Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays

Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians’ decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice’s quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants’ confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare

Dicer Is Required for Maintaining Adult Pancreas

Dicer1, an essential component of RNA interference and the microRNA pathway, has many important roles in the morphogenesis of developing tissues. Dicer1 null mice have been reported to die at E7.5; therefore it is impossible to study its function in adult tissues. We previously reported that Dicer1-hypomorphic mice, whose Dicer1 expression was reduced to 20% in all tissues, were unexpectedly viable. Here we analyzed these mice to ascertain whether the down-regulation of Dicer1 expression has any influence on adult tissues. Interestingly, all tissues of adult (8–10 week old) Dicer1-hypomorphic mice were histologically normal except for the pancreas, whose development was normal at the fetal and neonatal stages; however, morphologic abnormalities in Dicer1-hypomorphic mice were detected after 4 weeks of age. This suggested that Dicer1 is important for maintaining the adult pancreas

Dicer Is Required for Maintaining Adult Pancreas

Dicer1, an essential component of RNA interference and the microRNA pathway, has many important roles in the morphogenesis of developing tissues. Dicer1 null mice have been reported to die at E7.5; therefore it is impossible to study its function in adult tissues. We previously reported that Dicer1-hypomorphic mice, whose Dicer1 expression was reduced to 20% in all tissues, were unexpectedly viable. Here we analyzed these mice to ascertain whether the down-regulation of Dicer1 expression has any influence on adult tissues. Interestingly, all tissues of adult (8–10 week old) Dicer1-hypomorphic mice were histologically normal except for the pancreas, whose development was normal at the fetal and neonatal stages; however, morphologic abnormalities in Dicer1-hypomorphic mice were detected after 4 weeks of age. This suggested that Dicer1 is important for maintaining the adult pancreas

Observation of associated near-side and away-side long-range correlations in √sNN=5.02 TeV proton-lead collisions with the ATLAS detector

Two-particle correlations in relative azimuthal angle (Δϕ) and pseudorapidity (Δη) are measured in √sNN=5.02  TeV p+Pb collisions using the ATLAS detector at the LHC. The measurements are performed using approximately 1  μb-1 of data as a function of transverse momentum (pT) and the transverse energy (ΣETPb) summed over 3.1<η<4.9 in the direction of the Pb beam. The correlation function, constructed from charged particles, exhibits a long-range (2<|Δη|<5) “near-side” (Δϕ∼0) correlation that grows rapidly with increasing ΣETPb. A long-range “away-side” (Δϕ∼π) correlation, obtained by subtracting the expected contributions from recoiling dijets and other sources estimated using events with small ΣETPb, is found to match the near-side correlation in magnitude, shape (in Δη and Δϕ) and ΣETPb dependence. The resultant Δϕ correlation is approximately symmetric about π/2, and is consistent with a dominant cos⁡2Δϕ modulation for all ΣETPb ranges and particle pT

Measurement of the cross-section of high transverse momentum vector bosons reconstructed as single jets and studies of jet substructure in pp collisions at √s = 7 TeV with the ATLAS detector

This paper presents a measurement of the cross-section for high transverse momentum W and Z bosons produced in pp collisions and decaying to all-hadronic final states. The data used in the analysis were recorded by the ATLAS detector at the CERN Large Hadron Collider at a centre-of-mass energy of √s = 7 TeV;{\rm Te}{\rm V}$and correspond to an integrated luminosity of$4.6\;{\rm f}{{{\rm b}}^{-1}}$. The measurement is performed by reconstructing the boosted W or Z bosons in single jets. The reconstructed jet mass is used to identify the W and Z bosons, and a jet substructure method based on energy cluster information in the jet centre-of-mass frame is used to suppress the large multi-jet background. The cross-section for events with a hadronically decaying W or Z boson, with transverse momentum${{p}_{{\rm T}}}\gt 320\;{\rm Ge}{\rm V}$and pseudorapidity$|\eta |\lt 1.9$, is measured to be${{\sigma }_{W+Z}}=8.5\pm 1.7$ pb and is compared to next-to-leading-order calculations. The selected events are further used to study jet grooming techniques

Thermal modeling of lesion growth with radiofrequency ablation devices

BACKGROUND: Temperature is a frequently used parameter to describe the predicted size of lesions computed by computational models. In many cases, however, temperature correlates poorly with lesion size. Although many studies have been conducted to characterize the relationship between time-temperature exposure of tissue heating to cell damage, to date these relationships have not been employed in a finite element model. METHODS: We present an axisymmetric two-dimensional finite element model that calculates cell damage in tissues and compare lesion sizes using common tissue damage and iso-temperature contour definitions. The model accounts for both temperature-dependent changes in the electrical conductivity of tissue as well as tissue damage-dependent changes in local tissue perfusion. The data is validated using excised porcine liver tissues. RESULTS: The data demonstrate the size of thermal lesions is grossly overestimated when calculated using traditional temperature isocontours of 42°C and 47°C. The computational model results predicted lesion dimensions that were within 5% of the experimental measurements. CONCLUSION: When modeling radiofrequency ablation problems, temperature isotherms may not be representative of actual tissue damage patterns

Prediction of Phenotype-Associated Genes via a Cellular Network Approach: A Candida albicans Infection Case Study

Candida albicans is the most prevalent opportunistic fungal pathogen in humans causing superficial and serious systemic infections. The infection process can be divided into three stages: adhesion, invasion, and host cell damage. To enhance our understanding of these C. albicans infection stages, this study aimed to predict phenotype-associated genes involved during these three infection stages and their roles in C. albicans–host interactions. In light of the principles that proteins that lie closer to one another in a protein interaction network are more likely to have similar functions, and that genes regulated by the same transcription factors tend to have similar functions, a cellular network approach was proposed to predict the phenotype-associated genes in this study. A total of 4, 12, and 3 genes were predicted as adhesion-, invasion-, and damage-associated genes during C. albicans infection, respectively. These predicted genes highlight the facts that cell surface components are critical for cell adhesion, and that morphogenesis is crucial for cell invasion. In addition, they provide targets for further investigations into the mechanisms of the three C. albicans infection stages. These results give insights into the responses elicited in C. albicans during interaction with the host, possibly instrumental in identifying novel therapies to treat C. albicans infection

Positive Selection for New Disease Mutations in the Human Germline: Evidence from the Heritable Cancer Syndrome Multiple Endocrine Neoplasia Type 2B

Multiple endocrine neoplasia type 2B (MEN2B) is a highly aggressive thyroid cancer syndrome. Since almost all sporadic cases are caused by the same nucleotide substitution in the RET proto-oncogene, the calculated disease incidence is 100–200 times greater than would be expected based on the genome average mutation frequency. In order to determine whether this increased incidence is due to an elevated mutation rate at this position (true mutation hot spot) or a selective advantage conferred on mutated spermatogonial stem cells, we studied the spatial distribution of the mutation in 14 human testes. In donors aged 36–68, mutations were clustered with small regions of each testis having mutation frequencies several orders of magnitude greater than the rest of the testis. In donors aged 19–23 mutations were almost non-existent, demonstrating that clusters in middle-aged donors grew during adulthood. Computational analysis showed that germline selection is the only plausible explanation. Testes of men aged 75–80 were heterogeneous with some like middle-aged and others like younger testes. Incorporating data on age-dependent death of spermatogonial stem cells explains the results from all age groups. Germline selection also explains MEN2B's male mutation bias and paternal age effect. Our discovery focuses attention on MEN2B as a model for understanding the genetic and biochemical basis of germline selection. Since RET function in mouse spermatogonial stem cells has been extensively studied, we are able to suggest that the MEN2B mutation provides a selective advantage by altering the PI3K/AKT and SFK signaling pathways. Mutations that are preferred in the germline but reduce the fitness of offspring increase the population's mutational load. Our approach is useful for studying other disease mutations with similar characteristics and could uncover additional germline selection pathways or identify true mutation hot spots