This study develops a model-based index approach called the Generalised Shared Component Model (GSCM) by drawing on the large field of factor models. The proposed fully Bayesian approach accommodates heteroscedastic model error, multiple shared factors and flexible spatial priors. Moreover, unlike previous index approaches, our model provides indices with uncertainty. Focusing on unhealthy behaviors that increase the risk of cancer, the proposed GSCM is used to develop the Area Indices of Behaviors Impacting Cancer product - representing the first area level cancer risk factor index in Australia. This advancement aids in identifying communities with elevated cancer risk, facilitating targeted health interventions.

UNASSIGNED: Canine lymphoma is the most common hematopoietic cancer in dogs. Numerous studies have evaluated the prognostic value of hematological abnormalities and ratios in both humans and dogs with lymphoma.
UNASSIGNED: To compare hematological parameters and complete blood count ratios between a population of dogs affected by lymphoma and healthy dogs to identify potential prognostic markers for lymphoma.
UNASSIGNED: This retrospective case-control study compares hematological parameters and complete blood count ratios between a population of 114 dogs affected by multicentric large B-cell lymphoma (LBCL) and 60 healthy dogs.
UNASSIGNED: The study found several statistically significant differences between the hematological indices of LBCL dogs and healthy dogs, but no correlation between these parameters and the survival times of 78 dogs treated with chemotherapy Madison Wisconsin protocol. In addition, hematological alterations were evaluated such as anemia, leukocytosis, and thrombocytopenia.
UNASSIGNED: Hematological ratios have been suggested as potential prognostic markers for canine LBCL but their real prognostic value remains controversial and requires future investigation.

暂无摘要。

The subject of this article is the discovery of dento-dental disharmony (DDD) at the end of treatment. Lack of diagnosis is the source of this type of disappointment.
The diagnosis of DDD is not easily accessible on clinical examination and the compensations it generates mask it, especially if it is associated with other dysmorphoses. The use of indices, the best-known of which is Bolton\'s, enables diagnosis with the setup, a pre-treatment model which also has many other prognostic interests.
Once DDD has been considered, it can be resolved by adapting dental volumes, either by subtraction or addition.
Advances in computerized diagnosis with artificial intelligence are opening up new avenues for the systematic diagnosis of DDD.
L’objectif de cet article est de présenter, à l’issue de la découverte d’une dysharmonie dento-dentaire (DDD) en fin de traitement orthodontique, les modalités de sa prise en charge. Le défaut de diagnostic initial est la source de ce type de déconvenue.
Le diagnostic de la DDD n’est pas facilement accessible à l’examen clinique, les compensations qu’elle génère la masquent, surtout si elle est associée à d’autres dysmorphoses. L’utilisation d’indices, dont le plus connu est celui de Bolton, permet le diagnostic avec le setup, maquette de prétraitement qui a aussi beaucoup d’autres intérêts pronostiques.
Une fois prise en compte, la DDD trouve sa solution par l’adaptation des volumes dentaires soit par soustraction amélaire soit par addition.
Les avancées du diagnostic informatisé avec l’intelligence artificielle ouvrent des portes pour intégrer le diagnostic systématique de la DDD.

BACKGROUND: Hemolysis is a common reason for nonreporting results in biochemistry and is measured using the hemolysis index (HI), with nonreporting limits set for analytes by manufacturers.
OBJECTIVE: To verify the nonreporting HI limit for potassium, phosphate, magnesium, aspartate aminotransferase (AST), lactate dehydrogenase (LDH), iron, CA19-9, and vitamin D on the Beckman Coulter AU5800/DxI800 analyzers.
METHODS: Hemolysate was created from EDTA-lined tubes of whole blood using an osmotic shock procedure. The hemolysate underwent serial dilutions with saline and was spiked in paired serum. The delta changes in HI and analyte concentration were measured, assessed using regression analysis, and compared against calculated reference change values.
RESULTS: A linear relationship between increasing HI and increasing analyte concentration (R2 > 0.9) was observed for potassium (y = 0.8864x), phosphate (y = 0.1079x), magnesium (y = 0.0678x), AST (y = 29.035x), and LDH (y = 350x). Increasing HI values did not have a linear effect on iron (y = -0.2544x), CA19-9 (y = 2.7019x), or vitamin D (y = 8.036x) concentrations.
CONCLUSIONS: The results from this experiment support increasing the HI nonreporting limit to 100 mg/dL for potassium; 200 mg/dL for magnesium; and 300 mg/dL for phosphate, CA19-9, and vitamin D. The iron assay is not affected by hemolysis as high as 500 mg/dL. The current HI nonreporting limit of 50 mg/dL is appropriate for LDH.

Fundamental data analysis assists in the evaluation of critical questions to discern essential facts and elicit formerly invisible evidence. In this article, we provide clarity into a subtle phenomenon observed in cancer incidences throughout the time of the COVID-19 pandemic. We analyzed the cancer incidence data from the American Cancer Society [1]. We partitioned the data into three groups: the pre-COVID-19 years (2017, 2018), during the COVID-19 years (2019, 2020, 2021), and the post-COVID-19 years (2022, 2023). In a novel manner, we applied principal components analysis (PCA), computed the angles between the cancer incidence vectors, and then added lognormal probability concepts in our analysis. Our analytic results revealed that the cancer incidences shifted within each era (pre, during, and post), with a meaningful change in the cancer incidences occurring in 2020, the peak of the COVID-19 era. We defined, computed, and interpreted the exceedance probability for a cancer type to have 1000 incidences in a future year among the breast, cervical, colorectal, uterine corpus, leukemia, lung & bronchus, melanoma, Hodgkin\'s lymphoma, prostate, and urinary cancers. We also defined, estimated, and illustrated indices for other cancer diagnoses from the vantage point of breast cancer in pre, during, and post-COVID-19 eras. The angle vectors post the COVID-19 were 72% less than pre-pandemic and 28% less than during the pandemic. The movement of cancer vectors was dynamic between these eras, and movement greatly differed by type of cancer. A trend chart of cervical cancer showed statistical anomalies in the years 2019 and 2021. Based on our findings, a few future research directions are pointed out.

To identify U.S. lead exposure risk hotspots, we expanded upon geospatial statistical methods from a published Michigan case study. The evaluation of identified hotspots using five lead indices, based on housing age and sociodemographic data, showed moderate-to-substantial agreement with state-identified higher-risk locations from nine public health department reports (45-78%) and with hotspots of children\'s blood lead data from Michigan and Ohio (e.g., Cohen\'s kappa scores of 0.49-0.63). Applying geospatial cluster analysis and 80th-100th percentile methods to the lead indices, the number of U.S. census tracts ranged from ∼8% (intersection of indices) to ∼41% (combination of indices). Analyses of the number of children <6 years old living in those census tracts revealed the states (e.g., Illinois, Michigan, New Jersey, New York, Ohio, Pennsylvania, Massachusetts, California, Texas) and counties with highest potential lead exposure risk. Results support use of available lead indices as surrogates to identify locations in the absence of consistent, complete blood lead level (BLL) data across the United States. Ground-truthing with local knowledge, additional BLL data, and environmental data is needed to improve identification and analysis of lead exposure and BLL hotspots for interventions. While the science evolves, these screening results can inform \"deeper dive\" analyses for targeting lead actions.

During the initial diagnosis of urgent medical conditions, which include acute infectious diseases, it is important to assess the severity of the patient\'s clinical state as quickly as possible. Unlike individual biochemical or physiological indicators, derived indices make it possible to better characterize a complex syndrome as a set of symptoms, and therefore quickly take a set of adequate measures. Recently, we reported on novel diagnostic indices containing butyrylcholinesterase (BChE) activity, which is decreased in COVID-19 patients. Also, in these patients, the secretion of von Willebrand factor (vWF) increases, which leads to thrombosis in the microvascular bed. The objective of this study was the determination of the concentration and activity of vWF in patients with COVID-19, and the search for new diagnostic indices. One of the main objectives was to compare the prognostic values of some individual and newly derived indices. Patients with COVID-19 were retrospectively divided into two groups: survivors (n = 77) and deceased (n = 24). According to clinical symptoms and computed tomography (CT) results, the course of disease was predominantly moderate in severity. The first blood sample (first point) was taken upon admission to the hospital, the second sample (second point)-within 4-6 days after admission. Along with the standard spectrum of biochemical indicators, BChE activity (BChEa or BChEb for acetylthiocholin or butyrylthiocholin, respectively), malondialdehyde (MDA), and vWF analysis (its antigen level, AGFW, and its activity, ActWF) were determined and new diagnostic indices were derived. The pooled sensitivity, specificity, and area under the receiver operating curve (AUC), as well as Likelihood ratio (LR) and Odds ratio (OR) were calculated. The level of vWF antigen in the deceased group was 1.5-fold higher than the level in the group of survivors. Indices that include vWF antigen levels are superior to indices using vWF activity. It was found that the index [Urea] × [AGWF] × 1000/(BChEb × [ALB]) had the best discriminatory power to predict COVID-19 mortality (AUC = 0.91 [0.83, 1.00], p < 0.0001; OR = 72.0 [7.5, 689], p = 0.0002). In addition, [Urea] × 1000/(BChEb × [ALB]) was a good predictor of mortality (AUC = 0.95 [0.89, 1.00], p < 0.0001; OR = 31.5 [3.4, 293], p = 0.0024). The index [Urea] × [AGWF] × 1000/(BChEb × [ALB]) was the best predictor of mortality associated with COVID-19 infection, followed by [Urea] × 1000/(BChEb × [ALB]). After validation in a subsequent cohort, these two indices could be recommended for diagnostic laboratories.

Composite indices have been widely used to rank the environmental performance of nations. Such environmental indices can be useful in communicating complex information as a single value and have the potential to generate political and media awareness of environmental issues. However, poorly constructed, or poorly communicated indices, can hinder efforts to identify environmental failings, and there are considerable differences in rank among existing environmental indices. Here, we provide a review of the conceptual frameworks and methodological choices used for existing environmental indices to enhance our understanding of their accuracy and applicability. In the present study, we review existing global indices according to their conceptual framework (objectives of the index and set of indicators included) and methodological choices made in their construction (e.g., weighting and aggregation). We examine how differences in conceptual frameworks and methodology may yield a more, or less, optimistic view of a country\'s environment. Our results indicate that (1) multidimensional environmental indices with indicators related to human health and welfare or policy are positively correlated; (2) environment-only indices are positively correlated with one another or are not correlated at all; (3) multidimensional indices and environment-only indices are negatively correlated with each other or are not correlated at all. This indicates that the conceptual frameworks and indicators included may influence a country\'s rank among different environmental indices. Our results highlight that, when choosing an existing environmental index-or developing a new one-it is important to assess whether the conceptual framework (and associated indicators) and methodological choices are appropriate for the phenomenon being measured and reported on. This is important because the inclusion of confounding indicators in environmental indices may provide a misleading view of the quality of a country\'s environment.

We investigated the predictive capacity of six prognostic indices [Karnofsky Performance Status (KPS), Hematopoietic Cell Transplant-Specific Comorbidity Index (HCT-CI), Disease Risk Index (DRI), European Bone Marrow Transplantation (EBMT) and Revised Pre-Transplantation Assessment of Mortality (rPAM) Scores and Endothelial Activation and Stress Index (EASIX)] in 205 adults undergoing post-transplant cyclophosphamide (PTCy)-based allo-HCT. KPS, HCT-CI, DRI and EASIX grouped patients into higher and lower risk strata. KPS and EASIX maintained appropriate discrimination for OS prediction across the first 2 years after allo-HCT [receiver operating characteristic curve (area under the curve (AUC) > 55 %)]. The discriminative capacity of DRI and HCT-CI increased during the post-transplant period, with a peak of prediction at 2 years (AUC of 61.1 % and 61.8 %). The maximum rPAM discriminative capacity was at 1 year (1-year AUC of 58.2 %). The predictive capacity of the EBMT score was not demonstrated. This study validates the discrimination capacity for OS prediction of KPS, HCT-CI, DRI and EASIX in PTCy-based allo-HCT.