Pair Interactions of Self-Propelled SiO2-Pt Janus Colloids: Chemically Mediated Encounters.

Driven by the necessity to achieve a thorough comprehension of the bottom-up fabrication process of functional materials, this experimental study investigates the pairwise interactions or collisions between chemically active SiO2-Pt Janus colloids. These collisions are categorized based on the Janus colloids' orientations before and after they make physical contact. In addition to the hydrodynamic interactions, the Janus colloids are also known to affect each other's chemical field, resulting in chemophoretic interactions, which depend on the degree of surface anisotropy in reactivity of Janus colloid and the solute-surface interaction at play. Our study reveals that these interactions lead to a noticeable decrease in particle speed and changes in orientation that correlate with the contact duration and yield different collision types. Distinct configurations of contact during collisions were found, whose mechanisms and likelihood are found to be dependent primarily on the chemical interactions. Such estimates of collision and their characterization in dilute suspensions shall have a key impact in determining the arrangement and time scales of dynamical structures and assemblies of denser suspensions and potentially the functional materials of the future.

Physical activity and sleep changes among children during the COVID-19 pandemic.

Daily routines, including in-person school and extracurricular activities, are important for maintaining healthy physical activity and sleep habits in children. The COVID-19 pandemic significantly disrupted daily routines as in-person school and activities closed to prevent spread of SARS-CoV-2. We aimed to examine and assess differences in objectively measured physical activity levels and sleep patterns from wearable sensors in children with obesity before, during, and after a period of school and extracurricular activity closures associated with the COVID-19 pandemic. We compared average step count and sleep patterns (using the Mann-Whitney U Test) before and during the pandemic-associated school closures by using data from activity tracker wristbands (Garmin VivoFit 3). Data were collected from 94 children (aged 5-17) with obesity, who were enrolled in a randomized controlled trial testing a community-based lifestyle intervention for a duration of 12-months. During the period that in-person school and extracurricular activities were closed due to the COVID-19 pandemic, children with obesity experienced objectively-measured decreases in physical activity, and sleep duration. From March 15, 2020 to March 31, 2021, corresponding with local school closures, average daily step count decreased by 1655 steps. Sleep onset and wake time were delayed by about an hour and 45 min, respectively, while sleep duration decreased by over 12 min as compared with the pre-closure period. Step counts increased with the resumption of in-person activities. These findings provide objective evidence for parents, clinicians, and public health professionals on the importance of in-person daily activities and routines on health behaviors, particularly for children with pre-existing obesity. Trial Registration: Clinical trial registration: NCT03339440.

Assessment of ownership of smart devices and the acceptability of digital health data sharing.

Smart portable devices- smartphones and smartwatches- are rapidly being adopted by the general population, which has brought forward an opportunity to use the large volumes of physiological, behavioral, and activity data continuously being collected by these devices in naturalistic settings to perform research, monitor health, and track disease. While these data can serve to revolutionize health monitoring in research and clinical care, minimal research has been conducted to understand what motivates people to use these devices and their interest and comfort in sharing the data. In this study, we aimed to characterize the ownership and usage of smart devices among patients from an expansive academic health system in the southeastern US and understand their willingness to share data collected by the smart devices. We conducted an electronic survey of participants from an online patient advisory group around smart device ownership, usage, and data sharing. Out of the 3021 members of the online patient advisory group, 1368 (45%) responded to the survey, with 871 female (64%), 826 and 390 White (60%) and Black (29%) participants, respectively, and a slight majority (52%) age 58 and older. Most of the respondents (98%) owned a smartphone and the majority (59%) owned a wearable. In this population, people who identify as female, Hispanic, and Generation Z (age 18-25), and those completing higher education and having full-time employment, were most likely to own a wearable device compared to their demographic counterparts. 50% of smart device owners were willing to share and 32% would consider sharing their smart device data for research purposes. The type of activity data they are willing to share varies by gender, age, education, and employment. Findings from this study can be used to design both equitable and cost-effective digital health studies, leveraging personally-owned smartphones and wearables in representative populations, ultimately enabling the development of equitable digital health technologies.

Physical activity and sleep changes among children with obesity during a period of school closures related to the COVID-19 pandemic.

Daily routines, including in-person school and extracurricular activities, are important for maintaining healthy physical activity and sleep habits in children. The COVID-19 pandemic significantly disrupted daily routines as in-person school and activities closed to prevent spread of SARS-CoV-2. We aimed to examine and assess differences in objectively measured physical activity levels and sleep patterns from wearable sensors in children with obesity before, during, and after a period of school and extracurricular activity closures associated with the COVID-19 pandemic. We compared average step count and sleep patterns (using the Mann Whitney U Test) before and during the pandemic-associated school closures by using data from activity tracker wristbands (Garmin VivoFit 3). Data was collected from 94 children (aged 5-17) with obesity, who were enrolled in a randomized controlled trial testing a community-based lifestyle intervention for a duration of 12-months. During the period that in-person school and extracurricular activities were closed due to the COVID-19 pandemic, children with obesity experienced objectively-measured decreases in physical activity, and sleep duration. From March 15, 2020 to March 31, 2021, corresponding with local school closures, average daily step count decreased by 1,655 steps. Sleep onset and wake time were delayed by about an hour and 45 minutes, respectively, while sleep duration decreased by over 12 minutes as compared with the pre-closure period. Step counts increased with the resumption of in-person activities. These findings provide objective evidence for parents, clinicians, and public health professionals on the importance of in-person daily activities and routines on health behaviors, particularly for children with pre-existing obesity. We demonstrate the utility of wearable sensors in objectively measuring longitudinal physical activity and sleep behavior patterns in children with obesity and in quantifying changes in their health behaviors due to disruption of structured, daily routines following in-person school closures during the COVID-19 pandemic. Trial Registration: Clinical trial registration: NCT03339440.

TRIB1 confers therapeutic resistance in GBM cells by activating the ERK and Akt pathways.

GBM (Glioblastoma) is the most lethal CNS (Central nervous system) tumor in adults, which inevitably develops resistance to standard treatments leading to recurrence and mortality. TRIB1 is a serine/threonine pseudokinase which functions as a scaffold platform that initiates degradation of its substrates like C/EBPα through the ubiquitin proteasome system and also activates MEK and Akt signaling. We found that increased TRIB1 gene expression associated with worse overall survival of GBM patients across multiple cohorts. Importantly, overexpression of TRIB1 decreased RT/TMZ (radiation therapy/temozolomide)-induced apoptosis in patient derived GBM cell lines in vitro. TRIB1 directly bound to MEK and Akt and increased ERK and Akt phosphorylation/activation. We also found that TRIB1 protein expression was maximal during G2/M transition of cell cycle in GBM cells. Furthermore, TRIB1 bound directly to HDAC1 and p53. Importantly, mice bearing TRIB1 overexpressing tumors had worse overall survival. Collectively, these data suggest that TRIB1 induces resistance of GBM cells to RT/TMZ treatments by activating the cell proliferation and survival pathways thus providing an opportunity for developing new targeted therapeutics.

Scalable Strategies to Increase Efficiency and Augment Public Health Activities During Epidemic Peaks.

OBJECTIVE: Scalable strategies to reduce the time burden and increase contact tracing efficiency are crucial during early waves and peaks of infectious transmission. DESIGN: We enrolled a cohort of SARS-CoV-2-positive seed cases into a peer recruitment study testing social network methodology and a novel electronic platform to increase contact tracing efficiency. SETTING: Index cases were recruited from an academic medical center and requested to recruit their local social contacts for enrollment and SARS-CoV-2 testing. PARTICIPANTS: A total of 509 adult participants enrolled over 19 months (384 seed cases and 125 social peers). INTERVENTION: Participants completed a survey and were then eligible to recruit their social contacts with unique "coupons" for enrollment. Peer participants were eligible for SARS-CoV-2 and respiratory pathogen screening. MAIN OUTCOME MEASURES: The main outcome measures were the percentage of tests administered through the study that identified new SARS-CoV-2 cases, the feasibility of deploying the platform and the peer recruitment strategy, the perceived acceptability of the platform and the peer recruitment strategy, and the scalability of both during pandemic peaks. RESULTS: After development and deployment, few human resources were needed to maintain the platform and enroll participants, regardless of peaks. Platform acceptability was high. Percent positivity tracked with other testing programs in the area. CONCLUSIONS: An electronic platform may be a suitable tool to augment public health contact tracing activities by allowing participants to select an online platform for contact tracing rather than sitting for an interview.

Dynamics of Active SiO2-Pt Janus Colloids in Dilute Poly(ethylene oxide) Solutions.

Self-propelled Janus colloids (JCs) have recently gained much attention due to their ability to move autonomously and mimic biological microswimmers. This ability makes them suitable for prospective drug/cargo-delivery applications in microscopic domains. Understanding their dynamics in surroundings doped with macromolecules such as polymers is crucial, as most of the target application media are complex in nature. In this study, we investigate the self-diffusiophoretic motion of hydrogen peroxide-fuelled SiO2-Pt JCs in the presence of dilute amounts of poly(ethylene oxide) (PEO). Despite the addition of PEO chains producing a Newtonian behavior with negligible increase in viscosity, the ballistic movement and rotational fluctuations of active JCs are observed to be significantly suppressed. With an increase in the polymer concentration, this leads to a transition from smooth to jittery to cage-hopping to the arrested motion of active JCs. We further propose that the anisotropic interaction of the polymers with the JC increases the "local drag" of the medium, resulting in the unusual impediment of the active motion.

A method for intelligent allocation of diagnostic testing by leveraging data from commercial wearable devices: a case study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants and emerging diseases like monkeypox, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing toward individuals who are most likely to be infected and, thus, increasing the testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6765 participants) and the MyPHD study (8580 participants), including smartwatch data from 1265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate (RHR) features distinguished between COVID-19-positive and -negative cases earlier in the course of the infection than steps features, as early as 10 and 5 days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 4.5-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve the allocation of diagnostic testing resources and reduce the burden of test shortages.

A Method for Intelligent Allocation of Diagnostic Testing by Leveraging Data from Commercial Wearable Devices: A Case Study on COVID-19.

Mass surveillance testing can help control outbreaks of infectious diseases such as COVID-19. However, diagnostic test shortages are prevalent globally and continue to occur in the US with the onset of new COVID-19 variants, demonstrating an unprecedented need for improving our current methods for mass surveillance testing. By targeting surveillance testing towards individuals who are most likely to be infected and, thus, increasing testing positivity rate (i.e., percent positive in the surveillance group), fewer tests are needed to capture the same number of positive cases. Here, we developed an Intelligent Testing Allocation (ITA) method by leveraging data from the CovIdentify study (6,765 participants) and the MyPHD study (8,580 participants), including smartwatch data from 1,265 individuals of whom 126 tested positive for COVID-19. Our rigorous model and parameter search uncovered the optimal time periods and aggregate metrics for monitoring continuous digital biomarkers to increase the positivity rate of COVID-19 diagnostic testing. We found that resting heart rate features distinguished between COVID-19 positive and negative cases earlier in the course of the infection than steps features, as early as ten and five days prior to the diagnostic test, respectively. We also found that including steps features increased the area under the receiver operating characteristic curve (AUC-ROC) by 7-11% when compared with RHR features alone, while including RHR features improved the AUC of the ITA model's precision-recall curve (AUC-PR) by 38-50% when compared with steps features alone. The best AUC-ROC (0.73 ± 0.14 and 0.77 on the cross-validated training set and independent test set, respectively) and AUC-PR (0.55 ± 0.21 and 0.24) were achieved by using data from a single device type (Fitbit) with high-resolution (minute-level) data. Finally, we show that ITA generates up to a 6.5-fold increase in the positivity rate in the cross-validated training set and up to a 3-fold increase in the positivity rate in the independent test set, including both symptomatic and asymptomatic (up to 27%) individuals. Our findings suggest that, if deployed on a large scale and without needing self-reported symptoms, the ITA method could improve allocation of diagnostic testing resources and reduce the burden of test shortages.

Interaction of Active Janus Colloids with Tracers.

Understanding the motion of artificial active swimmers in complex surroundings, such as a dense bath of passive particulate matter, is essential for their successful utilization as cargo (drug) carriers and sensors or for medical imaging, under microscopic domains. In this study, we experimentally investigated the motion of active SiO2-Pt Janus particles (JPs) in a two-dimensional bath of smaller silica tracers dispersed with varying areal densities. Our observations indicate that when an active JP undergoes a collision with an isolated tracer, their interaction can have a significant impact on the swimmer's motion. However, the overall impact of tracers on the active JPs' motion (translation and rotation) depends on the frequency of collisions and also on the nature of the collision, which is marked by the time-duration for which the particles maintain contact during the collisions. Further, in the high-density tracer bath, our experiments reveal that the motion of the active JP results in a novel organizational behavior of the tracers on the trailing Pt (depletion of tracers) and the leading SiO2 (accumulation of tracers) side. In laboratory frame the emergence and the subsequent vanishing of the depletion zone are discussed in detail.

Detection and Monitoring of Viral Infections via Wearable Devices and Biometric Data.

Mounting clinical evidence suggests that viral infections can lead to detectable changes in an individual's normal physiologic and behavioral metrics, including heart and respiration rates, heart rate variability, temperature, activity, and sleep prior to symptom onset, potentially even in asymptomatic individuals. While the ability of wearable devices to detect viral infections in a real-world setting has yet to be proven, multiple recent studies have established that individual, continuous data from a range of biometric monitoring technologies can be easily acquired and that through the use of machine learning techniques, physiological signals and warning signs can be identified. In this review, we highlight the existing knowledge base supporting the potential for widespread implementation of biometric data to address existing gaps in the diagnosis and treatment of viral illnesses, with a particular focus on the many important lessons learned from the coronavirus disease 2019 pandemic.

Pancreatic cancer: genetics, disease progression, therapeutic resistance and treatment strategies.

Pancreatic cancer is a deadly disease and the third-highest cause of cancer-related deaths in the United States. It has a very low five-year survival rate (< 5%) in the United States as well as in the world (about 9%). The current gemcitabine-based therapy soon becomes ineffective because treatment resistance and surgical resection also provides only selective benefit. Signature mutations in pancreatic cancer confer chemoresistance by deregulating the cell cycle and promoting anti-apoptotic mechanisms. The stroma-rich tumor microenvironment impairs drug delivery and promotes tumor-specific immune escape. All these factors render the current treatment incompetent and prompt an urgent need for new, improved therapy. In this review, we have discussed the genetics of pancreatic cancer and its role in tumor evolution and treatment resistance. We have also evaluated new treatment strategies for pancreatic cancer, like targeted therapy and immunotherapy.

Tetrandrine inhibits deregulated cell cycle in pancreatic cancer cells: Differential regulation of p21Cip1/Waf1, p27Kip1 and cyclin D1.

Current therapies in Pancreatic Cancer (PaCa) are ineffective due to deregulated cell cycle driven by landscape mutations. In this study, we show for the first time that tetrandrine (TET) inhibits proliferation of the PaCa cells and inhibits PaCa tumor growth. TET inhibits cell cycle transition at G1/S boundary. TET increased levels of p21Cip1/Waf1 and p27Kip1, had no effect on the levels of CDK4/6 proteins and decreased the levels of cyclin D1 and pRb proteins. TET resulted in changes in mRNA levels of cyclin D1 and p21Cip1/Waf1 but had no effect on the mRNA of p27Kip1. We show, for the first time in any system, that TET treatment downregulated Skp2, E3 ligase specific for degradation of p27Kip1 during the cell cycle. Taken together our results show, that TET indirectly impairs the activities of CDK4/6 to halt deregulated cell cycle and inhibit PaCa tumor growth. These results suggest that TET may serve as a novel agent for treatment of PaCa, for which there is no effective cure to date.

Rapamycin inhibits mSin1 phosphorylation independently of mTORC1 and mTORC2.

Current knowledge indicates that the mammalian target of rapamycin (mTOR) functions as two complexes, mTORC1 and mTORC2, regulating cell growth, proliferation, survival, differentiation, and motility. Recently mSin1 has been identified as a critical component of mTORC2, which is essential for phosphorylation of Akt and other signaling molecules. Studies have shown that rapamycin inhibits phosphorylation of mSin1. However, the underlying mechanism is unknown. Here we found that rapamycin inhibited phosphorylation of mSin1 potently and rapidly. Expression of rapamycin-resistant mutant of mTOR (mTOR-T), but not rapamycin-resistant and kinase dead mutant of mTOR (mTOR-TE), prevented rapamycin from inhibiting mSin1 phosphorylation, suggesting that rapamycin-induced dephosphorylation of mSin1 is mTOR-dependent. Surprisingly, ectopic expression of rapamycin-resistant and constitutively active p70 S6 kinase 1 (S6K1) did not confer resistance to rapamycin-induced dephosphorylation of mSin1. Furthermore, disruption of mTORC1 and mTORC2 by silencing raptor and rictor, respectively, or downregulation of S6K1 or Akt did not induce the dephosphorylation of mSin1 as rapamycin did. However, silencing mTOR or mLST8 mimicked the effect of rapamycin, inhibiting mSin1 phosphorylation. Our findings suggest that rapamycin inhibits mSin1 phosphorylation, which is independent of mTORC1 and mTORC2, but is possibly dependent on a new mTOR complex, which at least contains mTOR and mLST8.

Maduramicin inhibits proliferation and induces apoptosis in myoblast cells.

Maduramicin, a polyether ionophore antibiotic derived from the bacterium Actinomadura yumaensis, is currently used as a feed additive against coccidiosis in poultry worldwide. It has been clinically observed that maduramicin can cause skeletal muscle and heart cell damage, resulting in skeletal muscle degeneration, heart failure, and even death in animals and humans, if improperly used. However, the mechanism of its toxic action in myoblasts is not well understood. Using mouse myoblasts (C2C12) and human rhabdomyosarcoma (RD and Rh30) cells as an experimental model for myoblasts, here we found that maduramicin inhibited cell proliferation and induced cell death in a concentration-dependent manner. Further studies revealed that maduramicin induced accumulation of the cells at G0/G1 phase of the cell cycle, and induced apoptosis in the cells. Concurrently, maduramicin downregulated protein expression of cyclin D1, cyclin-dependent kinases (CDK4 and CDK6), and CDC25A, and upregulated expression of the CDK inhibitors (p21Cip1 and p27Kip1), resulting in decreased phosphorylation of Rb. Maduramicin also induced expression of BAK, BAD, DR4, TRADD and TRAIL, leading to activation of caspases 8, 9 and 3 as well as cleavage of poly ADP ribose polymerase (PARP). Taken together, our results suggest that maduramicin executes its toxicity in myoblasts at least by inhibiting cell proliferation and inducing apoptotic cell death.

Fusarochromanone induces G1 cell cycle arrest and apoptosis in COS7 and HEK293 cells.

Fusarochromanone (FC101), a mycotoxin produced by the fungus Fusarium equiseti, is frequently observed in the contaminated grains and feedstuffs, which is toxic to animals and humans. However, the underlying molecular mechanism remains to be defined. In this study, we found that FC101 inhibited cell proliferation and induced cell death in COS7 and HEK293 cells in a concentration-dependent manner. Flow cytometric analysis showed that FC101 induced G1 cell cycle arrest and apoptosis in the cells. Concurrently, FC101 downregulated protein expression of cyclin D1, cyclin-dependent kinases (CDK4 and CDK6), and Cdc25A, and upregulated expression of the CDK inhibitors (p21Cip1 and p27Kip1), resulting in hypophosphorylation of Rb. FC101 also inhibited protein expression of Bcl-2, Bcl-xL, Mcl-1 and survivin, and induced expression of BAD, leading to activation of caspase 3 and cleavage of PARP, indicating caspase-dependent apoptosis. However, Z-VAD-FMK, a pan-caspase inhibitor, only partially prevented FC101-induced cell death, implying that FC101 may induce cell death through both caspase-dependent and -independent mechanisms. Our results support the notion that FC101 executes its toxicity at least by inhibiting cell proliferation and inducing cell death.