Search

Abstract Introduction: Societal guidelines offer a weak recommendation to perform cystoscopy for female patients with recurrent urinary tract infections (rUTI) of advanced age and/or with high-risk features. These guidelines lack the support of robust data and are instead based on expert opinion. In this retrospective cohort study, we aim to determine the utility of cystoscopy in patients with and without high-risk features for rUTI. Materials and methods: We identified 476 women who underwent cystoscopy for the evaluation of rUTI at a single tertiary academic medical center from May 1, 2015 and March 15, 2021. Patients were excluded if they had a competing indication for cystoscopy. Risk factors, demographic information, cystoscopic findings, and patient outcomes were analyzed. Results: 192 (41.1%) were classified as having complicated UTI. We identified six patients (1.3%) with findings that prompted management to significantly impact patient outcomes. All six patients had high-risk features. 14 patients (3.0%) were found to have mucosal abnormalities prompting biopsy, three of which required general anesthesia. All 14 biopsies were ultimately benign. Conclusions: Our findings demonstrate a low diagnostic yield and increased risk exposure for women undergoing cystoscopy for the evaluation of complicated rUTI. Additionally, our observations support prior studies indicating that cystoscopy has limited utility in the evaluation of rUTI without high-risk features.

Objective: Preoperative surgical planning for localized, small renal masses relies on two-dimensional axial imaging when determining suitability for partial vs radical nephrectomy. Iris, a recently introduced segmentation software, generates interactive three-dimensional virtual models to aid preoperative surgical planning and intraoperative navigation. We present a video of three surgical cases demonstrating different features and applications of the Iris anatomical model and report our initial perioperative results of partial nephrectomies performed using this software and three-dimensional model. Patients and surgical procedure: We retrospectively analyzed all robotic-assisted partial nephrectomy cases at our institution in which Iris was utilized between February 1, 2020, and August 1, 2021. All cases were performed by a single experienced robotic surgeon (M.S.). Baseline patient demographics, perioperative data, and complications were collected and reviewed. Results: In total, 34 consecutive robotic partial nephrectomy cases using the Iris system were included in this series. The mean nephrometry score was 8.2. Total operative time averaged 136.1 min. The mean warm ischemia time was 18.3 min. Arterial clamping was performed in 33 cases (97.1%); selective clamping was used in 12 (35.2%) cases. One intraoperative and one major postoperative complication were observed. Conclusion: Our early experience with the Iris software demonstrates that the three-dimensional model may be a useful adjunct in preoperative surgical planning and intraoperative decision making in patients undergoing robotic partial nephrectomy with moderate and complex nephrometry scores.

BackgroundInformation on U.S. COVID-19 mortality rates by occupation is limited. We aimed to characterize 2020 COVID-19 fatalities among working Californians to inform preventive strategies.MethodsWe identified laboratory-confirmed COVID-19 fatalities with dates of death in 2020 by matching death certificates to the state's COVID-19 case registry. Working status for decedents aged 18-64 years was determined from state employment records, death certificates, and case registry data and classified as "confirmed working," "likely working," or "not working." We calculated age-adjusted overall and occupation-specific COVID-19 mortality rates using 2019 American Community Survey denominators.ResultsCOVID-19 accounted for 8,050 (9.9%) of 81,468 fatalities among Californians 18-64 years old. Of these decedents, 2,486 (30.9%) were matched to state employment records and classified as "confirmed working." The remainder were classified as "likely working" (n = 4,121 [51.2%]) or "not working" (n = 1,443 [17.9%]) using death certificate and case registry data. Confirmed and likely working COVID-19 decedents were predominantly male (76.3%), Latino (68.7%), and foreign-born (59.6%), with high school or less education (67.9%); 7.8% were Black. The overall age-adjusted COVID-19 mortality rate was 30.0 per 100,000 workers (95% confidence interval [CI], 29.3-30.8). Workers in nine occupational groups had age-adjusted mortality rates higher than this overall rate, including those in farming (78.0; 95% CI, 68.7-88.2); material moving (77.8; 95% CI, 70.2-85.9); construction (62.4; 95% CI, 57.7-67.4); production (60.2; 95% CI, 55.7-65.0); and transportation (57.2; 95% CI, 52.2-62.5) occupations. While occupational differences in mortality were evident across demographic groups, mortality rates were three-fold higher for male compared with female workers and three- to seven-fold higher for Latino and Black workers compared with Asian and White workers.ConclusionCalifornians in manual labor and in-person service occupations experienced disproportionate COVID-19 mortality, with the highest rates observed among male, Latino, and Black workers; these occupational group should be prioritized for prevention.

Introduction BCG vaccination modulates immune responses to unrelated pathogens. This off-target effect could reduce the impact of emerging pathogens. As a readily available, inexpensive intervention that has a well-established safety profile, BCG is a good candidate for protecting healthcare workers (HCWs) and other vulnerable groups against COVID-19.Methods and analysis This international multicentre phase III randomised controlled trial aims to determine if BCG vaccination reduces the incidence of symptomatic and severe COVID-19 at 6 months (co-primary outcomes) compared with no BCG vaccination. We plan to randomise 10 078 HCWs from Australia, The Netherlands, Spain, the UK and Brazil in a 1:1 ratio to BCG vaccination or no BCG (control group). The participants will be followed for 1 year with questionnaires and collection of blood samples. For any episode of illness, clinical details will be collected daily, and the participant will be tested for SARS-CoV-2 infection. The secondary objectives are to determine if BCG vaccination reduces the rate, incidence, and severity of any febrile or respiratory illness (including SARS-CoV-2), as well as work absenteeism. The safety of BCG vaccination in HCWs will also be evaluated. Immunological analyses will assess changes in the immune system following vaccination, and identify factors associated with susceptibility to or protection against SARS-CoV-2 and other infections.Ethics and dissemination Ethical and governance approval will be obtained from participating sites. Results will be published in peer-reviewed open-access journals. The final cleaned and locked database will be deposited in a data sharing repository archiving system.Trial registration ClinicalTrials.gov NCT04327206

Key aims of the WHO Strategy to halve snakebite morbidity and mortality include health system strengthening and training of health workers. This requires knowledge of local health system needs and capacity, health worker training needs, and factors influencing health worker decision-making in snakebite management. This study explored health worker experiences and perceptions of snakebite management, both individually and in the context of their local health system.We used a qualitative study design with semi-structured interviews (n = 14) and focus group discussions (n = 4). We employed a combination of sampling strategies aiming to achieve maximum variation among key informants within resource limitations. We recruited health workers (n = 33) of varying roles from purposively selected tier 2, 3 and 4 health facilities (n = 12) and the community (tier 1) in four sub-counties in Kitui County, Kenya. We conducted inductive thematic analysis of all transcripts.The results identified that health workers recognised snake envenoming as a time-critical emergency in which delay in care seeking, sometimes exacerbated by health system referral delays, was a major barrier to effective management of patients. Clinicians strongly voiced a need for training in snakebite management, diagnosis and antivenom administration. Unexpressed needs for training were demonstrated in traditional remedy ineffectiveness, syndromic management, and critical appraisal of treatment effectiveness. Under-resourcing in antivenom, other medication, equipment, infrastructure and staffing also challenged management. Fear of snakebite and fear of antivenom, both linked to past experiences, influenced clinical decision-making.Our findings clearly indicate a need in Kitui County for training programmes that equip health workers for clinical decision-making in snakebite management. We further identify community intervention needs to facilitate prompt presentation to healthcare, including practical affordable transport solutions, and systematic health system resourcing needs. In addition, we recommend supportive supervision and further research in response to the emotional stress resulting from managing difficult cases in under-resourced settings.

Prior to the physical build of the industrial automation system, some challenges arise, such as processes’ cycle times calculations, ergonomics and safety evaluation, and the integration of separate machines to the complete production shops. This, in turn, requires reconfiguring the processes and component parameters. As a result, the lifecycle of the system development is prolonged, and the potential for erroneous performance increases. In modern digital manufacturing environments, virtual engineering (VE) and virtual commissioning (VC) serve as effective tools to tackle the aforementioned problems and their consequences. The virtual models developed for VE and VC not only assist system developers in the physical build stage but also in the following stages of the system lifecycle by providing a common virtual model, a digital twin (DT), of the manufacturing processes and the product. This developed model should possess the ability to simulate the system behaviour, e.g., the mechanics, kinematics, speed and acceleration profiles. Three stakeholders are involved in the development process: the machine builder, system integrator and end user. The current work focuses on the virtual engineering approach to support the entire lifecycle of a manufacturing system from the machine builder, system integrator and end user perspectives. For this purpose, it puts forward a systematic methodology of implementing VC and VE using a toolset developed by the Automation Systems Group at the University of Warwick within an industrial project. The suggested methodology is illustrated in a case study where a digital twin of a physical station was modelled, developed and tested in parallel with the physical machine development and build. Finally, the benefits and limitations are highlighted based on the gained outcomes and the implemented activities.

BackgroundThough SARS-CoV-2 outbreaks have been documented in occupational settings and in-person essential work has been suspected as a risk factor for COVID-19, occupational differences in excess mortality have, to date, not been examined. Such information could point to opportunities for intervention, such as vaccine prioritization or regulations to enforce safer work environments.Methods and findingsUsing autoregressive integrated moving average models and California Department of Public Health data representing 356,188 decedents 18-65 years of age who died between January 1, 2016 and November 30, 2020, we estimated pandemic-related excess mortality by occupational sector and occupation, with additional stratification of the sector analysis by race/ethnicity. During these first 9 months of the COVID-19 pandemic, working-age adults experienced 11,628 more deaths than expected, corresponding to 22% relative excess and 46 excess deaths per 100,000 living individuals. Sectors with the highest relative and per-capita excess mortality were food/agriculture (39% relative excess; 75 excess deaths per 100,000), transportation/logistics (31%; 91 per 100,000), manufacturing (24%; 61 per 100,000), and facilities (23%; 83 per 100,000). Across racial and ethnic groups, Latino working-age Californians experienced the highest relative excess mortality (37%) with the highest excess mortality among Latino workers in food and agriculture (59%; 97 per 100,000). Black working-age Californians had the highest per-capita excess mortality (110 per 100,000), with relative excess mortality highest among transportation/logistics workers (36%). Asian working-age Californians had lower excess mortality overall, but notable relative excess mortality among health/emergency workers (37%), while White Californians had high per-capita excess deaths among facilities workers (70 per 100,000).ConclusionsCertain occupational sectors are associated with high excess mortality during the pandemic, particularly among racial and ethnic groups also disproportionately affected by COVID-19. In-person essential work is a likely venue of transmission of coronavirus infection and must be addressed through vaccination and strict enforcement of health orders in workplace settings.

Complexity is one of the factors, inducing high cost, operational issues, and increased lead time for product realization and continues to pose challenges to manufacturing systems. One solution to reduce the negative impacts of complexity is its assessment, which can help designers to compare and rationalize various designs that meet the functional requirements. In this paper, a systemic approach is proposed to assess complexity of a product’s assembly. The approach is based on Hückel’s molecular orbital theory and defines complexity as a combination of both the complexity of product entities and their topological connections. In this model, the complexity of product entities (i.e., components and liaisons) is defined as the degree to which the entity comprises structural characteristics that lead to challenges during handling or fitting operations. The characterization of entity complexities is carried out based on the widely used DFA principles. Moreover, the proposed approach is tested on two case studies from electronics industry for its validity. The results showed that the approach can be used at initial design stages to improve both the quality and assemblability of products by reducing their complexity and accompanying risks.

Background To improve user-centred design efforts and efficiency; there is a need to disseminate information on modern day clinician preferences for technologies such as computerised clinical decision support (CDS).Objective To describe clinician perceptions regarding beneficial features of CDS for chronic medications in primary care.Methods This study included focus groups and clinicians individually describing their ideal CDS. Three focus groups were conducted including prescribing clinicians from a variety of disciplines. Outcome measures included identification of favourable features and unintended consequences of CDS for chronic medication management in primary care. We transcribed recordings, performed thematic qualitative analysis and generated counts when possible.Results There were 21 participants who identified four categories of beneficial CDS features during the group discussion: non-interruptive alerts, clinically relevant and customisable support, presentation of pertinent clinical information and optimises workflow. Non-interruptive alerts were broadly defined as passive alerts that a user chooses to review, whereas interruptive were active or disruptive alerts that interrupted workflow and one is forced to review before completing a task. The CDS features identified in the individual descriptions were consistent with the focus group discussion, with the exception of non-interruptive alerts. In the individual descriptions, 12 clinicians preferred interruptive CDS compared with seven clinicians describing non-interruptive CDS.Conclusion Clinicians identified CDS for chronic medications beneficial when they are clinically relevant and customisable, present pertinent clinical information (eg, labs, vitals) and improve their workflow. Although clinicians preferred passive, non-interruptive alerts, most acknowledged that these may not be widely seen and may be less effective. These features align with literature describing best practices in CDS design and emphasise those features clinicians prioritise, which should be considered when designing CDS for medication management in primary care. These findings highlight the disparity between the current state of CDS design and clinician-stated design features associated with beneficial CDS.

Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.

The computational aspects of the problem in this paper involve, firstly, selective mapping of methylated DNA clones according to methylation level and, secondly, extracting motif information from all the mapped elements in the absence of prior probability distribution. Our novel implementation of algorithms to map and maximize expectation in this setting has generated data that appear to be distinct for each lymphoma subtype examined. A “clone” represents a polymerase chain reaction (PCR) product (on average ~500 bp) which belongs to a microarray of 8544 such sequences preserving CpG-rich islands (CGIs) [1]. Accumulating evidence indicates that cancers including lymphomas demonstrate hypermethylation of CGIs “silencing” an increasing number of tumor suppressor (TS) genes which can lead to tumorigenesis.

East Asia's emergence as the forthcoming center of global manufacturing is leading nations in the region to reconsider their current and future role in the world economy. For its part, the Republic of Korea is pursuing a strategy to transform the country into the business hub of Northeast Asia. A central aspect in this strategy is the development of a regional “Pentaport” in the city of lncheon, which will consist of five components: an airport, a seaport, a business port, a technoport, and a leisure port. Hopefully, the Port of lncheon will be able to specialize in moving both coastal cargoes and transshipment cargoes to and from China and Japan using Short Sea Shipping (SSS). The first section of this paper provides an analysis of recent trade patterns between the Asian countries of the Asian-Pacific Economic Cooperation (APEC) agreement and the United States, with an emphasis on the Republic of Korea. The second part of the paper discusses current intermodal coastal shipping practices in the United States along the Gulf of Mexico and offers suggestions to the developers of Korea s Pentaport initiative; based upon observations in the United States, to maximize the likelihood of their success.

The feasibility of using imperfectly phenotyped "silver standard" samples identified from electronic medical record diagnoses is considered in genetic association studies when these samples might be combined with an existing set of samples phenotyped with a gold standard technique. An analytic expression is derived for the power of a chi-square test of independence using either research-quality case/control samples alone, or augmented with silver standard data. The subset of the parameter space where inclusion of silver standard samples increases statistical power is identified. A case study of dementia subjects identified from electronic medical records from the Electronic Medical Records and Genomics (eMERGE) network, combined with subjects from two studies specifically targeting dementia, verifies these results.

Non-covalent and covalent homo-oligomerization of membrane proteins regulates their subcellular localization and function. Here, we described a novel oligomerization mechanism affecting solute carrier family 30 member 3/zinc transporter 3 (SLC30A3/ZnT3). Oligomerization was mediated by intermolecular covalent dityrosine bonds. Using mutagenized ZnT3 expressed in PC12 cells, we identified two critical tyrosine residues necessary for dityrosine-mediated ZnT3 oligomerization. ZnT3 carrying the Y372F mutation prevented ZnT3 oligomerization, decreased ZnT3 targeting to synaptic-like microvesicles (SLMVs), and decreased resistance to zinc toxicity. Strikingly, ZnT3 harboring the Y357F mutation behaved as a "gain-of-function" mutant as it displayed increased ZnT3 oligomerization, targeting to SLMVs, and increased resistance to zinc toxicity. Single and double tyrosine ZnT3 mutants indicate that the predominant dimeric species is formed between tyrosine 357 and 372. ZnT3 tyrosine dimerization was detected under normal conditions and it was enhanced by oxidative stress. Covalent species were also detected in other SLC30A zinc transporters localized in different subcellular compartments. These results indicate that covalent tyrosine dimerization of a SLC30A family member modulates its subcellular localization and zinc transport capacity. We propose that dityrosine-dependent membrane protein oligomerization may regulate the function of diverse membrane protein in normal and disease states.

A considerable amount of research has been performed by various groups with the goal of detecting objects of interest in various environments. These objects can be above ground, partially buried, or fully buried. One of the detection methods that has piqued the interest of researchers is Automated Target Recognition (ATR) algorithms that can pick out the locations of such objects from infrared (IR) sensor imagery once it was recognized that such objects absorb and emit heat at rates that distinguish them from their surrounding environments. In order to obtain training data for ATRs, one option is to gather large volumes of field data from many locations, but such efforts are resource and labor intensive. Another option is to generate synthetic training data that simulates heat flux in real environments. Several research groups in the past have implemented a combination of both, using field data to inform and verify simulation tools that model heat flux, energy absorption, and IR emission from various parts of the environment, as well developing simulation workflows that model real environments. Such workflows are often developed for the purpose of simulating a single environment. Our goal is to modify and generalize such a workflow so that simulation parameters and the location of object of interest can be changed by non-experts or automated processes. In addition; this study is also focused on developing an efficient methodology to simulate a large variety of scenarios to produce varied sets of synthetic training data for the ATRs. The HPC simulations involved in generating the training data for ATRs require the exchange of data between several high-fidelity physics models. Efficient techniques have been implemented to ensure that the inputs to each component are consistent and correct. A more generalized workflow has been developed that is able to simulate complex scenarios with new LiDAR data, weather data, vegetation placements, target placements, and as output produce synthetic IR imagery useful for training ATRs. The synthetic IR images are automatically tagged with bounding boxes so that minimum post processing is required when the images are used for training the ATRs. The synthetic IR imagery was passed on to researchers developing ATRs and used to train the algorithms. The parts of the workflow that are streamlined can now be executed with relative ease on a high-performance computing environment and components of the workflow can now be more easily updated and modified in the future. KEYWORDS: Automatic Target Recognition, Environmental Simulation, Sensor Simulation, High Performance Computation, Configuration and Input File Management, Synthetic Infrared Imagery, INTRODUCTION A considerable amount of research has been performed by various groups with the goal of detecting objects of interest in various environments. These objects can be above ground, partially [...]