BACKGROUND: Abnormalities in resting-state functional brain activity have been detected in patients with temporal lobe epilepsy (TLE). The results of individual neuroimaging studies of TLE, however, are frequently inconsistent due to small and heterogeneous samples, analytical flexibility, and publication bias toward positive findings.
OBJECTIVE: To investigate the most consistent regions of resting-state functional brain activity abnormality in patients with TLE through a quantitative meta-analysis of published neuroimaging data.
METHODS: Meta-analysis.
METHODS: Exactly 1474 TLE patients (716 males and 758 females) from 31 studies on resting-state functional brain activity were included in this study.
UNASSIGNED: Studies utilizing 1.5 T or 3 T MR scanners were included for meta-analysis. Resting-state functional MRI using gradient echo-planar imaging, T1-weighted imaging.
RESULTS: PubMed, Web of Science, Chinese National Knowledge Infrastructure, and WanFang databases were searched to identify studies investigating amplitude of low-frequency fluctuation (ALFF), fractional ALFF (fALFF), and regional homogeneity (ReHo) at the whole-brain level between patients with TLE and healthy controls (HCs).
METHODS: Seed-based d Mapping with Permutation of Subject Images, standard randomization tests and meta-regression analysis were used. Results were significant if P < 0.05 with family-wise error corrected.
RESULTS: Patients with TLE displayed resting-state functional brain activity which was a significant increase in the right hippocampus, and significant decrease in the right angular gurus and right precuneus. Additionally, the meta-regression analysis demonstrated that age (P = 0.231), sex distribution (P = 0.376), and illness duration (P = 0.184), did not show significant associations with resting state functional brain activity in patients with TLE.
CONCLUSIONS: Common alteration patterns of spontaneous brain activity were identified in the right hippocampus and default-model network regions in patients with TLE. These findings may contribute to understanding of the underlying mechanism for potentially effective intervention of TLE.
METHODS:
UNASSIGNED: Stage 2.

Oral chemotherapy is commonly prescribed, and by using decision aids (DAs), clinicians can facilitate shared decision-making (SDM) to align treatment choices with patient goals and values. Although products exist commercially, little evidence informs the development of DAs targeting the unique challenges of oral chemotherapy. To address this gap in the literature, our objective was to review DAs developed for oral anticoagulation, DA use in oncology, and patient preference surveys to guide the development of DAs for oral chemotherapy. We focused on reviewing SDM, patient preferences, and specifically the development, efficacy, and patient experience of DAs in oral anticoagulation and oncologic conditions, ultimately including conclusions and data from 30 peer-reviewed publications in our viewpoint paper. We found that effective DAs in oral anticoagulation improved knowledge, lowered decisional conflict, increased adherence, and covered a broad range of SDM elements; however, limited information on patient experience was a common shortcoming. In oncology, DAs increased knowledge and aligned decisions with the values of the patients. Ineffective oncology DAs provided general, unclear, or overly optimistic information, while providing \"too much\" information was not shown to do harm. Patients preferred DAs that included pros and cons, side effects, questions to ask, and expected quality of life changes. In developing DAs for oral chemotherapy, patients should be included in the development process, and DA content should be specifically tailored to patient preferences. Providing DAs ahead of appointments proved more effective than during, and additional considerations included addressing barriers to efficacy. There is a need for evidence-based DAs to facilitate SDM for patients considering oral chemotherapy. Developers should use data from studies in oral anticoagulation, oncology, and preference surveys to optimize SDM.

The nitidulid beetle Carpophilus truncatus is rapidly becoming a major pest of nut crops around the world. This insect first infested Australian almonds in 2013 and has since escalated to be the preeminent insect pest for the industry. Data pertaining to C. truncatus distribution are scant, but without awareness of its origin, distribution, and ecological factors that influence distribution, efforts to understand and manage the insect as a pest are stymied. Here, we employ an integrative approach to gain a multifaceted understanding of the distribution of C. truncatus in Australia. Methods employed were (1) reviewing historical records in insect collections to establish the presence of C. truncatus prior to commercial almond horticulture, (2) field trapping of insects to establish presence in regions of interest, (3) laboratory trials to determine the thermal limits of the organism, and (4) correlative species distribution modelling to describe its current distribution. We find that C. truncatus is more widespread across Australia than was previously known, with historical records preceding commercial almond production in Australia by a century. The methods developed in this study can be applied elsewhere in the world where C. truncatus is an emerging pest, or to novel pest species as they arise with increasing frequency in a globalised and warming world.

In areas with limited field data, predictive habitat mapping is a valuable method for elucidating species-environment relationships and enhancing our knowledge of the spatial distribution and complexity of benthic habitats. Species distribution models (SDMs) can be an important tool to support in science-based ecosystem management. The availability of direct observations of mesophotic species, including gorgonians and black corals, during costly surveys is generally limited. Therefore, predicting the distribution of mesophotic species in relation to key physical parameters of the seafloor would help improving conservation strategies in existing and new Marine Protected Areas (MPAs). This study aims to assess the distribution of gorgonians and black corals off Linosa Island, in the Strait of Sicily, a biogeographic boundary area between the western and eastern Mediterranean. The volcanic island of Linosa represents a small, naturally preserved area, with very limited human pressure, hosting rich marine benthic biodiversity on its wide submarine portions. Distribution of the most common coral species off Linosa Island was modelled under an SDM framework, relying on direct observations collected during two research cruises in 2016 and 2017 and a series of terrain parameters acquired through geophysical techniques. We used the so-called \"ensemble of small models\" approach to calibrate SDMs, which achieved fair-to-excellent results (AUC >0.7). In addition to identifying depth as the primary factor influencing coral distribution, our study also highlighted ruggedness as a significant terrain variable. Specifically, the depth range of 110-230 m emerged as the critical parameter determining habitat suitability for all modelled species, also highlighting peculiar and specie-specific habitat requirements.

Ecological well-being performance (EWP), a novel concept in sustainable development research, diverges from traditional ecological efficiency in terms of perspectives, core content, and driving factors. However, research on EWP remains insufficiently comprehensive, particularly the corresponding theoretical and methodological investigations into driving pathways. To address this gap, this study develops an \"economy-environment-health\" framework, incorporating air pollution and associated health losses into the evaluation system, and employs a two-stage Super-NSBM and Window DEA model for reevaluating EWP. The study further investigates the primary pathways of EWP, focusing on environmental regulations, technological innovation, and structural adjustments through both quantitative and qualitative methods. Quantitative spatial econometric analysis reveals that factors such as market-driven environmental regulations, green invention patents, and industrial and energy consumption structures significantly enhance EWP. While examining the \"net effects\" contributions of individual variables using spatial econometric models, the fsQCA method is employed to identify four effective driving paths for EWP from a configurational perspective. These paths are 1) technological innovation and structural adjustment under environmental regulations with public participation; 2) a combination of environmental regulation, technological innovation, and structural adjustment; 3) structural adjustment with minimal influence from environmental regulations and technological innovation; and 4) structural adjustment directed by market-incentive environmental regulations.

The Amazon rainforest has approximately 23% of its sampled area dedicated to bats, making it one of the least sampled and most diverse regions for bats in Brazil. The lack of sampling results in a lack of knowledge regarding the accurate geographical distribution of bat species. This lack is referred to as the Wallacean shortfall, which should be addressed with primary data obtained from in situ collections. However, the use of Species Distribution Models (SDMs) can help alleviate this gap. The states of Pará and Acre are located in the Brazilian Amazon. So, our objective is to decrease the Wallacean shortfall concerning Amazonian bat species. To achieve this, we provide (i) a list of bat species sampled in the states of Pará and Acre in the last 5 years (2017 to 2022); (ii) the potential distribution of species considered as new occurrences for the region; and (iii) the potential distribution of species classified as Data Deficient (DD) and Near Threatened (NT) according to the IUCN classification. With 96 nights of collection and 129,600 m2h of mist netting, we obtained 75 bat species, with an estimated total of 94.78 species. Additionally, 21 species were considered as range extensions. The Brazilian Amazon region has a vast geographic expanse and few established research centers, resulting in a limited sampling of bats and other biological groups. Furthermore, we draw attention to the significant number of bat species with expanded geographical distributions, with 21 out of the 75 sampled species. This should be a reminder that primary biogeographic data is still necessary for the neotropical region.

The wild relatives of cultivated apples would be an ideal source of diversity for breeding new varieties, which could potentially grow in diverse habitats shaped by climate change. However, there is still a lack of knowledge about the potential distribution of these species. The aim of the presented work was the understand the impacts of climate change on the potential distribution and habitat fragmentation of Caucasian crab apple (Malus orientalis Uglitzk.) and the designation of areas of high interest according to climatic conditions. We used the MaxEnt models and Morphological-Spatial Analysis (MSPA) to evaluate the potential distribution, suitability changes, habitat fragmentation, and connectivity throughout the species range in Turkey, Armenia, Georgia, Russia, and Iran. The results revealed that the potentially suitable range of M. orientalis encompasses 858,877 km², 635,279 km² and 456,795 km² under the present, RCP4.5 and RCP8.5 scenario, respectively. The range fragmentation analysis demonstrated a notable shift in the edge/core ratio, which increased from 50.95% in the current scenario to even 67.70% in the future. The northern part of the range (Armenia, northern Georgia, southern Russia), as well as the central and western parts of Hyrcania will be a core of the species range with suitable habitats and a high connectivity between M. orientalis populations and could work as major refugia for the studied species. However, in the Zagros and central Turkey, the potential range will shrink due to the lack of suitable climatic conditions, and the edge/core ratio will grow. In the southern part of the range, a decline of M. orientalis habitats is expected due to changing climatic conditions. The future outlook suggests that the Hyrcanian forest and the Caucasus region could serve as important refuges for M. orientalis. This study helps to understand spatial changes in species\' range in response to climate change and can help develop conservation strategies. This is all the more important given the species\' potential use in future breeding programs aimed at enriching the gene pool of cultivated apple varieties.

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the \"Overview, Data, Model, Assessment, and Prediction\" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models\' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

BACKGROUND: Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)-based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM).
OBJECTIVE: This study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process.
METHODS: This is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post-kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m2. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results.
RESULTS: The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025.
CONCLUSIONS: This is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic.
BACKGROUND: ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518.
UNASSIGNED: PRR1-10.2196/54857.

OBJECTIVE: To describe the role of patients with a chronic disease, healthcare professionals (HCPs) and technology in shared decision making (SDM) and the use of clinical decision support systems (CDSSs), and to evaluate the effectiveness of SDM and CDSSs interventions.
METHODS: Randomized controlled studies published between 2011 and 2021 were identified and screened independently by two reviewers, followed by data extraction and analysis. SDM elements and interactive styles were identified to shape the roles of patients, HCPs and technology.
RESULTS: Forty-three articles were identified and reported on 21 SDM-studies, 15 CDSS-studies, 2 studies containing both an SDM-tool and a CDSS, and 5 studies with other decision support components. SDM elements were mostly identified in SDM-tools and interactions styles were least common in the other decision support components.
CONCLUSIONS: Patients within the included RCTs mainly received information from SDM-tools and occasionally CDSSs when it concerns treatment strategies. HCPs provide and clarify information using SDM-tools and CDSSs. Technology provides interactions, which can support more active SDM. SDM-tools mostly showed evidence for positive effects on SDM outcomes, while CDSSs mostly demonstrated positive effects on clinical outcomes.
CONCLUSIONS: Technology-supported SDM has potential to optimize SDM when patients, HCPs and technology collaborate well together.