Despite improvements in patient outcomes, pediatric cancer remains a leading cause of non-accidental death in children. Recent genetic analysis of patients with pediatric cancers indicates an important role for both germline genetic predisposition and cancer-specific somatic driver mutations. Increasingly, evidence demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of vulnerability in pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers and how these models compare to other pediatric cancer model modalities.
Today, pediatric cancer is a leading cause of non-accidental death in children. In order to further improve outcomes, it is important for researchers and clinicians alike to recognize how pediatric cancers are distinct from adult cancers. Inherited risk of cancer may play a greater role in pediatric cancer risk, and subsequent tumor-specific acquired driver mutations initiate tumor formation. However, there is substantial interaction between inherited and acquired mutations, which supports consideration of both simultaneously. Recent advancements in biotechnology, have improved matching between early cells of development and pediatric cancer cells, although cell-of-origin for certain pediatric central nervous system tumors remain elusive. Increasingly, evidence, particularly in pediatric medulloblastoma, demonstrates that the developmental timepoint at which the cancer cell-of-origin transforms is critical to tumor identity and therapeutic response. Therefore, future therapeutic development would be bolstered by the use of disease models that faithfully recapitulate the genetic context, cell-of-origin, and developmental window of pediatric cancers. Human stem cells have the potential to incorporate all of these characteristics into a pediatric cancer model, while serving as a platform for rapid genetic and pharmacological testing. In this review, we describe how human stem cells have been used to model pediatric cancers, how human these models compare to other pediatric cancer model modalities, and how these models can be improved in the future.

Female gender, younger age and stressful life events are known predisposing factors for functional neurological disorders (FNDs). Employment in a healthcare profession has also been suggested to be a predisposing factor. We set out to conduct a large-scale case-control study to estimate the rate employment in a healthcare profession among people with FND.
We included 200 consecutive patients with a confirmed diagnosis of FND, referred to our clinic at University Hospital Bern Switzerland between October 1, 2016, and August 1, 2019. In addition, we included a control group of 200 patients with a confirmed neurological disorder, matched for age and gender, seen during the same period. The primary endpoint was to compare the prevalence of healthcare professionals between the groups. We also describe the clinical manifestations and concomitant psychiatric diagnoses in the FND cohort.
Female gender was predominant (70%), and the participants\' mean age was 37 years. The proportion of healthcare professionals in the FND patients was 18% (33/186), which was significantly higher than in the control group, in which it was 10.6% (17/189; p = 0.019, 95% confidence interval odds ratio 1.168-4.074). Most healthcare professionals in both cohorts were nurses (21/33 among FND patients, 10/17 among controls). Among FND patients, 140 (70%) had motor symptoms and 65 (32.5%) had a concomitant psychiatric diagnosis.
This case-control study confirmed a higher rate of employment in healthcare professions in patients with FND, suggesting two potential mechanisms of FND: exposure to models/specific knowledge about neurological symptoms or stress-related professional factors. This warrants further studies on underlying mechanisms and prevention.

Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have made it possible to simulate the efficacy of cancer drug protocols in diverse patient populations, which could inform and improve clinical trial design. Here, we review the current and potential use of in silico trials and causal AI to increase the efficacy and safety of traditional clinical trials. We conclude that in silico trials using causal AI approaches can simulate control and efficacy arms, inform patient recruitment and regimen titrations, and better enable subgroup analyses critical for precision medicine.

The outbreak of the coronavirus disease-19 (COVID-19) pandemic has created much speculation on the behavior of the disease. Some of the questions that have been asked can be addressed by computational modeling based on the use of high-performance computing (HPC) and machine learning techniques.  The Reference Model previously used such techniques to model diabetes. The Reference Model is now used to answer a few questions on COVID-19, while changing the traditional susceptible-infected-recovered (SIR) model approach. This adaptation allows us to answer questions such as the probability of transmission per encounter, disease duration, and mortality rate. The Reference Model uses data on US infection and mortality from 52 states and territories combining multiple assumptions of human interactions to compute the best fitting parameters that explain the disease behavior for given assumptions and accumulated data from April 2020 to June 2020. This is a preliminary report aimed at demonstrating the possible use of computational models based on computing power to aid comprehension of disease characteristics. This infrastructure can accumulate models and assumptions from multiple contributors.

Human pluripotent stem cells (PSCs) generated from affected blastocysts or from patient-derived somatic cells are an emerging platform for disease modeling and drug discovery. Fragile X syndrome (FXS), the leading cause of inherited intellectual disability, was one of the first disorders modeled in both embryonic stem cells and induced PCSs and can serve as an exemplary case for the utilization of human PSCs in the study of human diseases. Over the past decade, FXS-PSCs have been used to address the fundamental questions regarding the pathophysiology of FXS. In this review we summarize the methodologies for generation of FXS-PSCs, discuss their advantages and disadvantages compared with existing modeling systems and describe their utilization in the study of FXS pathogenesis and in the development of targeted treatment.

The various thresholding quantities grouped under the \"Basic Reproductive Number\" umbrella are often confused, but represent distinct approaches to estimating epidemic spread potential, and address different modeling needs. Here, we contrast several common reproduction measures applied to stochastic compartmental models, and introduce a new quantity dubbed the \"empirically adjusted reproductive number\" with several advantages. These include: more complete use of the underlying compartmental dynamics than common alternatives, use as a potential diagnostic tool to detect the presence and causes of intensity process underfitting, and the ability to provide timely feedback on disease spread. Conceptual connections between traditional reproduction measures and our approach are explored, and the behavior of our method is examined under simulation. Two illustrative examples are developed: First, the single location applications of our method are established using data from the 1995 Ebola outbreak in the Democratic Republic of the Congo and a traditional stochastic SEIR model. Second, a spatial formulation of this technique is explored in the context of the ongoing Ebola outbreak in West Africa with particular emphasis on potential use in model selection, diagnosis, and the resulting applications to estimation and prediction. Both analyses are placed in the context of a newly developed spatial analogue of the traditional SEIR modeling approach.