The adoption of standardized metrics and indicators of antimicrobial use (AMU) in the food animal industry is essential for the success of programs aimed at promoting the responsible and judicious use of antimicrobials in this activity. The objective of this study was to introduce the use of standardized AMU metrics and indicators to quantify the use of florfenicol and oxytetracycline in the Chilean salmon industry, and in this way evaluate the feasibility of their use given the type of health and production information currently managed by the National Fisheries and Aquaculture Service (SERNAPESCA), the Chilean agency responsible for regulating aquaculture in Chile. The data available from SERNAPESCA allowed the construction and evaluation of the most data-demanding AMU metrics and indicators. Consequently, the use of florfenicol and oxytetracycline administered by oral and parenteral routes was quantified using the treatment incidence based on both animal defined daily dose (TIDDDvet) and animal used daily dose (TIUDDA). To that end, the study included information from 1320 closed production cycles from farms rearing Atlantic salmon, coho salmon and rainbow trout that were active between January 2017 and December 2021. By applying standardized AMU metrics and indicators, we were able to determine that the median of TIDDDvet for florfenicol was 75.1 (80 % range, 20.0-158.0) DDDvet per ton-year at risk for oral procedures and 0.36 (80 % range, 0.07-1.19) DDDvet per ton-year at risk for parenteral procedures. For oxytetracycline, the median TIDDDvet was 3.09 (80 % range, 0.74-42.8) and 0.47 (80 % range, 0.09-1.68) DDDvet per ton-year at risk for oral and parenteral procedures, respectively. The median TIUDDA for treatments with florfenicol was 45.6 (80 % range, 10.9-96.5) UDDA per ton-year at risk for oral treatments and 0.28 (80 % range, 0.05-0.80) UDDA per ton-year at risk for parenteral treatments. For oxytetracycline, the median TIUDDA was 2.63 (80 % range, 0.61-28.2) UDDA per ton-year at risk for oral treatments and 0.41 (80 % range, 0.08-1.29) UDDA per ton-year at risk for parenteral treatments. This study demonstrates that it is feasible to move from traditional AMU metrics and indicators to standardized ones in the Chilean salmon industry. This is possible because the competent authority requires salmon farms to report detailed health and production information at a high frequency. The use of standardized AMU metrics and indicators can help the authority to have a more comprehensive view of the antimicrobial use in the Chilean salmon industry.

The antiviral and antibacterial properties of ozone gas have been utilised for air and on-site disinfection. However, excess ozone gas exposure is toxic to humans, necessitating the monitoring of exposure levels. While this has been achieved using ozone indicators, determining exposure levels at intermediate time points during exposure remains challenging owing to the current ones being capable of measuring exposure only at endpoints. Shifts in the endpoint are also difficult as the coverage ranges of the indicators are dependent on a single colour dye. The indicator that covers a wide range is desirable to treat various pathogenic microorganisms. The aim of this study was to develop a two-colour ozone indicator with a broad coverage range that does not compromise the performance of the individual dyes. Exposure levels were indicated by visible changes in colour density and phase, aiding the measurement of exposure at the endpoints and during the exposure period, thereby maintaining better safety profiles for exposed individuals. Additionally, a theoretical approach for the development of two-colour indicators has been described.

Microbial environmental DNA and RNA (collectively \'eNA\') originate from a diverse and abundant array of microbes present in environmental samples. These eNA signals, largely representing whole organisms, serve as a powerful complement to signals derived from fragments or remnants of larger organisms. Integrating microbial data into the toolbox of ecosystem assessments and biotic indices therefore has the potential to transform how we use eNA data to understand biodiversity dynamics and ecosystem functions, and to inform the next generation of environmental monitoring. Incorporating holobiont and Tree of Life approaches into eNA analyses offers further holistic insight into the range of ecological interactions between microbes and other organisms, paving the way for advancing our understanding of, and ultimately manipulating ecosystem properties pertinent to environmental management, conservation, wildlife health, and food production.

BACKGROUND: The association between serological indexes and occurrence of complications in patients with autoimmune gastritis (AIG) remains unclear.
METHODS: 91 patients with AIG were recruited and their clinical information were collected. The differences between serological indexes and complications of AIG were analyzed. And potential biomarker for early prediction and diagnosis of AIG with complications was explored.
RESULTS: AIG patients in our study was 58.12 ± 11.68 years old, containing 31 males and 60 females. G17 was elevated in 49 of 52; PGI/II decreased in 43/49; GPA positive in 48/61; Anemia presented 28 in 80; Vitamin B12 deficiency occurred 23 in 58. Neuroendocrine tumor (NET) was the most common complication in AIG patients, accounting for 27/91. The second was polyps, making up for 14/91. There is also 9/91 of gastric mucosa neoplasia happened in AIG. No significant difference of G7, PGI, PGII, PGI/II and VB12 in AIG was found in different gastric mucosal lesions (P > 0.05). However, AIG patients with TPOAb positive had a higher risk in the occurrence of NET simultaneously (P = 0.0212). Those AIG with NET patients exhibited a significantly higher TPOAb level (P = 0.0078). ROC curve suggested that TPOAb can predict the existence of NET in AIG (AUC = 0.7410, P < 0.05).
CONCLUSIONS: We found that TPOAb can serve as a predictive biomarker of NET in AIG. This accessible test is helpful for endoscopy specialists to pay attention to gastric mucosal lesions in TPOAb-positive AIG patients, improving early diagnosis and intervention of comorbidities ability.

Artificial intelligence (AI) is a rapidly evolving field that can impel research in communicable diseases with respect to climate projections, ecological indicators and environmental impact, at the same time revealing new, previously overlooked events. A number of zoonotic and vector-borne diseases already show signs of expanding their northern geographical ranges and appropriate risk assessment and decision support are urgently needed. The deployment of AI-enabled monitoring systems tracking animal populations and environmental changes is of immense potential in the study of transmission under different climate scenarios. In addition, AI\'s capability to identify new treatments should not only accelerate drug and vaccine discovery but also help predicting their effectiveness, while its contribution to genetic pathogen speciation would assist the evaluation of spillover risks with regard to viral infections from animals to human. Close collaboration between AI experts, epidemiologists and other stakeholders is not only crucial for responding to challenges interconnected with a variety of variables effectively, but also necessary to warrant responsible AI use. Despite its wider successful implementation in many fields, AI should be seen as a complement to, rather than a replacement of, traditional public health measures.

Quality indicators are essential for hospital management, clinical decision-making, and patient care. In today\'s technologically advanced era, it is increasingly critical to derive effective statistical data analysis from intricate datasets. However, the administrative department within our hospital often fails to prioritize information needs due to limited resources. Consequently, the collection of indicator data continues to rely on paper-based operations.To tackle common issues in collecting indicator data, such as incomplete or inaccurate data, time-consuming aggregation and statistical analysis, and a lack of timeliness, we have implemented appropriate Business Intelligence (BI) tools. We provide educational training to empower users to create visual templates for indicators that are tailored to their specific requirements. This approach saves time, reduces data duplication, and minimizes writing errors. It enhances supervisors\' understanding of the current situation and future prospects in terms of data analysis and timeliness. Ultimately, this initiative improves healthcare quality and management effectiveness.

UNASSIGNED: Leishmaniases are neglected diseases transmitted by sand flies. They disproportionately affect vulnerable groups globally. Understanding the relationship between climate and disease transmission allows the development of relevant decision-support tools for public health policy and surveillance. The aim of this modelling study was to develop an indicator that tracks climatic suitability for Leishmania infantum transmission in Europe at the subnational level.
UNASSIGNED: Historical records of sand fly vectors, human leishmaniasis, bioclimatic indicators, and environmental variables were integrated in a machine learning framework (XGBoost) to predict suitability in two past periods (2001-2010 and 2011-2020). We further assessed if predictions were associated with human and animal disease data from selected countries (France, Greece, Italy, Portugal, and Spain).
UNASSIGNED: An increase in the number of climatically suitable regions for leishmaniasis was detected, especially in southern and eastern countries, coupled with a northward expansion towards central Europe. The final model had excellent predictive ability (AUC = 0.970 [0.947-0.993]), and the suitability predictions were positively associated with human leishmaniasis incidence and canine seroprevalence for Leishmania.
UNASSIGNED: This study demonstrates how key epidemiological data can be combined with open-source climatic and environmental information to develop an indicator that effectively tracks spatiotemporal changes in climatic suitability and disease risk. The positive association between the model predictions and human disease incidence demonstrates that this indicator could help target leishmaniasis surveillance to transmission hotspots.
UNASSIGNED: European Union Horizon Europe Research and Innovation Programme (European Climate-Health Cluster), United Kingdom Research and Innovation.

UNASSIGNED: Malignant pleural effusion (MPE) is prevalent among cancer patients, indicating pleural metastasis and predicting poor prognosis. However, accurately identifying MPE in clinical settings is challenging. The aim of this study was to establish an innovative nomogram-derived model based on clinical indicators and serum metal ion levels to identify MPE.
UNASSIGNED: From July 2020 to May 2022, 428 patients diagnosed with pleural effusion (PE) were consecutively recruited. Comprehensive demographic details, clinical symptoms, imaging data, pathological information, and laboratory results, including serum metal ion levels, were systematically collected. The nomogram was created by incorporating the most significant predictors identified through LASSO and multivariate logistic regression analysis. The predictors were assigned weighted points based on their respective regression coefficients, allowing for the calculation of a total score that corresponds to the probability of MPE. Internal validation using bootstrapping techniques assessed the nomogram\'s performance, including calibration, discrimination, and clinical applicability.
UNASSIGNED: Seven key variables were identified using LASSO regression and multiple regression analysis, including dyspnea, fever, X-ray/CT compatible with malignancy, pleural carcinoembryonic antigen(pCEA), serum neuron-specific enolase(sNSE), serum carcinoembryonic antigen(sCEA), and pleural lactate dehydrogenase(pLDH). Internal validation underscored the superior performance of our model (AUC=0.940). Decision curve analysis (DCA) analysis demonstrated substantial net benefit across a probability threshold range > 1%. Additionally, serum calcium and copper levels were significantly higher, while serum zinc levels were significantly lower in MPE patients compared to benign pleural effusion (BPE) patients.
UNASSIGNED: This study effectively developed a user-friendly and reliable MPE identification model incorporating seven markers, aiding in the classification of PE subtypes in clinical settings. Furthermore, our study highlights the clinical value of serum metal ions in distinguishing malignant pleural effusion from BPE. This significant advancement provides essential tools for physicians to accurately diagnose and treat patients with MPE.

Cetaceans harbor multiple epibionts on their external surface, and these attach to particular microhabitats. Understanding what drives the selection of attachment sites is relevant for refining the use of epibionts as indicators of their hosts. We report on about 100 females of the mesoparasitic copepod Pennella balaenoptera attached to a dead Cuvier\'s beaked whale Ziphius cavirostris stranded in Tunisia (western Mediterranean); the first report of P. balaenoptera in this country. The copepods were exclusively attached to numerous incisive, likely anthropogenic, wounds found on the host\'s skin. This finding suggests that newly recruited females may actively seek skin areas where physical penetration is facilitated; a factor that may help explain patterns of microhabitat selection by Pennella spp., and perhaps other pennellids, on their hosts. The estimated age of parasitization by P. balaenoptera (supported by age estimations of the co-occurring epibiotic barnacle Conchoderma virgatum) also suggests that the cetacean host likely survived these injuries, at least initially, and the presumed cause of death was starvation due to entanglement in a fishing net.

Anti-Müllerian hormone (AMH) serves as an indirect marker for predicting primordial follicles that are representative of ovarian reserve. In this study the possibility of using AMH and age to predict the ovarian reserve in domestic cats. Ovaries and blood were collected from 30 cats undergoing routine ovariohysterectomy. The animals were divided into three age groups: prepubertal (<4 mo, n = 10), adult (1-5 y, n = 10), and senior (>5 y, n = 10). Blood was collected at surgery for serum AMH measurements using the AMH Gen II ELISA kit. The intra-assay coefficient of variation (CV) and inter-assay CV were 3.56 % and 7.68 %, respectively. One side of the ovary was processed to determine AMH localization using immunohistochemistry and for a histological count of follicles, which is the gold standard. The expression of AMH protein was quantified from the contralateral ovary by Western blot analysis. Primordial follicles exhibited the most pronounced inverse relationship with age (rho = -0.779, P < 0.05), followed by a positive association with serum AMH concentration (rho = 0.490, P < 0.05), indicating that both age and AMH are potential markers indicative of primordial follicles. Furthermore, secondary (rho = 0.651, P < 0.05) and small antral follicles (rho = 0.648, P < 0.05) were identified as the major sources of circulating AMH, as indicated by the stronger correlation with serum AMH concentrations compared with primary follicles. However, there was no significant correlation between the expression of AMH protein and other factors, including age, primordial follicles, primary follicles, secondary follicles, small antral follicles, and serum AMH concentration. A model for predicting primordial follicle number using serum AMH concentration (AIC = 672.66, P < 0.05) and age (AIC = 668.93, P < 0.05) was established. In conclusion, both serum AMH concentration and age may serve as comparable markers of ovarian reserve in domestic cats. Moreover, AMH is particularly useful in situations where age information is not available.