimism was likely due to overfitting. The different methods do not lead to similar ITRs and are therefore not interchangeable. The choice of the method strongly influences for which patients a certain treatment is recommended, drawing some concerns about their practical use.

Evidence from the large VALIGA cohort validates the subclassification of focal segmental glomerulosclerosis in IgA nephropathy.

Evidence from the Oxford IgA nephropathy (IgAN) cohort supports the clinical value of subclassifying focal segmental glomerulosclerosis lesions (S1). Using the larger Validation in IgA (VALIGA) study cohort, we investigated the association between podocytopathic changes and higher proteinuria, kidney outcome and response to immunosuppressive therapy. All biopsies were evaluated for glomeruli with segmental capillary occlusion by matrix ("not otherwise specified", NOS lesion), simple capsular adhesion without capillary occlusion (Adh), tip lesions, and podocyte hypertrophy (PH). S1 required a NOS lesion and/or Adh. A Chi-Squared Automatic Interaction Detection method was used to identify subgroups of FSGS lesions associated with distinctive proteinuria at biopsy. We assessed survival from a combined event (kidney failure or 50% decline in estimated glomerular filtration rate). Finally, we evaluated within each subgroup if immunosuppression was associated with a favorable outcome using propensity analysis. In 1147 patients, S1 was found in 70% of biopsies. Subclassification found NOS lesions in 44%, Adh in 59%, PH in 13%, and tip lesions in 3%, with much overlap. Four subgroups were identified with progressively higher proteinuria: from lowest, S1 without NOS, S1 with NOS but without Adh/PH, to highest, S1 with NOS and Adh but without PH, and S1 with NOS and PH. These four subgroups showed progressively worse kidney survival. Immunosuppression was associated with a better outcome only in the two highest proteinuria subgroups. Propensity analysis in these two groups, adjusted for clinical and pathological findings, found a significantly reduced time-dependent hazard of combined outcome with corticosteroids. Podocyte hypertrophy and glomeruli with simple adhesions appeared to reflect active lesions associated with a response to corticosteroids, while other S1 lesions defined chronicity. Thus, our findings support subclassifying S1 lesions in IgAN.

Electrical Impedance Spectroscopy with Bacterial Biofilms: Neuronal-like Behavior.

Negative capacitance at low frequencies for spiking neurons was first demonstrated in 1941 (K. S. Cole) by using extracellular electrodes. The phenomenon subsequently was explained by using the Hodgkin-Huxley model and is due to the activity of voltage-gated potassium ion channels. We show that Escherichia coli (E. coli) biofilms exhibit significant stable negative capacitances at low frequencies when they experience a small DC bias voltage in electrical impedance spectroscopy experiments. Using a frequency domain Hodgkin-Huxley model, we characterize the conditions for the emergence of this feature and demonstrate that the negative capacitance exists only in biofilms containing living cells. Furthermore, we establish the importance of the voltage-gated potassium ion channel, Kch, using knock-down mutants. The experiments provide further evidence for voltage-gated ion channels in E. coli and a new, low-cost method to probe biofilm electrophysiology, e.g., to understand the efficacy of antibiotics. We expect that the majority of bacterial biofilms will demonstrate negative capacitances.

A novel combination treatment for fragile X syndrome predicted using computational methods.

Fragile X syndrome is a neurodevelopmental disorder caused by silencing of the fragile X messenger ribonucleotide gene. Patients display a wide spectrum of symptoms ranging from intellectual and learning disabilities to behavioural challenges including autism spectrum disorder. In addition to this, patients also display a diversity of symptoms due to mosaicism. These factors make fragile X syndrome a difficult syndrome to manage and suggest that a single targeted therapeutic approach cannot address all the symptoms. To this end, we utilized Healx's data-driven drug discovery platform to identify a treatment strategy to address the wide range of diverse symptoms among patients. Computational methods identified the combination of ibudilast and gaboxadol as a treatment for several pathophysiological targets that could potentially reverse multiple symptoms associated with fragile X syndrome. Ibudilast is an approved broad-spectrum phosphodiesterase inhibitor, selective against both phosphodiesterase 4 and phosphodiesterase 10, and has demonstrated to have several beneficial effects in the brain. Gaboxadol is a GABAA receptor agonist, selective against the delta subunit, which has previously displayed encouraging results in a fragile X syndrome clinical trial. Alterations in GABA and cyclic adenosine monophosphate metabolism have long since been associated with the pathophysiology of fragile X syndrome; however, targeting both pathways simultaneously has never been investigated. Both drugs have a good safety and tolerability profile in the clinic making them attractive candidates for repurposing. We set out to explore whether the combination of ibudilast and gaboxadol could demonstrate therapeutic efficacy in a fragile X syndrome mouse model. We found that daily treatment with ibudilast significantly enhanced the ability of fragile X syndrome mice to perform a number of different cognitive assays while gaboxadol treatment improved behaviours such as hyperactivity, aggression, stereotypy and anxiety. Importantly, when ibudilast and gaboxadol were co-administered, the cognitive deficits as well as the aforementioned behaviours were rescued. Moreover, this combination treatment showed no evidence of tolerance, and no adverse effects were reported following chronic dosing. This work demonstrates for the first time that by targeting multiple pathways, with a combination treatment, we were able to rescue more phenotypes in a fragile X syndrome mouse model than either ibudilast or gaboxadol could achieve as monotherapies. This combination treatment approach holds promise for addressing the wide spectrum of diverse symptoms in this heterogeneous patient population and may have therapeutic potential for idiopathic autism.

Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study.

AIMS: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples. METHODS: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies. RESULTS: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either. CONCLUSIONS: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

Detection of PatIent-Level distances from single cell genomics and pathomics data with Optimal Transport (PILOT).

Although clinical applications represent the next challenge in single-cell genomics and digital pathology, we still lack computational methods to analyze single-cell or pathomics data to find sample-level trajectories or clusters associated with diseases. This remains challenging as single-cell/pathomics data are multi-scale, i.e., a sample is represented by clusters of cells/structures, and samples cannot be easily compared with each other. Here we propose PatIent Level analysis with Optimal Transport (PILOT). PILOT uses optimal transport to compute the Wasserstein distance between two individual single-cell samples. This allows us to perform unsupervised analysis at the sample level and uncover trajectories or cellular clusters associated with disease progression. We evaluate PILOT and competing approaches in single-cell genomics or pathomics studies involving various human diseases with up to 600 samples/patients and millions of cells or tissue structures. Our results demonstrate that PILOT detects disease-associated samples from large and complex single-cell or pathomics data. Moreover, PILOT provides a statistical approach to find changes in cell populations, gene expression, and tissue structures related to the trajectories or clusters supporting interpretation of predictions.

Histologic and Clinical Factors Associated with Kidney Outcomes in IgA Vasculitis Nephritis.

BACKGROUND: Nephritis is a common manifestation of IgA vasculitis and is morphologically indistinguishable from IgA nephropathy. While MEST-C scores are predictive of kidney outcomes in IgA nephropathy, their value in IgA vasculitis nephritis has not been investigated in large multiethnic cohorts. METHODS: Biopsies from 262 children and 99 adults with IgA vasculitis nephritis ( N =361) from 23 centers in North America, Europe, and Asia were independently scored by three pathologists. MEST-C scores were assessed for correlation with eGFR/proteinuria at biopsy. Because most patients ( N =309, 86%) received immunosuppression, risk factors for outcomes were evaluated in this group using latent class mixed models to identify classes of eGFR trajectories over a median follow-up of 2.7 years (interquartile range, 1.2-5.1). Clinical and histologic parameters associated with each class were determined using logistic regression. RESULTS: M, E, T, and C scores were correlated with either eGFR or proteinuria at biopsy. Two classes were identified by latent class mixed model, one with initial improvement in eGFR followed by a late decline (class 1, N =91) and another with stable eGFR (class 2, N =218). Class 1 was associated with a higher risk of an established kidney outcome (time to ≥30% decline in eGFR or kidney failure; hazard ratio, 5.84; 95% confidence interval, 2.37 to 14.4). Among MEST-C scores, only E1 was associated with class 1 by multivariable analysis. Other factors associated with class 1 were age 18 years and younger, male sex, lower eGFR at biopsy, and extrarenal noncutaneous disease. Fibrous crescents without active changes were associated with class 2. CONCLUSIONS: Kidney outcome in patients with biopsied IgA vasculitis nephritis treated with immunosuppression was determined by clinical risk factors and endocapillary hypercellularity (E1) and fibrous crescents, which are features that are not part of the International Study of Diseases of Children classification.

A neurocomputational account of the link between social perception and social action.

People selectively help others based on perceptions of their merit or need. Here, we develop a neurocomputational account of how these social perceptions translate into social choice. Using a novel fMRI social perception task, we show that both merit and need perceptions recruited the brain's social inference network. A behavioral computational model identified two non-exclusive mechanisms underlying variance in social perceptions: a consistent tendency to perceive others as meritorious/needy (bias) and a propensity to sample and integrate normative evidence distinguishing high from low merit/need in other people (sensitivity). Variance in people's merit (but not need) bias and sensitivity independently predicted distinct aspects of altruism in a social choice task completed months later. An individual's merit bias predicted context-independent variance in people's overall other-regard during altruistic choice, biasing people towards prosocial actions. An individual's merit sensitivity predicted context-sensitive discrimination in generosity towards high and low merit recipients by influencing other-regard and self-regard during altruistic decision-making. This context-sensitive perception-action link was associated with activation in the right temporoparietal junction. Together, these findings point towards stable, biologically based individual differences in perceptual processes related to abstract social concepts like merit, and suggest that these differences may have important behavioral implications for an individual's tendency toward favoritism or discrimination in social settings.