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BackgroundThe high prevalence of COVID-19 has resulted in 200,000 deaths as of early 2020. The corresponding mortality rate among different countries and times varies. ObjectiveThis study aims to investigate the relationship between the mortality rate and prevalence of COVID-19 within a country. MethodsWe collected data from the Johns Hopkins Coronavirus Resource Center. These data included the daily cumulative death count, recovered count, and confirmed count for each country. This study focused on a total of 36 countries with over 10,000 confirmed COVID-19 cases. Mortality was the main outcome and dependent variable, and it was computed by dividing the number of COVID-19 deaths by the number of confirmed cases. ResultsThe results of our global panel regression analysis showed that there was a highly significant correlation between prevalence and mortality (ρ=0.8304; P

BackgroundA mobile app generates passive data, such as GPS data traces, without any direct involvement from the user. These passive data have transformed the manner of traditional assessments that require active participation from the user. Passive data collection is one of the most important core techniques for mobile health development because it may promote user retention, which is a unique characteristic of a software medical device. ObjectiveThe primary aim of this study was to quantify user retention for the “Staff Hours” app using survival analysis. The secondary aim was to compare user retention between passive data and active data, as well as factors associated with the survival rates of user retention. MethodsWe developed an app called “Staff Hours” to automatically calculate users’ work hours through GPS data (passive data). “Staff Hours” not only continuously collects these passive data but also sends an 11-item mental health survey to users monthly (active data). We applied survival analysis to compare user retention in the collection of passive and active data among 342 office workers from the “Staff Hours” database. We also compared user retention on Android and iOS platforms and examined the moderators of user retention. ResultsA total of 342 volunteers (224 men; mean age 33.8 years, SD 7.0 years) were included in this study. Passive data had higher user retention than active data (P=.011). In addition, user retention for passive data collected via Android devices was higher than that for iOS devices (P=.015). Trainee physicians had higher user retention for the collection of active data than trainees from other occupations, whereas no significant differences between these two groups were observed for the collection of passive data (P=.700). ConclusionsOur findings demonstrated that passive data collected via Android devices had the best user retention for this app that records GPS-based work hours.

BackgroundAssessing human behaviors via smartphone for monitoring the pattern of daily behaviors has become a crucial issue in this century. Thus, a more accurate and structured methodology is needed for smartphone use research. ObjectiveThe study aimed to investigate the duration of data collection needed to establish a reliable pattern of use, how long a smartphone use cycle could perpetuate by assessing maximum time intervals between 2 smartphone periods, and to validate smartphone use and use/nonuse reciprocity parameters. MethodsUsing the Know Addiction database, we selected 33 participants and passively recorded their smartphone usage patterns for at least 8 weeks. We generated 4 parameters on the basis of smartphone use episodes, including total use frequency, total use duration, proactive use frequency, and proactive use duration. A total of 3 additional parameters (root mean square of successive differences, Control Index, and Similarity Index) were calculated to reflect impaired control and compulsive use. ResultsOur findings included (1) proactive use duration correlated with subjective smartphone addiction scores, (2) a 2-week period of data collection is required to infer a 2-month period of smartphone use, and (3) smartphone use cycles with a time gap of 4 weeks between them are highly likely independent cycles. ConclusionsThis study validated temporal stability for smartphone use patterns recorded by a mobile app. The results may provide researchers an opportunity to investigate human behaviors with more structured methods.

The exact contraction of a generic two-dimensional (2D) tensor network state (TNS) is known to be exponentially hard, making simulation of 2D systems difficult. The recently introduced class of isometric TNS (isoTNS) represents a subset of TNS that allows for efficient simulation of such systems on finite square lattices. The isoTNS ansatz requires the identification of an "orthogonality column"of tensors, within which one-dimensional matrix product state (MPS) methods can be used for calculation of observables and optimization of tensors. Here we extend isoTNS to infinitely long strip geometries and introduce an infinite version of the Moses Move algorithm for moving the orthogonality column around the network. Using this algorithm, we iteratively transform an infinite MPS representation of a 2D quantum state into a strip isoTNS and investigate the entanglement properties of the resulting state. In addition, we demonstrate that the local observables can be evaluated efficiently. Finally, we introduce an infinite time-evolving block decimation algorithm (iTEBD2) and use it to approximate the ground state of the 2D transverse field Ising model on lattices of infinite strip geometry.

Purpose: Periprosthetic joint infection (PJI) is a leading cause of joint arthroplasty failure, potentially leading to critical complications like vertebral osteomyelitis (VO). The factors contributing to VO after PJI and the outcomes for these patients are not well understood. Our study aims to (1) identify risk factors for VO following PJI and (2) assess the clinical outcomes in these cases. Methods: We included PJI patients treated surgically at our centre from January 2006 to December 2020, excluding those with simultaneous VO post‐PJI. Our focus was on patients with VO occurring after PJI, monitored for at least 5 years. Analysis included patient comorbidities, PJI treatment approaches, pathogen identification and clinical outcomes. Results: Of 1701 PJI cases, 21 (1.23%) developed VO. Key risk factors for VO post‐PJI were identified: systemic inflammatory response syndrome, substance misuse, polymicrobial infection and undergoing at least three stages of resection arthroplasty (odds ratios: 1.86, 54.28, 52.33 and 31.88, respectively). Adverse outcomes were noted in VO patients, with recurrent VO in 6/21 and repeated PJIs in 18/21 cases. Conclusions: Patients with PJI, especially those with certain risk factors, have an increased likelihood of developing VO and encountering negative outcomes. The potential role of bacteremia in the development of VO after PJI needs further exploration. Level of Evidence: Level III. [ABSTRACT FROM AUTHOR]

Multiple mediation analysis is a powerful methodology to assess causal effects in the presence of multiple mediators. Several methodologies, such as G‐computation and inverse‐probability‐weighting, have been widely used to draw inferences about natural indirect effects (NIEs). However, a limitation of these methods is their potential for model misspecification. Although powerful semiparametric methods with high robustness and consistency have been developed for inferring average causal effects and for analyzing the effects of a single mediator, a comparably robust method for multiple mediation analysis is still lacking. Therefore, this theoretical study proposes a method of using multiply robust estimators of NIEs in the presence of multiple ordered mediators. We show that the proposed estimators not only enjoy the multiply robustness to model misspecification, they are also consistent and asymptotically normal under regular conditions. We also performed simulations for empirical comparisons of the finite‐sample properties between our multiply robust estimators and existing methods. In an illustrative example, a dataset for liver disease patients in Taiwan is used to examine the mediating roles of liver damage and liver cancer in the pathway from hepatitis B/C virus infection to mortality. The model is implemented in the open‐source R package "MedMR." [ABSTRACT FROM AUTHOR]

Drawing from experience with the severe acute respiratory syndrome epidemic in 2003, the Taiwan government has established a central command system for a quick response to epidemics arising from China [...]

Membrane protrusion and adhesion to the extracellular matrix, which involves the extension of actin filaments and formation of adhesion complexes, are the fundamental processes for cell migration, tumor invasion, and metastasis. How cancer cells efficiently coordinate these processes remains unclear. Here, we showed that membrane-targeted chloride intracellular channel 1 (CLIC1) spatiotemporally regulates the formation of cell-matrix adhesions and membrane protrusions through the recruitment of PIP5Ks to the plasma membrane. Comparative proteomics identified CLIC1 upregulated in human hepatocellular carcinoma (HCC) and associated with tumor invasiveness, metastasis, and poor prognosis. In response to migration-related stimuli, CLIC1 recruited PIP5K1A and PIP5K1C from the cytoplasm to the leading edge of the plasma membrane, where PIP5Ks generate a phosphatidylinositol 4,5-bisphosphate-rich ([PIP.sub.2]-rich) microdomain to induce the formation of integrin-mediated cell-matrix adhesions and the signaling for cytoskeleon extension. CLIC1 silencing inhibited the attachment of tumor cells to culture plates and the adherence and extravasation in the lung alveoli, resulting in suppressed lung metastasis in mice. This study reveals what we believe is an unrecognized mechanism that spatiotemporally coordinates the formation of both lamellipodium/invadopodia and nascent cell-matrix adhesions for directional migration and tumor invasion/metastasis. The unique traits of upregulation and membrane targeting of CLIC1 in cancer cells make it an excellent therapeutic target for tumor metastasis., Introduction Membrane protrusion and adhesion to the extracellular matrix are the 2 fundamental processes of different modes of cell migration, which are crucial for embryonic development, wound healing, immune responses, [...]