Predictive value of dynamic arterial elastance for vasopressor withdrawal: a systematic review and meta-analysis.

BACKGROUND: Dynamic arterial elastance (Eadyn) has been investigated for its ability to predict hypotension during the weaning of vasopressors. Our study focused on assessing Eadyn's performance in the context of critically ill adult patients admitted to the intensive care unit, regardless of diagnosis. MAIN BODY: Our study was conducted in accordance with the Preferred Reported Items for Systematic Reviews and Meta-Analysis checklist. The protocol was registered in PROSPERO (CRD42023421462) on May 26, 2023. We included prospective observational studies from the MEDLINE and Embase databases through May 2023. Five studies involving 183 patients were included in the quantitative analysis. We extracted data related to patient clinical characteristics, and information about Eadyn measurement methods, results, and norepinephrine dose. Most patients (76%) were diagnosed with septic shock, while the remaining patients required norepinephrine for other reasons. The average pressure responsiveness rate was 36.20%. The synthesized results yielded an area under the curve of 0.85, with a sensitivity of 0.87 (95% CI 0.74-0.93), specificity of 0.76 (95% CI 0.68-0.83), and diagnostic odds ratio of 19.07 (95% CI 8.47-42.92). Subgroup analyses indicated no variations in the Eadyn based on norepinephrine dosage, the Eadyn measurement device, or the Eadyn diagnostic cutoff to predict cessation of vasopressor support. CONCLUSIONS: Eadyn, evaluated through subgroup analyses, demonstrated good predictive ability for the discontinuation of vasopressor support in critically ill patients.

Characteristics of Neonatal Sepsis and Predictive Values of Polyfunctional Assessment of Umbilical Cord Neutrophils Based on Single Cell Proteomic Secretion.

The early diagnosis of neonatal sepsis is crucial as it remains a prevalent cause of neonatal mortality. In this study, we conducted an analysis on the clinical data and detection indicators of 22 cases with sepsis and 62 cases without sepsis among neonates. Our findings indicate that the clinical signs observed in neonates with sepsis lack specificity. In addition, the commonly used clinical inflammatory indicators (such as leukocyte count, neutrophil-to-lymphocyte ratio [NLR], C-reactive protein [CRP], procalcitonin) exhibit limited sensitivity and specificity. Furthermore, the current clinical measures lack the assessment of inflammatory factors. Therefore, in order to enhance the accuracy of early sepsis diagnosis in neonates, we have employed a novel microfluidic-based single-cell technology platform for the analysis of 32 cytokines secreted by neutrophils at the individual cell level under various toxin stimulation conditions. We have further investigated and compared the disparities in single-cell protein secretomics between umbilical cord blood neutrophils and healthy adult peripheral neutrophils within an in vitro sepsis model. Our findings indicate that in a resting state UCB neutrophils exhibited lower polyfunctionality compared with healthy adult blood neutrophils, and notable variations in cytokine secretion profiles were detected between the two groups. However, the polyfunctionality of UCB neutrophils significantly increased and surpassed that of healthy adult neutrophils when exposed to alpha-hemolysin or lipopolysaccharide. UCB neutrophils secreted a wide range of chemokines and inflammatory factors, among which GM-CSF and IL-18 were the most significant. Furthermore, we initially categorized the functional subgroups of neutrophils by considering the secretion of five primary cytokines by neutrophils (GM-CSF, IL-18, IL-8, MIP-1ß, and MIF). The current study, for the first time, examined in detail the heterogeneity of protein secretion and the functional diversity of UCB neutrophils stimulated by different antigens. Moreover, new insight into neonatal sepsis, early diagnosis, and wider clinical applications of UCB neutrophils are provided by these data.

Analysis and Modelling of the Predictive Value of PCT, PLR and NLR for Ureteric Sepsis after Ureteral Stone Surgery: A Retrospective Cohort Study.

OBJECTIVE: This study aimed to analyse the value of procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in predicting postoperative ureteral stone complications of urogenic sepsis. The production of a clinical prediction model could provide additional direction to reduce the likelihood of postoperative urogenital sepsis. METHODS: The clinical data of 520 patients with ureteral stones who underwent surgical treatment from January 2022, to September 2023, in the hospital were retrospectively analysed. The patients were divided into urogenic sepsis group (n = 42) and non-urogenic sepsis group (n = 478) in accordance with the occurrence of urogenic sepsis in the postoperative period. The peripheral blood PCT, PLR and NLR levels were collected within 24 h postoperatively in the two groups. The receiver operating characteristic (ROC) curve was employed to evaluate the predictive value of PCT, PLR and NLR levels for postoperative urogenital sepsis in patients with ureteral stones. RESULTS: Logistic regression analysis showed that PCT (odds ratio (OR) = 4.25, 95% CI: 1.85-9.78), PLR (OR = 4.00, 95% CI: 1.78-9.05) and NLR (OR = 2.29, 95% CI: 1.05-5.01) were risk factors for postoperative complication sepsis in patients with ureteral stones (p < 0.05). The ROC curves showed that the areas under the curve of PCT, PLR and NLR levels alone and in combination for predicting urogenic sepsis complications after emergency ureteral stone surgery were 0.683, 0.692, 0.611 and 0.799, respectively. CONCLUSIONS: Urogenic sepsis leads to increased serum PCT, NLR and PLR levels in patients undergoing surgical treatment for ureteral stones. Physicians should pay close attention to these indices to provide further theoretical support for reducing postoperative urogenic sepsis.

A review of reinforcement learning based hyper-heuristics.

The reinforcement learning based hyper-heuristics (RL-HH) is a popular trend in the field of optimization. RL-HH combines the global search ability of hyper-heuristics (HH) with the learning ability of reinforcement learning (RL). This synergy allows the agent to dynamically adjust its own strategy, leading to a gradual optimization of the solution. Existing researches have shown the effectiveness of RL-HH in solving complex real-world problems. However, a comprehensive introduction and summary of the RL-HH field is still blank. This research reviews currently existing RL-HHs and presents a general framework for RL-HHs. This article categorizes the type of algorithms into two categories: value-based reinforcement learning hyper-heuristics and policy-based reinforcement learning hyper-heuristics. Typical algorithms in each category are summarized and described in detail. Finally, the shortcomings in existing researches on RL-HH and future research directions are discussed.

Evaluation of the diagnostic and prognostic clinical values of circulating tumor DNA and cell-free DNA in pancreatic malignancies: a comprehensive meta-analysis.

Background: The diagnostic and prognostic clinical value of circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) in pancreatic malignancies are unclear. Herein, we aimed to perform a meta-analysis to evaluate ctDNA and cfDNA as potential diagnostic and prognostic biomarkers. Methods: PRISMA reporting guidelines were followed closely for conducting the current meta-analysis. The PubMed/Medline, Scopus, and Web of Science (WoS) databases were scanned in detail to identify eligible papers for the study. A quality assessment was performed in accordance with the REMARK criteria. The risk ratios (RRs) of the diagnostic accuracy of ctDNA compared to that of carbohydrate antigen 19.9 (CA 19.9) in all disease stages and the hazard ratios (HRs) of the prognostic role of ctDNA in overall survival (OS) were calculated with 95% confidence intervals (CIs). Results: A total of 18 papers were evaluated to assess the diagnostic accuracy and prognostic value of biomarkers related to pancreatic malignancies. The pooled analysis indicated that CA19.9 provides greater diagnostic accuracy across all disease stages than ctDNA or cfDNA (RR = 0.64, 95% CI: 0.50-0.82, p < 0.001). Additionally, in a secondary analysis focusing on prognosis, patients who were ctDNA-positive were found to have significantly worse OS (HR = 2.00, 95% CI: 1.51-2.66, p < 0.001). Conclusion: The findings of this meta-analysis demonstrated that CA19-9 still has greater diagnostic accuracy across all disease stages than KRAS mutations in ctDNA or cfDNA. Nonetheless, the presence of detectable levels of ctDNA was associated with worse patient outcomes regarding OS. There is a growing need for further research on this topic. Systematic review registration: https://doi.org/10.37766/inplasy2023.12.0092, identifier INPLASY2023120092.

Why do generation X customers use wearable fitness technology equipment after recovering from coronavirus? The role of perceived health risks.

The health sector has prioritized the physical health of vulnerable Generation X individuals at high Coronavirus risk. Despite vaccination efforts, both infected and healthy people continue facing health threats. Unlike other industries devastated by COVID-19, wearable fitness technology equipment (WFTE) is essential for health-focused individuals. This research examined customers' intention to use WFTE using an adapted Technology Acceptance Model (TAM) framework. A key contribution is the inclusion of perceived health risk and its impact on WFTE value perceptions and usage attitudes post-pandemic. The study gathered qualitative data from coronavirus patients and survey data from 513 participants. Structural equation modeling analysis supported the theoretical model. While the standard TAM evaluated intent to use WFTE, this study uniquely examined how WFTE's functional, hedonic, and symbolic value shapes its perceived value. Perceived health risk was found to significantly impact perceived WFTE value and usage attitudes after the pandemic recovery. Findings offer managerial implications to boost WFTE adoption among the vulnerable Generation X demographic.

Prediction of acute coronary syndrome in patients with myeloproliferative neoplasms.

Background: Patients with myeloproliferative neoplasms (MPN) are exposed to a higher risk of cardiovascular disease, especially cardiovascular calcification. The present research aimed to analyze the clinical features and coronary artery calcium score (CACS) in MPN patients, and construct an effective model to predict acute coronary syndrome (ACS) in MPN patients. Materials and methods: A total of 175 MPN patients and 175 controls were recruited from the First Affiliated Hospital of Ningbo University. Based on cardiovascular events, the MPN patients were divided into the ACS group and the non-ACS group. Multivariate Cox analysis was completed to explore ACS-related factors. Furthermore, ROC curves were plotted to assess the predictive effect of CACS combined with white blood cells (WBC) and platelet for ACS in MPN patients. Results: The MPN group exhibited a higher CACS than the control group (133 vs. 55, P < 0.001). A total of 16 patients developed ACS in 175 MPN patients. Compared with non-ACS groups, significant differences in age, diabetes, smoking history, WBC, percentage of neutrophil, percentage of lymphocyte, neutrophil count, hemoglobin, hematocrit, platelet, lactate dehydrogenase, ß 2-microglobulin, and JAK2V617F mutation were observed in the ACS groups. In addition, the CACS in the ACS group was also significantly higher than that in the non-ACS group (374.5 vs. 121, P < 0.001). The multivariable Cox regression analysis identified WBC, platelet, and CACS as independent risk factors for ACS in MPN patients. Finally, ROC curves indicated that WBC, platelet, and CACS have a high predictive value for ACS in MPN patients (AUC = 0.890). Conclusion: CACS combined with WBC and platelet might be a promising model for predicting ACS occurrence in MPN patients.

Prediction of Adolescents' Fluid Intelligence Scores based on Deep Learning with Reconstruction Regularization.

Objective: The aim of this study was to develop a predictive model for uncorrected/actual fluid intelligence scores in 9-10 year old children using magnetic resonance T1-weighted imaging. Explore the predictive performance of an autoencoder model based on reconstruction regularization for fluid intelligence in adolescents. Methods: We collected actual fluid intelligence scores and T1-weighted MRIs of 11,534 adolescents who completed baseline tasks from ABCD Data Release 3.0. A total of 148 ROIs were selected and 604 features were proposed by FreeSurfer segmentation. The training and testing sets were divided in a ratio of 7:3. To predict fluid intelligence scores, we used AE, MLP and classic machine learning models, and compared their performance on the test set. In addition, we explored their performance across gender subpopulations. Moreover, we evaluated the importance of features using the SHapley Additive Explain method. Results: The proposed model achieves optimal performance on the test set for predicting actual fluid intelligence scores (PCC = 0.209 ± 0.02, MSE = 105.212 ± 2.53). Results show that autoencoders with refactoring regularization are significantly more effective than MLPs and classical machine learning models. In addition, all models performed better on female adolescents than on male adolescents. Further analysis of relevant characteristics in different populations revealed that this may be related to gender differences in underlying fluid intelligence mechanisms. Conclusions: We construct a weak but stable correlation between brain structural features and raw fluid intelligence using autoencoders. Future research may need to explore ensemble regression strategies utilizing multiple machine learning algorithms on multimodal data in order to improve the predictive performance of fluid intelligence based on neuroimaging features.

Multi-instance learning attention model for amyloid quantification of brain sub regions in longitudinal cognitive decline.

Amyloid PET scans help in identifying the beta-amyloid deposition in different brain regions. The purpose of this study is to develop a deep learning model that can automate the task of finding amyloid deposition in different regions of the brain only by using PET scan and without the corresponding MRI scan. 2647 18F-Florbetapir PET scans are collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) from multiple centres taken over a period. A deep learning model based on multi-instance learning and attention is proposed which is trained and validated using 80% of the scans and the remaining 20% of the scans are used for testing the model. The performance of the model is validated using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The proposed model is further tested upon an external dataset consisting of 1413 18F-Florbetapir PET scans from the Anti-Amyloid Treatment in Asymptomatic Alzheimer's (A4) study. The proposed model achieves MAE of 0.0243 and RMSE of 0.0320 for summary Standardized Uptake Value Ratio (SUVR) based on composite reference region for ADNI test set. When tested on the A4-study dataset, the proposed model achieves MAE of 0.038 and RMSE of 0.0495 for summary SUVR based on the composite region. The results show that the proposed model provides less MAE and RMSE when compared with existing models. A graphical user interface is developed based on the proposed model where the predictions are made by selecting the files of 18F-Florbetapir PET scans.

Using applied social science disciplines to implement creative outdoor cat management solutions and avoid the trap of one-size-fits-all policies.

In the United States, policy conflicts have prevented successful population-level management of outdoor cats for decades. Wildlife conservation professionals have sought widespread use of humane dispatch (i.e., lethal culling applied humanely), whereas cat welfare professionals have promoted trap-neuter-return (TNR) (cats are trapped, neutered, and returned to the outdoors). These conflicts represent a policy panacea trap, which we argue drives many conservation conflicts. In these situations, the focus on defending a one-size-fits-all policy fails to account for the value differences that shape the different understandings of the problem and desired outcomes associated with each policy, as well as complexities in the social-ecological system. Over the past 5 years, a group of wildlife conservation and cat welfare professionals codeveloped a set of products that have started to be used to help organizations break out of the policy panacea trap. We used a case study to illustrate how efforts grounded in applied social science disciplines, such as science communication, social-ecological systems, and conservation marketing, can help identify a more robust set of policy options tailored to local management and cultural contexts for successful implementation. Shifting the focus to embrace a shared understanding of the broader system helped us identify areas for collaboration, broaden the policy toolbox, and allow space for policy tools originally framed as opposing panaceas. This work helped prepare all parties to have difficult but productive discussions and address shared policy needs. We suggest that many value-based conservation conflicts would benefit from similar efforts that use applied social science to transform how conflict is addressed, moving beyond policy panaceas that end in stalemate to develop shared understandings of context-specific policies, and to identify opportunities for creative cooperation that yield real conservation progress.

Uso de las disciplinas aplicadas de las ciencias sociales para implementar soluciones creativas de manejo de gatos callejeros y evitar la trampa de las políticas universales Resumen Durante décadas, los conflictos entre políticas han evitado un manejo exitoso a nivel poblacional de los gatos callejeros en los Estados Unidos. Los profesionales de la conservación silvestre han buscado el uso extenso de los sacrificios con humanidad, mientras que los profesionales del bienestar felino han promovido la captura­esterilización­liberación (CEL) de los gatos. Estos conflictos representan una trampa panacea de políticas, la cual argumentamos causa muchos conflictos de conservación. En estas situaciones, el enfoque en defender una política universal no logra considerar la diferencia de valores que forman los diferentes entendimientos del problema y los resultados deseados que se asocian con cada política, así como las complejidades dentro del sistema socio­ecológico. A lo largo de los últimos cinco años, un grupo de profesionales de la conservación silvestre y del bienestar felino desarrollaron en conjunto una serie de productos que han comenzado a ayudar a las organizaciones a salir de la trampa panacea de políticas. Usamos un estudio de caso para ilustrar cómo los esfuerzos cimentados en las disciplinas aplicadas de las ciencias sociales (p. ej.: las ciencias de la comunicación, los sistemas socio­ecológicos y el marketing de la conservación) pueden ayudar a identificar un conjunto más sólido de opciones de políticas personalizadas para el manejo local y los contextos culturales para tener una implementación exitosa. El cambio de enfoque para aceptar el entendimiento compartido del sistema más amplio nos ayudó a identificar áreas de colaboración, a ampliar las herramientas para las políticas y a permitirle espacio a las herramientas formuladas originalmente como panaceas contrarias. Este trabajo ayudó a que todas las partes se prepararan para tener discusiones difíciles pero productivas y para abordar las necesidades compartidas de las políticas. Sugerimos que muchos conflictos de conservación basados en los valores se beneficiarían de un esfuerzo similar que use las ciencias sociales aplicadas para transformar cómo se aborda el conflicto, llegando más allá de las panaceas de políticas que terminan en un punto muerto para el desarrollo del entendimiento compartido de políticas específicas al contexto, y para identificar las oportunidades de cooperación creativa que producen un progreso real de la conservación.

Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.

Background: No robust predictive biomarkers exist to identify non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapies. The aim of this study was to explore the role of delta-radiomics features in predicting the clinical outcomes of patients with advanced NSCLC who received ICI therapy. Methods: Data of 179 patients with advanced NSCLC (stages IIIB-IV) from two institutions (Database 1 =133; Database 2 =46) were retrospectively analyzed. Patients in the Database 1 were randomly assigned into training and validation dataset, with a ratio of 8:2. Patients in Database 2 were allocated into testing dataset. Features were selected from computed tomography (CT) images before and 6-8 weeks after ICI therapy. For each lesion, a total of 1,037 radiomic features were extracted. Lowly reliable [intraclass correlation coefficient (ICC) <0.8] and redundant (r>0.8) features were excluded. The delta-radiomics features were defined as the relative net change of radiomics features between two time points. Prognostic models for progression-free survival (PFS) and overall survival (OS) were established using the multivariate Cox regression based on selected delta-radiomics features. A clinical model and a pre-treatment radiomics model were established as well. Results: The median PFS (after therapy) was 7.0 [interquartile range (IQR): 3.4, 9.1] (range, 1.4-13.2) months. To predict PFS, the model established based on the five most contributing delta-radiomics features yielded Harrell's concordance index (C-index) values of 0.708, 0.688, and 0.603 in the training, validation, and testing databases, respectively. The median survival time was 12 (IQR: 8.7, 15.8) (range, 2.9-23.3) months. To predict OS, a promising prognostic performance was confirmed with the corresponding C-index values of 0.810, 0.762, and 0.697 in the three datasets based on the seven most contributing delta-radiomics features, respectively. Furthermore, compared with clinical and pre-treatment radiomics models, the delta-radiomics model had the highest area under the curve (AUC) value and the best patients' stratification ability. Conclusions: The delta-radiomics model showed a good performance in predicting therapeutic outcomes in advanced NSCLC patients undergoing ICI therapy. It provides a higher predictive value than clinical and the pre-treatment radiomics models.

The Construction of a Nomogram Using the Pan-Immune-Inflammation Value Combined with a PILE Score for Immunotherapy Prediction Prognosis in Advanced NSCLC.

Purpose: The purpose of this study was to investigate the predictive value of Pan-Immune-Inflammation Value (PIV) combined with the PILE score for immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) and to construct a nomogram prediction model to provide reference for clinical work. Patients and Methods: Patients with advanced NSCLC who received ICIs treatment in Qingdao Municipal Hospital from January 2019 to December 2021 were selected as the study subjects. The chi-square test, Kaplan-Meier survival analysis, and Cox proportional risk regression analysis were used to evaluate the prognosis. The results were visualized by a nomogram, and the performance of the model was judged by indicators such as the area under the subject operating characteristic curve (AUC) and C-index. The patients were divided into high- and low-risk groups by PILE score, and the prognosis of patients in different risk groups was evaluated. Results: Multivariate Cox regression analysis showed that immune-related adverse events (irAEs) were prognostic factors for overall survival (OS) improvement, and ECOG PS score ≥2, bone metastases before treatment, and high PIV expression were independent risk factors for OS. The C index of OS predicted by the nomogram model is 0.750 (95% CI: 0.677-0.823), and the Calibration and ROC curves show that the model has good prediction performance. Compared with the low-risk group, patients in the high-risk group of PILE were associated with a higher inflammatory state and poorer physical condition, which often resulted in a poorer prognosis. Conclusion: PIV can be used as a prognostic indicator for patients with advanced NSCLC treated with ICIs, and a nomogram prediction model can be constructed to evaluate the survival prediction of patients, thus contributing to better clinical decision-making and prognosis assessment.

Health Star Rating of Nonalcoholic, Packaged, and Ready-to-Drink Beverages in Türkiye: A Decision Tree Model Study.

This study aimed to compare the nutritional quality of beverages sold in Türkiye according to their labeling profiles. A total of 304 nonalcoholic beverages sold in supermarkets and online markets with the highest market capacity in Türkiye were included. Milk and dairy products, sports drinks, and beverages for children were excluded. The health star rating (HSR) was used to assess the nutritional quality of beverages. The nutritional quality of beverages was evaluated using a decision tree model according to the HSR score based on the variables presented on the beverage label. Moreover, confusion matrix tests were used to test the model's accuracy. The mean HSR score of beverages was 2.6±1.9, of which 30.2% were in the healthy category (HSR≥3.5). Fermented and 100% fruit juice beverages had the highest mean HSR scores. According to the decision tree model of the training set, the predictors of HSR quality score, in order of importance, were as follows: added sugar (46%), sweetener (28%), additives (19%), fructose-glucose syrup (4%), and caffeine (3%). In the test set, the accuracy rate and F1 score were 0.90 and 0.82, respectively, suggesting that the prediction performance of our model had the perfect fit. According to the HSR classification, most beverages were found to be unhealthy. Thus, they increase the risk of the development of obesity and other diseases because of their easy consumption. The decision tree learning algorithm could guide the population to choose healthy beverages based on their labeling information.

A Review on Camel Milk Composition, Techno-Functional Properties and Processing Constraints.

Camel milk plays a critical role in the diet of peoples belongs to the semi-arid and arid regions. Since prehistoric times, camel milk marketing was limited due to lacking the processing facilities in the camel-rearing areas, nomads practiced the self-consumption of raw and fermented camel milk. A better understanding of the techno-functional properties of camel milk is required for product improvement to address market and customer needs. Despite the superior nutraceutical and health promoting potential, limited camel dairy products are available compared to other bovines. It is a challenging impetus for the dairy industry to provide diversified camel dairy products to consumers with superior nutritional and functional qualities. The physicochemical behavior and characteristics of camel milk is different than the bovine milk, which poses processing and technological challenges. Traditionally camel milk is only processed into various fermented and non-fermented products; however, the production of commercially important dairy products (cheese, butter, yogurt, and milk powder) from camel milk still needs to be processed successfully. Therefore, the industrial processing and transformation of camel milk into various products, including fermented dairy products, pasteurized milk, milk powder, cheese, and other products, require the development of new technologies based on applied research. This review highlights camel milk's processing constraints and techno-functional properties while presenting the challenges associated with processing the milk into various dairy products. Future research directions to improve product quality have also been discussed.

The effect of quarantine policy on pollution emission and the usage of private transportation in urban areas.

Governmental policies, regulations, and responses to the pandemic can benefit from a better understanding of people's resulting behaviours before, during, and after COVID-19. To avoid the inelasticity and subjectivity of survey datasets, several studies have already used some objective variables like air pollutants to estimate the potential impacts of COVID-19 on the urban transportation system. However, the usage of reactant gases and a narrow time scale might weaken the results somehow. Here, both the objective passenger volume of public transport and the concentration of private traffic emitted black carbon (BC) from 2018 to 2023 were collected/calculated to decipher the potential relationship between public and private traffic during the COVID-19 period. Our results indicated that the commuting patterns of citizens show significant (p < 0.01) different patterns before, during, and after the pandemic. To be specific, public transportation showed a significant (p < 0.01) positive correlation with private transportation before the pandemic. This public transportation was significantly (p < 0.01) affected by the outbreaks of COVID-19, showing a significant (p < 0.01) negative correlation with private transportation. Such impacts of the virus and governmental policy would affect the long-term behaviour of individuals and even affect public transportation usage after the pandemic. Our results also indicated that such behaviour was mainly linked to the governmental restriction policy and would soon be neglected after the cancellation of the restriction policy in China.

Value-based preoperative assessment in a large academic hospital.

BACKGROUND: Value-based healthcare (VBHC) is an approach that focuses on delivering the highest possible value for patients while driving cost efficiency in health services. It emphasizes improving patient outcomes and experiences while optimizing the use of resources, shifting the healthcare system's focus from the volume of services to the value delivered. Our study assessed the effectiveness of implementing a VBHC-principled, tailored preoperative evaluation in enhancing patient care and outcomes, as well as reducing healthcare costs. METHODS: We employed a quality improvement, before-and-after approach to assessing the effects of implementing VBHC strategies on the restructuring of the preoperative evaluation clinics at Humanitas Research Hospital. The intervention introduced a VBHC-tailored risk matrix during the postintervention phase (year 2021), and the results were compared with those of the preintervention phase (2019). The primary study outcome was the difference in the number of preoperative tests and visits at baseline and after the VBHC approach. Secondary outcomes were patient outcomes and costs. RESULTS: A total of 9722 patients were included: 5242 during 2019 (baseline) and 4,480 during 2021 (VBHC approach). The median age of the population was 63 (IQR 51-72), 23% of patients were classified as ASA 3 and 4, and 26.8% (2,955 cases) were day surgery cases. We found a considerable decrease in the number of preoperative tests ordered for each patient [6.2 (2.5) vs 5.3 (2.6) tests, p < 0.001]. The number of preoperative chest X-ray, electrocardiogram, and cardiac exams decreased significantly with VBHC. The length of the preoperative evaluation was significantly shorter with VBHC [373 (136) vs 290 (157) min, p < 0.001]. Cost analysis demonstrated a significant reduction in costs, while there was no difference in clinical outcomes. CONCLUSIONS: We demonstrated the feasibility, safety, and cost-effectiveness of a tailored approach for preoperative evaluation. The implementation of VBHC enhanced value, as evidenced by decreased patient time in preoperative evaluation and by a reduction in unnecessary preoperative tests.

Unraveling elements of value-based pricing from a pharmaceutical industry's perspective: a scoping review.

Health authorities use value-based pricing models to determine the value of innovative drugs and to establish a price. Pharmaceutical companies prefer value-based pricing over cost-based pricing. It is ambiguous whether value-based pricing has the same meaning to these stakeholders. We aimed to identify the elements that attribute to value-based pricing of innovative drugs from a pharmaceutical industry's perspective and as possible starting point for (value-based) contracting of drugs. We performed a scoping review of publications available in scientific databases with terms such as 'value-based pricing', 'pharmacoeconomics', 'drug cost', 'innovative drug' and 'drug therapy'. We included 31 publications, covering value elements of innovative drugs from a pharmaceutical industry's perspective. Overall, all found elements of value-based pricing were congruent with the elements of value-based pricing from a health authority's perspective. However, the emphasis placed on the elements differed. The most frequently mentioned elements in our review were economic considerations and cost aspects. Least mentioned were elements regarding cost-effectiveness, disease characteristics and patient characteristics. Although all elements in the drug value framework were present which indicate congruity, there seems controversy on the importance of cost-effectiveness as an element of value. Consequently, establishing a coherent and to all stakeholders' acceptable framework to value and price innovative drugs seems complicated. Mutual understanding can be found in the value elements societal considerations and healthcare process benefits. Our results supported the importance of economic and cost aspects regarding determination of prices of innovative drugs. Further research is required to quantify the weights of all relevant elements in the drug value framework, observe their possible interlinkages, and to weigh them over time.

Phylogenetic Diversity Indices from an Affine and Projective Viewpoint.

Phylogenetic diversity indices are commonly used to rank the elements in a collection of species or populations for conservation purposes. The derivation of these indices is typically based on some quantitative description of the evolutionary history of the species in question, which is often given in terms of a phylogenetic tree. Both rooted and unrooted phylogenetic trees can be employed, and there are close connections between the indices that are derived in these two different ways. In this paper, we introduce more general phylogenetic diversity indices that can be derived from collections of subsets (clusters) and collections of bipartitions (splits) of the given set of species. Such indices could be useful, for example, in case there is some uncertainty in the topology of the tree being used to derive a phylogenetic diversity index. As well as characterizing some of the indices that we introduce in terms of their special properties, we provide a link between cluster-based and split-based phylogenetic diversity indices that uses a discrete analogue of the classical link between affine and projective geometry. This provides a unified framework for many of the various phylogenetic diversity indices used in the literature based on rooted and unrooted phylogenetic trees, generalizations and new proofs for previous results concerning tree-based indices, and a way to define some new phylogenetic diversity indices that naturally arise as affine or projective variants of each other or as generalizations of tree-based indices.

Statistical Assessment of the Effectiveness of Multiple COVID-19 Vaccinations on Daily Confirmed Cases in Seoul City.

This study investigates the effectiveness of multiple COVID-19 vaccinations on daily confirmed cases in Seoul City. Utilizing comprehensive data on vaccinated individuals and confirmed cases sourced from the official website of the Korean Ministry of the Interior and Safety, we conducted detailed statistical analyses to assess the impact of each vaccination dose. The study covers data from April 21, 2021, to September 29, 2022. Statistical multiple linear regression was employed to analyze the relationship between daily confirmed cases (positive outcomes from PCR tests) and multiple vaccine doses, using p-values as the criteria for determining the effectiveness of each dose. The analysis included data from four vaccination doses. The analysis reveals that the first, second, and third doses of the COVID-19 vaccines have a statistically significant positive effect associated with the daily confirmed cases. However, the study finds that the fourth dose does not show a statistically significant impact on the reduction of daily confirmed cases. This suggests that while the initial three doses are crucial for establishing and maintaining high levels of immunity, the incremental benefit of subsequent doses may diminish.