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.

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.

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.

Natural anthocyanin indicator films with an excellent pH response enable the visual assessment of meat freshness. In this investigation, chitosan was initially employed as a colorimetric enhancer, leading to the development of a pH-sensitive indicator film that was enhanced in colorimetry. The characteristics of this indicator film were thoroughly analyzed, and the mechanism responsible for the increased sensitivity of anthocyanin within the chitosan matrix, as indicated by the color response, was elucidated. The recrystallization of chitosan impeded the hydration of AH+ as the pH increased from 6.0 to 8.0, leading to distinct color changes. Moreover, the application of this indicator film was extended to the monitoring of mutton meat freshness. It facilitated the differentiation of mutton meat into three distinct stages, namely, fresh, sub-fresh, and spoiled, based on alterations in color. Additionally, a robust positive correlation was established between the color difference value of the indicator film and the total volatile basic nitrogen and bacterial count of the mutton meat, enabling quantitative analysis. The present study, therefore, demonstrated a novel function of chitosan, i.e., the enhancement of the color of anthocyanin, which could be useful in designing and fabricating indicator films with a high color response.

Herbicides play a pivotal role in paddy rice cultivation by effectively controlling weeds, thus ensuring optimal resource utilisation and higher crop yields, making them indispensable for efficient rice production systems. However, herbicide applications could be related to potential environmental impacts such as water contamination and harm to non-target species, requiring special attention in their management to ensure the long-term sustainability of rice farming practices. The development and utilisation of robust risk assessment indicators for pesticides are essential tools in evaluating and mitigating potential environmental and human health hazards associated with pesticide use in agricultural practices. The Environmental Potential Risk Indicator for Pesticides (EPRIP) is not suitable for rice paddy cultivation due to its limitations in accurately assessing pesticide risk in this specific agricultural context. This is primarily attributed to the unique hydrological characteristics and ecosystem dynamics of paddy fields, which significantly differ from other agricultural systems. To address this issue and to enhance the accuracy of pesticide risk assessment in rice paddy fields, EPRIP has been improved and validated in two agricultural seasons. A synergistic approach involving field experiments and enhanced EPRIP model simulations was employed to assess the risk associated with the application of two herbicides in Italian paddy rice systems. The observed and model-predicted surface water (SW) concentrations exhibited a close alignment, though an overestimation was observed for groundwater (GW). In general, the estimated Risk Points (1 for SW and 4 for GW) were largely in accord with those derived from the field experiments (1 for SW and 3 for GW), suggesting that the refined EPRIP model holds promise for conducting reliable risk assessments following herbicide applications in such contexts.

This scoping review is a further step to build up the Mental Health Surveillance System for Germany. It summarizes and analyzes indicators used or described in Organization for Economic Co-operation and Development (OECD) countries for public mental health monitoring in children and adolescents aged 0-18 years.
We searched PubMed-MEDLINE, PsycINFO, Cochrane Databases, and Google Scholar from 2000 to September 2022. The search used five general keyword categories: 1) \"indicators/monitoring/surveillance\" at the population level, 2) \"mental/psychological,\" 3) \"health/disorders,\" 4) \"children and adolescents,\" and 5) 38 OECD countries. The search was complemented with an extensive grey literature search, including OECD public health institutions and an internet search using Google. A predefined set of inclusion and exclusion criteria was applied.
Over 15,500 articles and documents were screened (scientific search N = 10,539, grey literature search more than 5,000). More than 700 articles and documents have been full-text assessed, with 382 being ultimately included. Out of 7,477 indicators extracted, an initial set of 6,426 indicators met our inclusion criteria for indicators. After consolidating duplicates and similar content, this initial set was categorized into 19 topics, resulting in a final set of 210 different indicators. The analysis highlighted an increasing interest in the topic since 2008, but indicators for the younger age, particularly those aged 0 to 2 years, were less readily available.
Our research provides a comprehensive understanding of the current state of mental health indicators for children and adolescents, identifying both (1) indicators of public mental health noted in a previous scoping review on adults and (2) new indicators specific to this age group. These findings contribute to the development of effective public health surveillance strategies for children and adolescents and inform future research in this field.