The impact of COVID-19 threats, and preventive chain of action: molecule to medicine

The emergence of the novel coronavirus (SARS-CoV-2) had affected us significantly from the individual level, to nationwide and global with a big loss of finances, and the freezing of various factories, schools, and transportation in communities The pandemic started with anxiety and a loss of health guidance and policies due to the unknown causes of viral transmission to human features as well as a high infection rate with low mortality It remains the original source of Covid-19 where it comes from and what is the reality of real viral entities and its origin such as natural born and recombinant viral variants in the case of COVID-19 pandemic. This sentence is unclear. In this short perspective article, we address some issues of risk assessment and management issues using molecular-based decision tools which may benefit or provide future drills to counteract health and clinic safety against a viral pandemic. Every pandemic gives us life threatening lessons on previous and disconnected human networks due to uncertainty of viral infection, which we learned from this COVID-19 pandemic case as well. It gives us some insight on how to rebuild our community regarding the strength of public health and the integration of science tools into the early phase of medical application, such as the role of molecular diagnostics through educational engagement. To promote the value of awareness with solid knowledge-based communication and to develop resilient preventive solutions for supply chains or prevention, the systematic practice of connectivity through visual format using multidimensional data outcomes could help reconsider the leverage of molecules as a bridge for the improvement and application of updated scientific tools of prediction precisely to identify unknown pathogens encompass rigor community-based activity likelihood sensitivity and resistance to pathogen infiltrated society in the future.

Measurement-Based and Data-Informed Psychological Therapy

Outcome measurement in the field of psychotherapy has developed considerably in the last decade. This review discusses key issues related to outcome measurement, modeling, and implementation of data-informed and measurement-based psychological therapy. First, an overview is provided, covering the rationale of outcome measurement by acknowledging some of the limitations of clinical judgment. Second, different models of outcome measurement are discussed, including pre–post, session-by-session, and higher-resolution intensive outcome assessments. Third, important concepts related to modeling patterns of change are addressed, including early response, dose–response, and nonlinear change. Furthermore, rational and empirical decision tools are discussed as the foundation for measurement-based therapy. Fourth, examples of clinical applications are presented, which show great promise to support the personalization of therapy and to prevent treatment failure. Finally, we build on continuous outcome measurement as the basis for a broader understanding of clinical concepts and data-driven clinical practice in the future. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Use of the Correlation Coefficient for Rotating Machine Monitoring

The use of electronics and computer technology in production systems has greatly improved the quality of our industrial products. The productivity of these installations is a function of the maintenance quality applied to the equipment. Several methods are used to monitor the functioning of industrial installations. One of these methods is vibration analysis. The vibration signals from the rotating machines support several types of information related to the working state of the production tool. The processing of this information makes it possible to have decision tools for maintenance. In this work, we propose a method of anticipating the maintenance of rotating machines. The algorithm we propose starts with the removal of 512 point windows during the running time of the ball bearing. Each signal is decomposed by DWT: we obtain the approximation coefficients. These coefficients make it possible to determine the correlation coefficient between the so-called reference window and the other windows following the functioning of the ball bearing. The correlation coefficient is then the fundamental element of the decision. This algorithm has been applied to real vibration data and the results are encouraging.

Development of a Model-Informed Dosing Tool to Optimise Initial Antibiotic Dosing—A Translational Example for Intensive Care Units

The prevalence and mortality rates of severe infections are high in intensive care units (ICUs). At the same time, the high pharmacokinetic variability observed in ICU patients increases the risk of inadequate antibiotic drug exposure. Therefore, dosing tailored to specific patient characteristics has a high potential to improve outcomes in this vulnerable patient population. This study aimed to develop a tabular dosing decision tool for initial therapy of meropenem integrating hospital-specific, thus far unexploited pathogen susceptibility information. An appropriate meropenem pharmacokinetic model was selected from the literature and evaluated using clinical data. Probability of target attainment (PTA) analysis was conducted for clinically interesting dosing regimens. To inform dosing prior to pathogen identification, the local pathogen-independent mean fraction of response (LPIFR) was calculated based on the observed minimum inhibitory concentrations distribution in the hospital. A simple, tabular, model-informed dosing decision tool was developed for initial meropenem therapy. Dosing recommendations achieving PTA > 90% or LPIFR > 90% for patients with different creatinine clearances were integrated. Based on the experiences during the development process, a generalised workflow for the development of tabular dosing decision tools was derived. The proposed workflow can support the development of model-informed dosing tools for initial therapy of various drugs and hospital-specific conditions.

Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization

Background Mobile technology offers unique opportunities for monitoring short-term suicide risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk factors were assessed daily following discharge to predict near-term suicidal ideation and inform decision algorithms for identifying elevations in daily level risk, with implications for real-time suicide-focused interventions. Methods Adolescents (N = 78; 67.9% female) completed brief surveys texted daily for 4 weeks after discharge (n = 1621 observations). Using multi-level classification and regression trees (CARTSs) with repeated 5-fold cross-validation, we tested (a) a simple prediction model incorporating previous-day scores for each of 10 risk factors, and (b) a more complex model incorporating, for each of these factors, a time-varying person-specific mean over prior days together with deviation from that mean. Models also incorporated missingness and contextual (study week, day of the week) indicators. The outcome was the presence/absence of next-day suicidal ideation. Results The best-performing model (cross-validated AUC = 0.86) was a complex model that included ideation duration, hopelessness, burdensomeness, and self-efficacy to refrain from suicidal action. An equivalent model that excluded ideation duration had acceptable overall performance (cross-validated AUC = 0.78). Models incorporating only previous-day scores, with and without ideation duration (cross-validated AUC of 0.82 and 0.75, respectively), showed relatively weaker performance. Conclusions Results suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts. Findings represent an important step in the development of decision tools identifying short-term risk as well as guiding timely interventions sensitive to proximal elevations in suicide risk in daily life.

Global Satellite-Based Coastal Bathymetry from Waves

The seafloor—or bathymetry—of the world’s coastal waters remains largely unknown despite its primary importance to human activities and ecosystems. Here we present S2Shores (Satellite to Shores), the first sub-kilometer global atlas of coastal bathymetry based on depth inversion from wave kinematics captured by the Sentinel-2 constellation. The methodology reveals coastal seafloors up to a hundred meters in depth which allows covering most continental shelves and represents 4.9 million km2 along the world coastline. Although the vertical accuracy (RMSE 6–9 m) is currently coarser than that of traditional surveying techniques, S2Shores is of particular interest to countries that do not have the means to carry out in situ surveys and to unexplored regions such as polar areas. S2Shores is a major step forward in mitigating the effects of global changes on coastal communities and ecosystems by providing scientists, engineers, and policy makers with new science-based decision tools.

122 Comparing the External Validity of Clinical Prediction Tools Incorporating Serum Procalcitonin to Identify Febrile Infants (0-90 days) at Low Risk for Serious Bacterial Infection: A Retrospective Analysis

Primary Subject area Hospital Paediatrics Background Procalcitonin (PCT), a serum inflammatory biomarker, has recently been incorporated into several clinical decision tools to identify febrile infants at low risk for serious bacterial infection (SBI). These include the Pediatric Emergency Care Applied Research Network (PECARN) tool, the “Step-by-Step” approach, and the “Laboratory-Score.” Our institution is one of a few in Canada to incorporate serum PCT routinely, allowing us to complete these clinical decision tools. Thus, the objectives of this study were to externally validate and compare these tools in a Canadian pediatric population, indirectly assessing the utility of serum PCT in clinical practice. Objectives The primary outcomes were to derive the sensitivity, specificity, and negative predictive value (NPV) of each stratification tool in predicting SBI. Design/Methods We retrospectively reviewed the medical records of all infants less than 90 days of age presenting to our emergency departments between April 2016 and October 2019 with fever without a source, who had sufficient investigations to apply one (or more) of the above clinical decision tools. Results We applied the PECARN tool to 51 cases, and had sufficient data to apply the Step-by-Step and Lab Score criteria to 43 of these patients. Seventeen of the 51 patients (33%) were identified to have a SBI. The PECARN and Step-by-Step tools both had NPV of 100%; both were sensitive enough to detect all patients with SBI. They had poor specificity (0.47 and 0.55 respectively). These two tools were in agreement in 38 of 43 (88%) cases. Though the Laboratory-Score had the highest positive predictive value (0.88) and specificity (0.85), it failed to identify 3 of 16 true cases of SBI and had a suboptimal sensitivity of 0.81. Conclusion The ability to identify febrile infants at low risk for SBI in a reliable way would have significant clinical potential to change practice. Given the strong NPV of both the PECARN and Step-by-Step tools, we conclude that their use, incorporating the measurement of serum PCT, may be of use in reducing pediatric hospitalization, use of empiric broad-spectrum antibiotics, and investigations such as lumbar punctures, in these low-risk patients. This study had a small sample size. We look forward to analyzing a larger population of febrile infants, particularly in infants of a chronologic age (28-90 days) more amenable to clinical practice change.

Four Simple Questions: Decision-Centered Risk and Project Management

A decision-centric approach to projects creates confidence, improves value, and shortens time to revenue. A straight-forward objective based approach to managing project decisions is presented in the form of four primary questions. Those questions are:Does the issue/threat/opportunity make a material difference to the project? (Materiality)Can anything be done to affect the outcome? (Influence)Can you afford to do anything about it? (Value)What if you are wrong? (Confidence) Materiality – An issue/threat/opportunity must make a material difference to a project decision to be worth receiving attention. The concept of materiality will vary in size and consequence from project to project, so it is important to maintain a decision focus. Understanding the variability in the project with respect to decision thresholds can provide an indication of materiality. We must also ask how different our current assessment of the project, or its environment could get before we would like to change our decision. Affective ability – Accepted risk-management options of avoidance, mitigation, transfer, and acceptance present the decision options within this category. In considering the consequences the options, decision tools such as Indifference Assessment and Pain and Regret Assessment. Avoidance, Mitigation, or Transfer – while most projects can benefit through risk reduction, such effort must make economic sense. Risk reduction paths must add value to the project through added upside or elimination of at least a portion of downside threat. The value of these efforts is aided by use of tools such as Value-of-Information, Value-of-Control, and Value-of-learning. Being wrong – Making a regretful decision is always a possibility but the source of the "wrongness" and its likely impact is often overlooked by teams. In project planning and execution, decision-makers are often presented with a plethora of issues, threats, and opportunities. From development planning through implementation significant time and resource waste can be cut by prioritizing effort to the issues that matter. Understanding the issues in the context of materiality and then what to do, if anything, about an issue, becomes key to maximizing success. This approach cuts waste and focuses the attention on what matters. Decision Intelligence not only increases the probability of making the best decisions, but it also prioritizes work to those items that matter either for value or decision path. While most of the decision tools referenced are well documented in the literature, placing them into the context of the Four Question Approach allows teams and management to focus more closely on efficiently mitigating issues, shortening workflow, and creating significantly higher decision confidence. This novel approach works well in all phases of project planning through project management implementation.

Review of Existing Energy Retrofit Decision Tools for Homeowners

Energy retrofit tools are considered by many countries as one of the strongest incentives to encourage homeowners to invest in energy renovation. These tools help homeowners to get an initial overview of suitable retrofit measures. Although a large number of energy retrofit tools have been developed to inspire and educate homeowners, energy renovation by individual homeowners is still lagging and the impact of current tools is insufficient as awareness and information issues remain one of main obstacles that hinder the uptake of energy retrofitting schemes. This research extends the current knowledge by analysing the characteristics of 19 tools from 10 different countries. The selected tools were analysed in terms of energy calculation methods, features, generation and range of retrofit measures, evaluation criteria, and indications on financial support. The review indicates that: (1) most toolkits use empirical data-driven methods, pre-simulated databases, and normative calculation methods; (2) few tools generate long-term integrated renovation packages; (3) technological, social, and aesthetic aspects are rarely taken into consideration; (4) the generation of funding options varies between the existing tools; (5) most toolkits do not suggest specific retrofit solutions adapted to traditional buildings; and (6) preferences of homeowners in terms of evaluation criteria are often neglected.