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.

The study covered a small, shallow lake, intensively used for recreation (sailing, tourist services and port infrastructure). This study aimed to determine the spatial differentiation of bottom sediments and the potential for phosphorus release in five zones, differing mainly in the type of recreation, depth, direct catchment management, shoreline management and macrophyte presence. The results were used to propose protective and restoration measures to improve the water quality of the studied lake. The innovation in the study was the detailed analysis of bottom sediments, which can be a significant source of pollution besides the external load from the catchment and tourist pressure, in the planned management of this ecosystem. Examination of the physicochemical properties of the bottom sediments showed a clear variation in both composition and potential for internal phosphorus loading. The sediments from the profundal zone, where the most boating activity was observed, together with the sediments from the shallow zone where the boats dock (mooring zone), had the highest potential to supply phosphorus to the bottom waters. This fact was demonstrated by the highest total phosphorus (TP) concentrations in sediments (up to 1.32 mgPg-1 DW) and the content of the most mobile fractions (up to 33%). The other zones associated with the marina, fuel zone, tributary and canal were not significant sources of phosphorus to the ecosystem. Based on the above results, a restoration method involving the removal of bottom sediments from the bottom zone was proposed, supported, of course, by protective measures in the catchment (maintaining a buffer zone around the lake and limiting the inflow of pollutants with tributary waters). The proposed measures with sustainable tourist pressure should improve water quality and thus contribute to protecting this valuable natural landscape.

Cell therapy has proven to be a promising treatment for a range of neurological disorders, including Parkinson Disease, drug-resistant epilepsy, and stroke, by restoring function after brain damage. Nevertheless, evaluating the true effectiveness of these therapeutic interventions requires a deep understanding of the functional integration of grafted cells into existing neural networks. This review explores a powerful arsenal of molecular techniques revolutionizing our ability to unveil functional integration of grafted cells within the host brain. From precise manipulation of neuronal activity to pinpoint the functional contribution of transplanted cells by using opto- and chemo-genetics, to real-time monitoring of neuronal dynamics shedding light on functional connectivity within the reconstructed circuits by using genetically encoded (calcium) indicators in vivo. Finally, structural reconstruction and mapping communication pathways between grafted and host neurons can be achieved by monosynaptic tracing with viral vectors. The cutting-edge toolbox presented here holds immense promise for elucidating the impact of cell therapy on neural circuitry and guiding the development of more effective treatments for neurological disorders.

OBJECTIVE: Health development is a key element of national development. The goal of improving health development at the societal level will be readily achieved if it is directed from the smallest social unit, namely the family. This was the goal of the Healthy Indonesia Program with a Family Approach. The objective of the study was to analyze variables of family health indicators across all provinces in Indonesia to identify provincial disparities based on the status of healthy families.
METHODS: This study examined secondary data for 2021 from the Indonesia Health Profile, provided by the Ministry of Health of the Republic of Indonesia, and from the 2021 welfare statistics by Statistics Indonesia (BPS). From these sources, we identified 10 variables for analysis using the k-means method, a non-hierarchical method of cluster analysis.
RESULTS: The results of the cluster analysis of healthy family indicators yielded 5 clusters. In general, cluster 1 (Papua and West Papua Provinces) had the lowest average achievements for healthy family indicators, while cluster 5 (Jakarta Province) had the highest indicator scores.
CONCLUSIONS: In Indonesia, disparities in healthy family indicators persist. Nutrition, maternal health, and child health are among the indicators that require government attention.

Promotion of public health is one of the most important benefits of ecosystems. Nevertheless, the relationship between ecosystems and social health\' needs is not well understood. Therefore, a study was done to investigate the potential of natural (forests and rangelands) and artificial (urban parks and gardens) ecosystems in ensuring the five dimensions of public health (i.e. physical, mental, spiritual, social and environmental) in the social systems (urban and rural societies). Therefore, 47 health indicators were used in order to relate different ecosystems and social\' needs to five dimensions of public health through questionnaire. The results indicated that natural ecosystems had the greatest potential in providing mental, spiritual and environmental health due to ecological characteristics of wilderness and aesthetic. The artificial ecosystems had the greatest potential in providing physical and social health due to their easy access. However, there was a match between social health\' needs and ecosystem potential in the rural areas. The study highlighted the need for promotion of ecological indicators related to mental health in urban areas by enhancing silence and aesthetic in artificial ecosystems. Presented framework can provide comprehensive information on the weaknesses and strengths of different ecosystems to promote public health based on social needs and fixing the weaknesses of artificial ecosystems in urban areas.