Limited Coverage Research Articles

Medical Care For People In Precarious Living Situations In Germany: Comparative Analysis Of Five Clearing Centers

In addition to unemployment, poor living and housing conditions and increased morbidity, precarious living situations are often characterized by a lack of or inadequate health insurance. In the Federal Republic of Germany, there is a difficult-to-quantify number of people who, for various reasons, have only limited health insurance coverage or none at all. Those affected include German citizens, EU citizens, and non-EU-citizens who are frequently homeless and/or undocumented. Health care for those groups is predominantly provided outside the German health care system by voluntary non-governmental and welfare organizations. Several German states and municipalities together with non-governmental organizations have addressed this problem by establishing clearing centers often combined with cost reimbursement for medical treatment. In this article, five of these projects are analysed and compared with each other with regard to their structure and mode of operation. The results show that many people can be helped in this way, but in order to meet the high and increasing demand, effort must be put towards more comprehensive solutions as well as on the expansion and sustainability of these projects. At the same time, there is an urgent need to improve statistical and epidemiological data.

Q-learning and fuzzy logic multi-tier multi-access edge clustering for 5g v2x communication

ABSTRACT The 5th generation (5 G) network is required to meet the growing demand for fast data speeds and the expanding number of customers. Apart from offering higher speeds, 5 G will be employed in other industries such as the Internet of Things, broadcast services, and so on. Energy efficiency, scalability, resiliency, interoperability, and high data rate/low delay are the primary requirements and obstacles of 5 G cellular networks. Due to IEEE 802.11p’s constraints, such as limited coverage, inability to handle dense vehicle networks, signal congestion, and connectivity outages, efficient data distribution is a big challenge (MAC contention problem). In this research, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) and vehicle-to-pedestrian (V2P) services are used to overcome bandwidth constraints in very dense network communications from cellular tool to everything (C-V2X). Clustering is done through multi-layered multi-access edge clustering, which helps reduce vehicle contention. Fuzzy logic and Q-learning and intelligence are used for a multi-hop route selection system. The proposed protocol adjusts the number of cluster-head nodes using a Q-learning algorithm, allowing it to quickly adapt to a range of scenarios with varying bandwidths and vehicle densities.

Insuring deposits, ensuring stability: A critical evaluation of six decades of deposit insurance in the Indian banking sector

The deposit insurance system in India is one of the oldest in the world. Though deposit insurance is a well-researched area across the globe, it is relatively unexplored in India. This paper aims to critically evaluate some of the core aspects of the Indian deposit insurer, explicate the strengths and weaknesses of the system, highlight the risks that accrue to the system due to its features and the lessons learned from its 60 years of experience. The study suggests that the Indian deposit insurance system is marred by certain issues, such as irregular revisions of coverage limits, cross-subsidisation, delays in the recovery of settled claims, the restricted role of the insurer and the non-availability of a benchmark for the insurance fund. It further provides policy recommendations for practitioners to overcome these shortcomings and enhance the robustness of the deposit insurer by allowing better risk management under the scheme.

Ultra-dense Motion Capture: An exploratory full-automatic approach for dense tracking of breast motion in 4D.

Understanding the dynamic deformation pattern and biomechanical properties of breasts is crucial in various fields, including designing ergonomic bras and customized prostheses, as well as in clinical practice. Previous studies have recorded and analyzed the dynamic behaviors of the breast surface using 4D scanning, which provides a sequence of 3D meshes during movement with high spatial and temporal resolutions. However, these studies are limited by the lack of robust and automated data processing methods which result in limited data coverage or error-prone analysis results. To address this issue, we identify revealing inter-frame dense correspondence as the core challenge towards conducting reliable and consistent analysis of the 4D scanning data. We proposed a fully-automatic approach named Ulta-dense Motion Capture (UdMC) using Thin-plate Spline (TPS) to augment the sparse landmarks recorded via motion capture (MoCap) as initial dense correspondence and then rectified it with a sophisticated post-alignment scheme. Two downstream tasks are demonstrated to validate its applicability: virtual landmark tracking and deformation intensity analysis. For evaluation, a dynamic 4D human breast anthropometric dataset DynaBreastLite was constructed. The results show that our approach can robustly capture the dynamic deformation characteristics of the breast surfaces, significantly outperforms baselines adapted from previous works in terms of accuracy, consistency, and efficiency. For 10 fps dataset, average error of 0.25 cm on control-landmarks and 0.33 cm on non-control (arbitrary) landmarks were achieved, with 17-70 times faster computation time. Evaluation was also carried out on 60 fps and 120 fps datasets, with consistent and large performance gaining being observed. The proposed method may contribute to advancing research in breast anthropometry, biomechanics, and ergonomics by enabling more accurate tracking of the breast surface deformation patterns and dynamic characteristics.

Tourist satisfaction model: Structural relationship of destination image, electronic word of mouth, and service quality in Bali destination

Purpose: This research aims to investigate the interplay between destination image, Electronic Word of Mouth (e-WoM), service quality, and tourist satisfaction during their stay in Bali.
 Design/Methodology/Approach: The research design uses quantitative causality. The data collection technique employed in this study involves a questionnaire distributed to 600 respondents, determined through incidental sampling.
 Findings: Service quality, while the services provided by the Monkey Forest staff are well-received by tourists; destination image, while the attractiveness of Monkey Forest tourist attractions is unique and appealing; and the beauty of the natural landscape. Tourist satisfaction exerts a positive and significant influence on e-WoM, while a satisfied traveler with the image and service quality of the monkey forest destination is willing to engage in marketing through electronic word of mouth. Additionally, two moderation relationships are discovered: the relationship between service quality and e-WoM, which is positively and significantly influenced by the variable of tourist satisfaction, and the clause relationship, wherein destination image positively and significantly influences e-WoM, with tourist satisfaction as a moderating variable.
 Conclusions: So, it can be concluded that there are five clause relationships between variables as hypotheses, including three direct effects and two moderating relationships (indirect effects).
 Research Limitations: The limitations of this research lie in the limited coverage of the research object, focusing on either a specific area or solely on one destination.
 Practical Implications: The implication of this study on the e-WoM variable, which serves as an endogenous variable, significantly impacts the Normated Fit Index, albeit within reasonable limits.

A practical guide to over‐the‐counter treatments for hyperpigmentation

AbstractHyperpigmentation of the skin, defined as increased melanin in the epidermis, is a common condition associated with psychosocial impairment and decreased quality of life. Prescription options are often not covered by insurance and can be cost prohibitive. As such, many turn to over‐the‐counter (OTC) products for treatment. However, a plethora of options are available, which can be confusing. This review aims to evaluate the evidence for common ingredients found in OTC products for management of hyperpigmentation and provide guidance for optimal use to clinicians and consumers. To perform this review, a comprehensive literature search utilizing PubMed and Google Scholar was conducted to identify relevant studies on the clinico‐epidemiological aspects of hyperpigmentation, including postinflammatory hyperpigmentation (PIH), melasma and maturational hyperpigmentation, as well as on relevant OTC treatments. The efficacy and safety of OTC products were assessed based on available evidence, clinical trials and expert recommendations. Our results showed hyperpigmentation is more prevalent among individuals with darker skin types (Fitzpatrick skin phototypes III–VI). Due to limited insurance coverage and the high cost of prescription options, over‐the‐counter (OTC) treatments are widely used for managing postinflammatory hyperpigmentation (PIH). However, the lack of strict regulation by the US Food and Drug Administration (FDA) raises concerns about the safety and efficacy of these products. By critically evaluating the existing literature, this review offers practical, evidence‐based guidance for the selection and utilization of OTC products for the treatment of hyperpigmentation.

Evaluation and Comparison of Reanalysis Data for Runoff Simulation in the Data-Scarce Watersheds of Alpine Regions

Reanalysis datasets provide a reliable reanalysis of climate input data for hydrological models in regions characterized by limited weather station coverage. In this paper, the accuracy of precipitation, the maximum and minimum temperatures of four reanalysis datasets, the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS), time-expanded climate forecast system reanalysis (CFSR+), the European Centre for Medium-Range Weather Forecast Reanalysis (ERA). and the China Meteorological Forcing Dataset (CMFD), were evaluated by using data from 28 ground-based observations (OBs) in the Source of the Yangtze and Yellow Rivers (SYYR) region and were used as input data for the SWAT model for runoff simulation and performance evaluation, respectively. And, finally, the CMADS was optimized using Integrated Calibrated Multi-Satellite Retrievals for Global Precipitation Measurement (AIMERG) data. The results show that CMFD is the most representative reanalysis data for precipitation characteristics in the SYYR region among the four reanalysis datasets evaluated in this paper, followed by ERA5 and CFSR, while CMADS performs satisfactorily for temperature simulations in this region, but underestimates precipitation. And we contend that the accuracy of runoff simulations is notably contingent upon the precision of daily precipitation within the reanalysis dataset. The runoff simulations in this region do not effectively capture the extreme runoff characteristics of the Yellow River and Yangtze River sources. The refinement of CMADS through the integration of AIMERG satellite precipitation data emerges as a potent strategy for enhancing the precision of runoff simulations. This research can provide a reference for selecting meteorological data products and optimization methods for hydrological process simulation in areas with few meteorological stations.

Cost-effectiveness analysis of vaccination strategies against meningococcal disease for children under nine years of age in China

ABSTRACT Meningococcal vaccination strategies in China are intricate, including multiple vaccines targeting different serogroups. The current National Immunization Program (NIP) includes two polysaccharide vaccines for serogroups A and C (MPV-A and MPV-AC), covering limited serogroups and requiring adaptation. This study aims to evaluate the cost-effectiveness of replacing the current strategy with alternative strategies utilizing non-NIP vaccines to inform policy decisions. From a societal perspective, a decision tree-Markov model was constructed to simulate the economic and health consequences of meningococcal disease in a 2019 birth cohort with four vaccination strategies. Epidemiology, vaccine efficacy, cost, and other parameters were derived from previous studies. We conducted sensitivity analyses to assess the robustness of the findings and explored prices for non-NIP vaccines that enable cost-effective strategies. Compared to the current strategy, alternative strategies using quadrivalent polysaccharide vaccine (MPV-4), bivalent conjugate vaccine (MCV-AC), and quadrivalent conjugate vaccine (MCV-4) could avoid 91, 286, and 455 more meningococcal cases. The ICERs were estimated at approximately $250 thousand/QALY, $450 thousand/QALY, and $1.5 million/QALY, all exceeding the threshold of three times GDP per capita. The alternative strategies were not cost-effective. However, if vaccine prices were reduced to $3.9 for MPV-4, $9.9 for MCV-AC, and $12 for MCV-4, the corresponding strategy would be cost-effective. The current meningococcal vaccination strategy in China could effectively prevent the disease at a low cost, but with limited serogroup coverage. Strategies using MPV-4, MCV-AC, or MCV-4 could increase health benefits at a substantial cost, and might become cost-effective if vaccine prices decrease.

Unveiling Disparities: Investigating the Gap between Palestinian Authority Counter Segregation Policies and Local Implementation Amid Deteriorating Conditions in the West Bank

Spatial segregation in the West Bank remains a pressing issue with profound social, economic, and political implications. This study examines the gap between the Palestinian Authority's (PA) political statements and the local implementation of strategic planning measures to counter segregation in Palestinian communities. By analyzing the Strategic Development and Investment Plans (SDIPs) from 2018-2021, this research investigates the extent to which these plans address the challenges posed by Israeli settlements and promote inclusive urban development. The findings reveal significant shortcomings, including limited coverage of settlement-related challenges, inadequate strategies to confront settlement activity, and a lack of context-specific approaches; Out of the 122 plans analyzed, only 15 (12.3%) explicitly addressed the geopolitical situation, while 67 (54.9%) acknowledged settlements as a challenge. Disturbingly, 28 plans (22.9%) neglected the significance of Area C, highlighting a substantial oversight.

The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction

Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management.

3D radiated power analysis of JET SPI discharges using the Emis3D forward modeling tool

Precise values for radiated energy in tokamak disruption experiments are needed to validate disruption mitigation techniques for burning plasma tokamaks like ITER and SPARC. Control room analysis of radiated power (P rad) on JET assumes axisymmetry, since fitting 3D radiation structures with limited bolometry coverage is an under-determined problem. In mitigated disruptions, radiation is toroidally asymmetric and 3D, due to fast-growing 3D MHD modes and localized impurity sources. To address this problem, Emis3D adopts a physics motivated forward modeling (‘guess and check’) approach, comparing experimental bolometry data to synthetic data from user-defined radiation structures. Synthetic structures are observed with the Cherab modeling framework and a best fit chosen using a reduced χ 2 statistic. 2D tomographic inversion models are tested, as well as helical flux tubes and 3D MHD simulated structures from JOREK. Two nominally identical pure neon shattered pellet injection (SPI) mitigated discharges in JET are analyzed. 2D tomographic inversions with added toroidal freedom are the best fits in the thermal quench (TQ) and current quench (CQ). In the pre-TQ, 2D reconstructions are statistically the best fits, but are likely over-optimized and do not capture the 3D radiation structure seen in fast camera images. The next-best pre-TQ fits are helical structures that extend towards the high-field side, consistent with an impurity flow under the magnetic nozzle effect also observed in JOREK simulations. Whole-disruption radiated fractions of 0.98+0.03/ −0.29 and 1.01+0.02/−0.17 are found, suggesting that the stored energy may have been fully mitigated by each SPI, although mitigation efficiencies well below ITER and SPARC requirements for high energy pulses are still within the large uncertainties. Emis3D is also used to validate JOREK SPI simulations, and confirms improvements in matching experiment from changes to impurity modeling. Time-dependent toroidal peaking factors are calculated and discussed.

Challenges and Limitations of Remote Sensing Applications in Northern Peatlands: Present and Future Prospects

This systematic literature review (SLR) provides a comprehensive overview of remote sensing (RS) applications in northern peatlands from 2017 to 2022, utilising various platforms, including in situ, UAV, airborne, and satellite technologies. It addresses the challenges and limitations presented by the sophisticated nature of northern peatland ecosystems. This SLR reveals an in-creased focus on mapping, monitoring, and hydrology but identifies noticeable gaps in peatland degradation research. Despite the benefits of remote sensing, such as extensive spatial coverage and consistent monitoring, challenges persist, including high costs, underexplored areas, and limitations in hyperspectral data application. Fusing remote sensing data with on-site research offers new insights for regional peatland studies. However, challenges arise from issues like the cost of high-resolution data, coverage limitations, and inadequate field validation data in remote areas. This review suggests refining methodologies, validating with high-resolution data, and addressing these limitations for future research.

Micropillar arrays, wide window acquisition and AI-based data analysis improve comprehensiveness in multiple proteomic applications.

Comprehensive proteomic analysis is essential to elucidate molecular pathways and protein functions. Despite tremendous progress in proteomics, current studies still suffer from limited proteomic coverage and dynamic range. Here, we utilize micropillar array columns (µPACs) together with wide-window acquisition and the AI-based CHIMERYS search engine to achieve excellent proteomic comprehensiveness for bulk proteomics, affinity purification mass spectrometry and single cell proteomics. Our data show that µPACs identify ≤50% more peptides and ≤24% more proteins, while offering improved throughput, which is critical for large (clinical) proteomics studies. Combining wide precursor isolation widths of m/z 4-12 with the CHIMERYS search engine identified +51-74% and +59-150% more proteins and peptides, respectively, for single cell, co-immunoprecipitation, and multi-species samples over a conventional workflow at well-controlled false discovery rates. The workflow further offers excellent precision, with CVs <7% for low input bulk samples, and accuracy, with deviations <10% from expected fold changes for regular abundance two-proteome mixes. Compared to a conventional workflow, our entire optimized platform discovered 92% more potential interactors in a protein-protein interaction study on the chromatin remodeler Smarca5/Snf2h. These include previously described Smarca5 binding partners and undescribed ones including Arid1a, another chromatin remodeler with key roles in neurodevelopmental and malignant disorders.

Long-Acting Injectable Antiretrovirals for HIV Treatment: A Multi-Site Qualitative Study of Clinic-Level Barriers to Implementation in the United States.

Long-acting injectable antiretroviral therapy (LAI ART) has the potential to address adherence obstacles associated with daily oral ART, leading to enhanced treatment uptake, adherence, and viral suppression among people living with HIV (PLWH). Yet, its potential may be limited due to ongoing disparities in availability and accessibility. We need a better understanding of the organizational context surrounding the implementation of LAI ART, and to inform its widespread rollout, we conducted 38 in-depth interviews with medical and social service providers who offer HIV care at private and hospital-based clinics across six US cities. Our findings highlight real-world implementation barriers outside of clinical trial settings. Providers described ongoing and anticipated barriers across three stages of LAI ART implementation: (1) Patient enrollment (challenges registering patients and limited insurance coverage), (2) medication delivery (insufficient personnel and resources), and (3) leadership and management (lack of interprofessional coordination and a lack of programming guidelines). Providers described how these barriers would have a disproportionate impact on under-resourced clinics, potentially exacerbating existing disparities in LAI ART access and adherence. Our findings suggest strategies that clinic leadership, policymakers, and other stakeholders can pursue to promote rapid and equitable LAI ART implementation in clinics across the United States. Resource and staffing investments could support clinics to begin, sustain, and scale up LAI ART delivery; additionally, the establishment of guidelines and tools could facilitate wider adoption of LAI ART across clinical settings. These efforts are crucial to promote resourced, standardized, and equitable implementation of LAI ART and maximize its potential to help end the HIV epidemic.

MDPose: Human Skeletal Motion Reconstruction Using WiFi Micro-Doppler Signatures

Motion tracking systems based on optical sensors typically suffer from poor lighting conditions, occlusion, limited coverage, and may raise privacy concerns. More recently, radiofrequency (RF) based approaches using commercial WiFi devices have emerged which offer low-cost ubiquitous sensing whilst preservin privacy. However, RF sensing systems typically output range-Doppler maps, time-frequency spectrograms, cross-range plots etc which cannot represent human motion intuitively and usually requires further processing. In this study, we propose MDPose, a novel framework for human skeletal motion reconstruction base on WiFi micro-Doppler signatures. MDPose provides an effective solution to represent human activity by reconstructing a skeleton model with 17 key points, which can assist with the interpretation of conventional RF sensing outputs in a more understandable way. Specifically, MDPose is implemented over three sequential stage to address a series of challenges: First, a denoising algorithm is employed to remove any unwanted noise that may affect feature extraction and enhance weak Doppler measurements. Secondly, a convolutional neural network (CNN)-recurrent neural network (RNN) architecture is applied to learn temporal spatial dependenc from clean micro-Doppler signatures and restore velocity information to key points under the supervision of the motion capture (Mocap) system. Finally, a pose optimisation mechanism based on learning optimisation vectors is employed to estimate the initial state of the skeleton and to limit additional errors. We hav conducted a comprehensive set of tests in a variety of environments using numerous subjects with a single receiver radar system to demonstrate the performance of MDPose, and report 29.4mm mean absolute error over all key points positions on several common daily activities, which has performance comparable to that of state-ofthe- art RF-based pose estimation systems.

Climatology of Dayside E‐Region Zonal Neutral Wind Shears From ICON‐MIGHTI Observations

AbstractLarge vertical shears in the E‐region neutral zonal winds can lead to ion convergences and contribute to plasma irregularities, but climatological studies of vertical shears of horizontal winds in a global scale are lacking due to the limitations of data coverage. The Ionospheric Connection Explorer (ICON) Michelson Interferometer for Global High‐resolution Thermospheric Imaging (MIGHTI) has provided neutral wind observations with an unprecedented spatial coverage. In this study, the climatology of dayside E‐region neutral wind shears has been examined using 2‐years’ data (2020–2021). Specifically, the study focuses on large wind shears with a magnitude larger than 20 m/s/km, since large wind shears are more likely to cause significant perturbation in the ionosphere‐thermosphere (I‐T) system. The results show that the probability of occurrence of large shears is strongly dependent on the altitude, with the vertical profile varying with shear direction, latitude, season, and local time. In general, below 110 km altitude, large negative shears of the eastward wind are most likely to happen during summer at 8–10 LT in 25°N–40°N latitudes, showing a high probability across nearly all longitudes. Meanwhile, large positive shears tend to occur in 10°S–10°N latitudes, with peak probabilities exhibiting roughly consistent longitudinal structures across 8–10 LT in all seasons. The discrepancies between positive and negative large shear distributions underlie different global tidal influences. The large‐shear occurrence probabilities above 110 km are generally small, except in latitudes above 25°N during the winter for positive shears.

Leveraging ICESat, ICESat‐2, and Landsat for Global‐Scale, Multi‐Decadal Reconstruction of Lake Water Levels

AbstractLakes provide important water resources and many essential ecosystem services. Some of Earth's largest lakes recently reached record‐low levels, suggesting increasing threats from climate change and anthropogenic activities. Yet, continuous monitoring of lake levels is challenging at a global scale due to the sparse in situ gauging network and the limited spatial or temporal coverage of satellite altimeters. A few pioneering studies used water areas and hypsometric curves to reconstruct water levels but suffered from large uncertainties due to the lack of high‐quality hypsometry data. Here, we propose a novel proxy‐based method to reconstruct multi‐decadal water levels from 1992 to 2018 for both large and small lakes using Landsat images and ICESat (2003–2009) and recently launched ICESat‐2 (2018+) laser altimeters. Using the new method, we evaluate reconstructed levels of 342 lakes worldwide, with sizes ranging from 1 to 81,844 km2. Reconstructed water levels have a median root‐mean‐square error (RMSE) of 0.66 m, equivalent to 57% of the standard deviation of monthly level variability. Compared with two recently reconstructed water level data sets, the proposed method reduces the median RMSE by 27%–32%. The improvement is attributable to the new method's robust construction of high‐quality hypsometry, with a median R2 value of 0.92. Most reconstructed water level time series have a bi‐monthly or higher frequency. Given that ICESat‐2 and Landsat can observe hundreds of thousands of water bodies, this method can be applied to conduct an improved global inventory of time‐varying lake levels and thus inform water resource management more broadly than existing methods.

Characterizing Subsurface Oxygen Variability in the California Current System (CCS) and Its Links to Water Mass Distribution

AbstractThe california current system (CCS) supports a wide array of ecosystem services with hypoxia historically occurring in near‐bottom waters. Limited open ocean data coverage hinders the mechanistic understanding of CCS oxygen variability. By comparing three different models with varying horizontal resolutions, we found that dissolved oxygen (DO) anomalies in the CCS are propagated from shallower coastal areas to the deeper open ocean, where they are advected at a density and velocity consistent with basin‐scale circulation. Since DO decreases have been linked to water mass redistribution in the CCS, we conduct a water mass analysis on two of the models and on biogeochemical Argo floats that sampled multiple seasonal cycles. We found that high variability in biogeochemical variables (DO and nutrients) seen in regions of low variability of temperature and salinity could be linked to water mass mixing, as some of the water masses considered had higher gradients in biogeochemical variables compared to physical variables. Additional DO observations are needed, therefore, to further understand circulation changes in the CCS. We suggest that increased DO sampling north of 35˚N and near the shelf break would benefit model initialization and skill assessment, as well as allow for better assessment of the role of equatorial waters in driving DO in the northern CCS.

Fracture related infection in open tibial fractures

Open tibia fractures frequently occur following high-energy trauma. Contamination of the fracture site combined with limited soft tissue coverage and blood supply means that these open fractures are associated with a high rate of complications, including fracture related infection (FRI). FRI is associated with lowered patient outcomes and requires early recognition and appropriate surgical and medical management.The current evidence on FRI after open tibial fractures largely is limited to case series, small retrospective cohort studies and expert opinion. Recent expert consensus has produced guidelines with the aim of standardising care for these patients. This review summarises the current management strategies employed in treating FRI following open tibial fractures and where possible the evidence behind them.

Unveiling Assessment Gaps in Parkinson's Disease Psychosis: A Scoping Review.

Parkinson's disease psychosis (PDP) is a multidimensional construct that is challenging to measure. Accurate assessment of PDP requires comprehensive and reliable clinical outcome assessment (COA) measures. To identify PDP measurement gaps in available COAs currently used in clinical and research settings. We conducted a scoping review using Preferred Reporting Items for Systematic Review and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. We implemented a three-step search strategy in international databases with keywords related to Parkinson's disease (PD), psychosis, and COA. We analyzed studies using COA to assess PDP, classifying their items according to domains and subdomains. From 5673 identified studies, we included 628 containing 432 PDP core items from 32 COAs. Among the 32 COAs, 19 were PD-specific, containing 266 items, constructed as clinician-reported outcomes (ClinRO) (148 items), patient-reported outcomes (PRO) (112 items), and observer-reported outcomes (ObsRO) (six items). Across all PD-specific COAs, regardless of structure, 89.4% of the items from 27 COAs focused primarily on assessing PDP symptoms' severity, and only 9.7% of items probed the impact of PDP on a person's daily functioning. Symptom-based domains are currently prioritized for measuring the severity of PDP, with limited coverage of the functional impact of PDP on patients' lives. Whereas the International Parkinson and Movement Disorder Society has traditionally developed a "Unified" COA that culls items from prior COAs to form a new one, a new COA will largely need newly developed items if the functional impact of PDP is prioritized. © 2024 International Parkinson and Movement Disorder Society.