Single cell dynamics of tumor specificity vs bystander activity in CD8+ T cells define the diverse immune landscapes in colorectal cancer.

CD8+ T cell activation via immune checkpoint blockade (ICB) is successful in microsatellite instable (MSI) colorectal cancer (CRC) patients. By comparison, the success of immunotherapy against microsatellite stable (MSS) CRC is limited. Little is known about the most critical features of CRC CD8+ T cells that together determine the diverse immune landscapes and contrasting ICB responses. Hence, we pursued a deep single cell mapping of CRC CD8+ T cells on transcriptomic and T cell receptor (TCR) repertoire levels in a diverse patient cohort, with additional surface proteome validation. This revealed that CRC CD8+ T cell dynamics are underscored by complex interactions between interferon-γ signaling, tumor reactivity, TCR repertoire, (predicted) TCR antigen-specificities, and environmental cues like gut microbiome or colon tissue-specific 'self-like' features. MSI CRC CD8+ T cells showed tumor-specific activation reminiscent of canonical 'T cell hot' tumors, whereas the MSS CRC CD8+ T cells exhibited tumor unspecific or bystander-like features. This was accompanied by inflammation reminiscent of 'pseudo-T cell hot' tumors. Consequently, MSI and MSS CRC CD8+ T cells showed overlapping phenotypic features that differed dramatically in their TCR antigen-specificities. Given their high discriminating potential for CD8+ T cell features/specificities, we used the single cell tumor-reactive signaling modules in CD8+ T cells to build a bulk tumor transcriptome classification for CRC patients. This "Immune Subtype Classification" (ISC) successfully distinguished various tumoral immune landscapes that showed prognostic value and predicted immunotherapy responses in CRC patients. Thus, we deliver a unique map of CRC CD8+ T cells that drives a novel tumor immune landscape classification, with relevance for immunotherapy decision-making.

Can the Content of Social Networks Explain Epidemic Outbreaks?

People share and seek information online that reflects a variety of social phenomena, including concerns about health conditions. We analyze how the contents of social networks provide real-time information to monitor and anticipate policies aimed at controlling or mitigating public health outbreaks. In November 2020, we collected tweets on the COVID-19 pandemic with content ranging from safety measures, vaccination, health, to politics. We then tested different specifications of spatial econometrics models to relate the frequency of selected keywords with administrative data on COVID-19 cases and deaths. Our results highlight how mentions of selected keywords can significantly explain future COVID-19 cases and deaths in one locality. We discuss two main mechanisms potentially explaining the links we find between Twitter contents and COVID-19 diffusion: risk perception and health behavior.

Complex FFA1 receptor (in)dependent modulation of calcium signaling by free fatty acids.

The expression of free fatty acid 1 receptors (FFA1R), activated by long chain fatty acids in human pancreatic ß-cells and enhancing glucose-stimulated insulin secretion are an attractive target to treat type 2 diabetes. Yet several clinical studies with synthetic FFA1R agonists had to be discontinued due to cytotoxicity and/or so-called "liver concerns". It is not clear whether these obstructions are FFA1R dependent. In this context we used CHO-AEQ cells expressing the bioluminescent calcium-sensitive protein aequorin to investigate calcium signaling elicited by FFA1 receptor ligands α-linolenic acid (ALA), oleic acid (OLA) and myristic acid (MYA). This study revealed complex modulation of intracellular calcium signaling by these fatty acids. First these compounds elicited a typical transient increase of intracellular calcium via binding to FFA1 receptors. Secondly slightly higher concentrations of ALA substantially reduced ATP mediated calcium responses in CHO-AEQ cells and Angiotensin II responses in CHO-AEQ cells expressing human AT1 receptors. This effect was less pronounced with MYA and OLA and was not linked to FFA1 receptor activation nor to acute cytotoxicity as a result of plasma membrane perturbation. Yet it can be hypothesized that, in line with previous studies, unsaturated long chain fatty acids such as ALA and OLA are capable of inactivating the G-proteins involved in purinergic and Angiotensin AT1 receptor calcium signaling. Alternatively the ability of fatty acids to deplete intracellular calcium stores might underly the observed cross-inhibition of these receptor responses in the same cells.

But, Do I Need a College Degree?: Understanding Perceptions of College and Career Readiness among Students Enrolled in a Career and Technical High School.

Career and technical education (CTE) and college preparation curriculum in high school are often treated as mutually exclusive options rather than integrated, symbiotic tracks. However, increasingly career fields require some postsecondary education, and access to four-year college degrees are important for long-term earnings and mobility. In this two-year case study, we examined how 16 juniors enrolled in a CTE high school described and perceived their college and career aspirations. Our findings revealed that participants saw vocational and academic goals as mutually beneficial but experienced them through distinctive pathways, a disconnect amplified by a lack of resources in our sample site. While mechanisms to promote college-going existed, they were often only available to subsets of students and of limited utility. From this research, we suggest that the education system should expand dual enrollment opportunities, provide mentorship of diverse career possibilities, and begin integration between college and career planning earlier in students' schooling. Moreover, we examine the possibilities demonstrated by this case study for K-16 pathways and how postsecondary institutions can meaningfully engage with CTE schools to support this integration.

A digital workflow for personalized design of the interface parts integrated in a powered ankle foot orthosis (PAFO).

The use of actuated exoskeletons in gait rehabilitation increased significantly in recent years. Although most of these exoskeletons are produced with a generic cuff, at the foot and ankle there are a lot of bony prominences and a limited amount of soft tissue, making it less comfortable . Furthermore, a proper alignment of the actuation systems is essential for the correct functioning of the exoskeleton. Therefore, we propose a digital workflow for the design of bespoke cuffs as interface parts of a powered ankle foot orthoses (PAFO). Moreover, this digital workflow permits the creation of axis and points of reference for the anatomical features which allows not only for the creation of custom-made cuffs but also for the integration and alignment of the PAFO mechanical components and actuation unit.

Use of dialysis, tracheostomy, and extracorporeal membrane oxygenation among 240,392 patients hospitalized with COVID-19 in the United States

ObjectiveTo estimate the proportion of patients hospitalized with COVID-19 who undergo dialysis, tracheostomy, and extracorporeal membrane oxygenation (ECMO). DesignA network cohort study. SettingSix databases from the United States containing routinely-collected patient data: HealthVerity, Premier, IQVIA Open Claims, Optum EHR, Optum SES, and VA-OMOP. PatientsPatients hospitalized with a clinical diagnosis or a positive test result for COVID-19. InterventionsDialysis, tracheostomy, and ECMO. Measurements and Main Results240,392 patients hospitalized with COVID-19 were included (22,887 from HealthVerity, 139,971 from IQVIA Open Claims, 29,061 from Optum EHR, 4,336 from OPTUM SES, 36,019 from Premier, and 8,118 from VA-OMOP). Across the six databases, 9,703 (4.04% [95% CI: 3.96% to 4.11%]) patients received dialysis, 1,681 (0.70% [0.67% to 0.73%]) had a tracheostomy, and 398 (0.17% [95% CI: 0.15% to 0.18%]) patients underwent ECMO over the 30 days following hospitalization. Use of ECMO was generally concentrated among patients who were younger, male, and with fewer comorbidities except for obesity. Tracheostomy was used for a similar proportion of patients regardless of age, sex, or comorbidity. While dialysis was used for a similar proportion among younger and older patients, it was more frequent among male patients and among those with chronic kidney disease. ConclusionUse of dialysis among those hospitalized with COVID-19 is high at around 4%. Although less than one percent of patients undergo tracheostomy and ECMO, the absolute numbers of patients who have undergone these interventions is substantial and can be expected to continue grow given the continuing spread of the COVID-19.

Can we trust the prediction model? Demonstrating the importance of external validation by investigating the COVID-19 Vulnerability (C-19) Index across an international network of observational healthcare datasets

BackgroundSARS-CoV-2 is straining healthcare systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate between patients requiring hospitalization and those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision making during the pandemic. However, the model is at high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool and has not been externally validated. MethodsWe followed the OHDSI framework for external validation to assess the reliability of the C-19 model. We evaluated the model on two different target populations: i) 41,381 patients that have SARS-CoV-2 at an outpatient or emergency room visit and ii) 9,429,285 patients that have influenza or related symptoms during an outpatient or emergency room visit, to predict their risk of hospitalization with pneumonia during the following 0 to 30 days. In total we validated the model across a network of 14 databases spanning the US, Europe, Australia and Asia. FindingsThe internal validation performance of the C-19 index was a c-statistic of 0.73 and calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data the model obtained c-statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US and South Korean datasets respectively. The calibration was poor with the model under-estimating risk. When validated on 12 datasets containing influenza patients across the OHDSI network the c-statistics ranged between 0.40-0.68. InterpretationThe results show that the discriminative performance of the C-19 model is low for influenza cohorts, and even worse amongst COVID-19 patients in the US, Spain and South Korea. These results suggest that C-19 should not be used to aid decision making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Seek COVER: Development and validation of a personalized risk calculator for COVID-19 outcomes in an international network

ObjectiveTo develop and externally validate COVID-19 Estimated Risk (COVER) scores that quantify a patients risk of hospital admission (COVER-H), requiring intensive services (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis. MethodsWe analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries. We developed and validated 3 scores using 6,869,127 patients with a general practice, emergency room, or outpatient visit with diagnosed influenza or flu-like symptoms any time prior to 2020. The scores were validated on patients with confirmed or suspected COVID-19 diagnosis across five databases from South Korea, Spain and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death iii) death in the 30 days after index date. ResultsOverall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved high performance in influenza. When transported to COVID-19 cohorts, the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration was overall acceptable. ConclusionsA 9-predictor model performs well for COVID-19 patients for predicting hospitalization, intensive services and fatality. The models could aid in providing reassurance for low risk patients and shield high risk patients from COVID-19 during de-confinement to reduce the virus impact on morbidity and mortality.

Random walk in chemical space of Cantor dust as a paradigm of superdiffusion.

We point out that the chemical space of a totally disconnected Cantor dust K(n) [Symbol: see text E(n) is a compact metric space C(n) with the spectral dimension d(s) = d(ℓ) = n > D, where D and d(ℓ) = n are the fractal and chemical dimensions of K(n), respectively. Hence, we can define a random walk in the chemical space as a Markovian Gaussian process. The mapping of a random walk in C(n) into K(n) [Symbol: see text] E(n) defines the quenched Lévy flight on the Cantor dust with a single step duration independent of the step length. The equations, describing the superdiffusion and diffusion-reaction front propagation ruled by the local quenched Lévy flight on K(n) [Symbol: see text] E(n), are derived. The use of these equations to model superdiffusive phenomena, observed in some physical systems in which propagators decay faster than algebraically, is discussed.

Sonophotocatalytic degradation of alazine and gesaprim commercial herbicides in TiO2 slurry.

The photocatalytic degradation of alazine and gesaprim commercial herbicides was carried out in aqueous TiO(2) suspensions under UV light (15W, 352 nm). Degradation of these herbicides was also observed by the combined effects of photocatalysis with sonolysis (sonophotocatalysis) using an ultrasound source of 20kHz. Degradation profiles were recorded by measuring the concentration of the active compounds present in the alazine (alachlor and atrazine) and gesaprim (atrazine) by HPLC as a function of irradiation time (sound and/or light). Over 90% of the active component in the gesaprim was abated and those in alazine were completely degraded. The content of total organic carbon and chemical oxygen demand was also monitored. Mineralization of the commercial herbicides was achieved. Over 80% of chemical oxygen demand abatement was attained for both herbicides with sonophotocatalysis at 150 min of irradiation time. The photocatalytic degradation of the herbicides followed a pseudo-first order kinetics and their rate constant was increased by the combined effects of sonolysis.

Anomalous roughness with system-size-dependent local roughness exponent.

We note that in a system far from equilibrium the interface roughening may depend on the system size which plays the role of control parameter. To detect the size effect on the interface roughness, we study the scaling properties of rough interfaces formed in paper combustion experiments. Using paper sheets of different width lambda L0, we found that the turbulent flame fronts display anomalous multiscaling characterized by nonuniversal global roughness exponent alpha and the system-size-dependent spectrum of local roughness exponents zeta(q) (lambda) = zeta(1) (1) q(-omega) lambda(phi)