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A new mental health disorder, prolonged grief disorder (PGD), will be included in the 11th edition of the International Classification of Diseases (ICD-11). We provide a brief overview of the historical conceptualizations of disordered grief and the previous research efforts to assess and define this condition. We describe the new ICD-11 PGD symptom criteria and how they are conceptualized in terms of the World Health Organization's call for improved clinical utility. Finally, we review the research evidence for the clinical utility of the new ICD-11 PGD symptom structure and usability in the international arena.

【Background and objective】 Hydrological process is a complex phenomenon and affected by many biological and physical processes. Its modelling in water resource management and decision-making in watershed or catchment is often based on conceptual models where the complicated processes that directly or indirectly modulate water flow in different components are highly simplified. Such models are mathematically simple and have been widely used in both practical and scientific communities. Different conceptual hydrological models are available, but how they compare with each other for simulating hydrological processes in watersheds is not well studied. The purpose of this paper is to fill this gap. 【Method】 We compared 45 conceptual hydrological models for their application in the Lijiang River Basin. For each model, we simulated daily runoff in the basin from 2008 to 2016 using the same dataset, and compared and analyzed their performance in terms of model structure, calculated evapotranspiration and runoff, from which we identified main factors which affect model applicability and accuracy most. 【Result】 MODHYDROLOG was the model that worked best for simulating hydrological flow in the basin, followed by GR4J, IHACRES and Hillslope models. Model which was least accurate for simulating runoff did not work well for modelling real evapotranspiration and other runoff components neither. An accurate evapotranspiration calculation was not decisive for accurate runoff simulation, and dividing the runoff into different components was the key factor in applicability and accuracy of the models. We also found that the generalized semi-distributed model not only simplified the operation process of the model, but also affected the simulation accuracy. 【Conclusion】 Among the 45 conceptual models we compared, MODHYDROLOG, GR4J, IHACRES and Hillslope models worked best for simulating hydrological processes in the Lijiang River Basin and similar basins.

Water pollution has become one of the most serious issues threatening water environments, water as a resource and human health. The most urgent and effective measures rely on dynamic and accurate water quality monitoring on a large scale. Due to their temporal and spatial advantages, remote sensing technologies have been widely used to retrieve water quality data. With the development of hyper-spectral sensors, unmanned aerial vehicles (UAV) and artificial intelligence, there has been significant advancement in remotely sensed water quality retrieval owing to various data availabilities and retrieval methodologies. This article presents the application of remote sensing for water quality retrieval, and mainly discusses the research progress in terms of data sources and retrieval modes. In particular, we summarize some retrieval algorithms for several specific water quality variables, including total suspended matter (TSM), chlorophyll-a (Chl–a), colored dissolved organic matter (CDOM), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP). We also discuss the significant challenges to atmospheric correction, remotely sensed data resolution, and retrieval model applicability in the domains of spatial, temporal and water complexity. Finally, we propose possible solutions to these challenges. The review can provide detailed references for future development and research in water quality retrieval.

This study investigates the applicability of the fundamental bibliometric laws-Lotka’s Law, Bradford’s Law, and Zipf’s Law-within the domain of Gastritis research. Utilising data from the Web of Science Core Collection, a total of 19,856 records on Gastritis research were analysed using various scientometric tools and statistical tests.Lotka’s Law, which models author productivity, was tested through the Kolmogorov-Smirnov (K-S) test and the Chi-Square test which confirmed significant discrepancies between the observed and expected distributions of publications. Bradford’s Law, applied to journal productivity and article scattering, revealed a deviation from the anticipated 1:α:α² ratio. Zipf’s Law was validated through an analysis of the most frequently used terms and the results demonstrated an inverse relationship between rank and frequency, supporting the applicability of Zipf’s Law to the Gastritis research corpus.

Background The effectiveness of complex interventions, as well as their success in reaching relevant populations, is critically influenced by their implementation in a given context. Current conceptual frameworks often fail to address context and implementation in an integrated way and, where addressed, they tend to focus on organisational context and are mostly concerned with specific health fields. Our objective was to develop a framework to facilitate the structured and comprehensive conceptualisation and assessment of context and implementation of complex interventions. Methods The Context and Implementation of Complex Interventions (CICI) framework was developed in an iterative manner and underwent extensive application. An initial framework based on a scoping review was tested in rapid assessments, revealing inconsistencies with respect to the underlying concepts. Thus, pragmatic utility concept analysis was undertaken to advance the concepts of context and implementation. Based on these findings, the framework was revised and applied in several systematic reviews, one health technology assessment (HTA) and one applicability assessment of very different complex interventions. Lessons learnt from these applications and from peer review were incorporated, resulting in the CICI framework. Results The CICI framework comprises three dimensions—context, implementation and setting—which interact with one another and with the intervention dimension. Context comprises seven domains (i.e., geographical, epidemiological, socio-cultural, socio-economic, ethical, legal, political); implementation consists of five domains (i.e., implementation theory, process, strategies, agents and outcomes); setting refers to the specific physical location, in which the intervention is put into practise. The intervention and the way it is implemented in a given setting and context can occur on a micro, meso and macro level. Tools to operationalise the framework comprise a checklist, data extraction tools for qualitative and quantitative reviews and a consultation guide for applicability assessments. Conclusions The CICI framework addresses and graphically presents context, implementation and setting in an integrated way. It aims at simplifying and structuring complexity in order to advance our understanding of whether and how interventions work. The framework can be applied in systematic reviews and HTA as well as primary research and facilitate communication among teams of researchers and with various stakeholders.

This paper explores the enhancement of pharmacological outcomes through the combined use of melatonin and polydeoxyribonucleotide (PDRN), hypothesizing that their simultaneous application might surpass the effectiveness of individual use. Melatonin is a hormone that modulates sleep, oxidative stress and inflammation, and exerts analgesic and anti-inflammatory effects. Conversely, PDRN is well-known for its significant contributions to tissue regeneration and its role in promoting angiogenesis. This article details the pharmacological effects and mechanisms of each compound, suggesting that their integration could amplify their individual benefits, particularly in the realms of wound healing and various medical applications. This paper seeks to provide a comprehensive analysis of the interactions between melatonin and PDRN by reviewing existing studies, thereby paving the way for novel therapeutic strategies. It emphasizes the need for further clinical trials and research to optimize the use of this combination for the improved treatment of diverse cellular or tissue conditions. In conclusion, further research is needed to optimize combination therapies involving melatonin and PDRN, with the goal of confirming their enhanced benefits when used together. In conclusion, further research is necessary to optimize combination therapies involving melatonin and PDRN to confirm their enhanced benefits when used in conjunction. This review emphasizes the importance of exploring their potential synergistic effects and developing effective therapeutic strategies across various medical disciplines.

In drug development, early assessments of pharmacokinetic and toxic properties are important stepping stones to avoid costly and unnecessary failures. Considerable progress has recently been made in the development of computer-based ( ) models to estimate such properties. Nonetheless, such models can be further improved in terms of their ability to make predictions more rapidly, easily, and with greater reliability. To address this issue, we have used our vNN method to develop 15 absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction models. These models quickly assess some of the most important properties of potential drug candidates, including their cytotoxicity, mutagenicity, cardiotoxicity, drug-drug interactions, microsomal stability, and likelihood of causing drug-induced liver injury. Here we summarize the ability of each of these models to predict such properties and discuss their overall performance. All of these ADMET models are publically available on our website (https://vnnadmet.bhsai.org/), which also offers the capability of using the vNN method to customize and build new models.

One of the OECD principles for model validation requires defining the Applicability Domain (AD) for the QSAR models. This is important since the reliable predictions are generally limited to query chemicals structurally similar to the training compounds used to build the model. Therefore, characterization of interpolation space is significant in defining the AD and in this study some existing descriptor-based approaches performing this task are discussed and compared by implementing them on existing validated datasets from the literature. Algorithms adopted by different approaches allow defining the interpolation space in several ways, while defined thresholds contribute significantly to the extrapolations. For each dataset and approach implemented for this study, the comparison analysis was carried out by considering the model statistics and relative position of test set with respect to the training space.

The global climate is noticeably warming, and drought occurs frequently. Therefore, choosing a suitable index for drought monitoring is particularly important. The standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) are commonly used indicators in drought monitoring. The SPEI takes temperature into account, but the SPI does not. In the context of global warming, what are their differences and applicability in regional drought monitoring? In this study, after calculating the SPI and SPEI at 1-, 3-, 6-, and 12-month timescales at 102 meteorological stations in Inner Mongolia from 1981 to 2018, we compared and analyzed the performances of the SPI and SPEI in drought monitoring from temporal and spatial variations, and the consistency and applicability of the SPI and SPEI were also discussed. The results showed that (1) with increasing timescale, the temporal variations in the SPI and SPEI were increasingly consistent, but there were still slight differences in the fluctuation value and continuity; (2) due to the difference in time series, the drought characteristics identified by the SPI and SPEI were quite different in space at various timescales, and with the increase in timescale, the spatial distributions of the drought trends in Inner Mongolia were basically consistent, except in Alxa; (3) at the shortest timescale, the difference between the SPI and SPEI was the largest, and the drought reflected by the SPI and SPEI may be consistent at long timescales; and (4) compared with typical drought events and vegetation indexes, the SPEI may be more suitable than the SPI for drought monitoring in Inner Mongolia. It should be noted that the adaptability of the SPI and SPEI may be different in different periods and regions, which remains to be analyzed in the future.

Computational prediction of the interaction between drugs and targets is a standing challenge in the field of drug discovery. A number of rather accurate predictions were reported for various binary drug–target benchmark datasets. However, a notable drawback of a binary representation of interaction data is that missing endpoints for non-interacting drug–target pairs are not differentiated from inactive cases, and that predicted levels of activity depend on pre-defined binarization thresholds. In this paper, we present a method called SimBoost that predicts continuous (non-binary) values of binding affinities of compounds and proteins and thus incorporates the whole interaction spectrum from true negative to true positive interactions. Additionally, we propose a version of the method called SimBoostQuant which computes a prediction interval in order to assess the confidence of the predicted affinity, thus defining the Applicability Domain metrics explicitly. We evaluate SimBoost and SimBoostQuant on two established drug–target interaction benchmark datasets and one new dataset that we propose to use as a benchmark for read-across cheminformatics applications. We demonstrate that our methods outperform the previously reported models across the studied datasets.