Percepção de aprendizagem on-line na disciplina de biomateriais / Perception of online learning in the biomaterials course

Com as universidades fechadas e a implementação do Ensino Remoto Emergencial, as atividades curriculares ocorreram através de plataformas digitais. O objetivo do presente trabalho foi avaliar a percepção de aprendizagem on-line na disciplina de Biomateriais da Faculdade de Odontologia da Universidade Federal Fluminense no período da pandemia. O questionário COLLES (Constructivist OnLine Learning Environment Survey) foi enviado individualmente por e-mail aos cinquenta alunos, apresentando 24 declarações divididas em seis quesitos: relevância, reflexão crítica, interatividade, apoio dos tutores, apoio entre os colegas e compreensão; e para cada declaração cinco opções de resposta: quase sempre, frequentemente, algumas vezes, raramente e quase nunca. Quarenta e um alunos responderam. A soma das médias obtidas em quase sempre e frequentemente foi de 87,2% para relevância, 70% para reflexão crítica, 33,9% para interatividade, 47,6% para apoio dos tutores, 44,2% para apoio dos colegas e 89,5% para compreensão. Concluiu-se que a relevância, a reflexão crítica e a compreensão apresentaram melhores resultados, enquanto a interatividade, o apoio entre os colegas e o apoio dos tutores demonstraram necessidade de aprimoramento. E apesar das limitações do ERE, a avaliação positiva dos alunos evidenciou esta modalidade de educação on-line como uma solução plausível.

With universities closed and the implementation of Emergency Remote Teaching, curricular activities took place through digital platforms. The objective of this study was to assess the perception of online learning in the Biomaterials course at the Dental School of the Federal Fluminense University during the pandemic. The COLLES questionnaire (Constructivist OnLine Learning Environment Survey) was individually sent via email to fifty students, presenting 24 statements divided into six aspects: relevance, critical reflection, interactivity, tutor support, peer support, and comprehension. For each statement, there were five response options: almost always, often, sometimes, rarely, and almost never. Forty-one students responded. The sum of the averages obtained for almost always and often was 87.2% for relevance, 70% for critical reflection, 33.9% for interactivity, 47.6% for tutor support, 44.2% for peer support, and 89.5% for comprehension. It was concluded that relevance, critical reflection, and comprehension showed better results, while interactivity, peer support, and tutor support demonstrated a need for improvement. Despite the limitations of Emergency Remote Teaching, the positive evaluation from the students highlighted this mode of online education as a plausible solution.

Real-road NOx and CO2 emissions of city and highway China-6 heavy-duty diesel vehicles.

The emission of heavy-duty vehicles has raised great concerns worldwide. The complex working and loading conditions, which may differ a lot from PEMS tests, raised new challenges to the supervision and control of emissions, especially during real-world applications. On-board diagnostics (OBD) technology with data exchange enabled and strengthened the monitoring of emissions from a large number of heavy-duty diesel vehicles. This paper presents an analysis of the OBD data collected from more than 800 city and highway heavy-duty vehicles in China using remote OBD data terminals. Real-world NOx and CO2 emissions of China-6 heavy-duty vehicles have been examined. The results showed that city heavy-duty vehicles had higher NOx emission levels, which was mostly due to longer time of low SCR temperatures below 180°C. The application of novel methods based on 3B-MAW also found that heavy-duty diesel vehicles tended to have high NOx emissions at idle. Also, little difference had been found in work-based CO2 emissions, and this may be due to no major difference were found in occupancies of hot running.

Establishment and verification of anthropogenic speciated VOCs emission inventory of Central China.

Improving the accuracy of anthropogenic volatile organic compounds (VOCs) emission inventory is crucial for reducing atmospheric pollution and formulating control policy of air pollution. In this study, an anthropogenic speciated VOCs emission inventory was established for Central China represented by Henan Province at a 3 km × 3 km spatial resolution based on the emission factor method. The 2019 VOCs emission in Henan Province was 1003.5 Gg, while industrial process source (33.7%) was the highest emission source, Zhengzhou (17.9%) was the city with highest emission and April and August were the months with the more emissions. High VOCs emission regions were concentrated in downtown areas and industrial parks. Alkanes and aromatic hydrocarbons were the main VOCs contribution groups. The species composition, source contribution and spatial distribution were verified and evaluated through tracer ratio method (TR), Positive Matrix Factorization Model (PMF) and remote sensing inversion (RSI). Results show that both the emission results by emission inventory (EI) (15.7 Gg) and by TR method (13.6 Gg) and source contribution by EI and PMF are familiar. The spatial distribution of HCHO primary emission based on RSI is basically consistent with that of HCHO emission based on EI with a R-value of 0.73. The verification results show that the VOCs emission inventory and speciated emission inventory established in this study are relatively reliable.

Spatial ecology, biodiversity, and abiotic determinants of Congo's bai ecosystem.

Canopy gaps are foundational features of rainforest biodiversity and successional processes. The bais of Central Africa are among the world's largest natural forest clearings and thought to be critically important islands of open-canopy habitat in an ocean of closed-canopy rainforest. However, while frequently denoted as a conservation priority, there are no published studies on the abundance or distribution of bais across the landscape, nor on their biodiversity patterns, limiting our understanding of their ecological contribution to Congolese rainforests. We combined remote sensing and field surveys to quantify the abundance, spatial distribution, shape, size, biodiversity, and soil properties of bais in Odzala-Kokoua National Park (OKNP), Republic of the Congo (hereafter, Congo). We related bai spatial distribution to variation in hydrology and topography, compared plant community composition and 3D structure between bais and other open ecosystems, quantified animal diversity from camera traps, and measured soil moisture content in different bai types. We found bais to be more numerous than previously thought (we mapped 2176 bais in OKNP), but their predominantly small size (80.7% of bais were <1 ha), highly clustered distribution, and restriction to areas of low topographic position make them a rare riparian habitat type. We documented low plant community and structural similarity between bai types and with other open ecosystems, and identified significant differences in soil moisture between bai and open ecosystem types. Our results demonstrate that two distinct bai types can be differentiated based on their plant and animal communities, soil properties, and vegetation structure. Taken together, our findings provide insights into how bais relate to other types of forest clearings and on their overall importance to Congolese rainforest ecosystems.

The AlzMatch Pilot Study - Feasibility of Remote Blood Collection of Plasma Biomarkers for Preclinical Alzheimer's Disease Trials.

BACKGROUND: Advances in plasma biomarkers to detect Alzheimer's disease (AD) biology allows researchers to improve the efficiency of participant recruitment into preclinical trials. Recently, protein levels of plasma amyloid-beta and tau proteins have been shown to be predictive of elevated amyloid in brain. Online registries, such as the Alzheimer's Prevention Trials (APT) Webstudy, include and follow participants using remote assessments to facilitate efficient screening and enrollment of large numbers of individuals who may be at higher risk for AD. OBJECTIVES: The AlzMatch Pilot Study investigated the feasibility of recruiting individuals from an online registry for blood sample collection at community-based phlebotomy centers and plasma biomarker quantification to assess an individual's eligibility for AD preclinical trials. DESIGN: Pilot feasibility study with co-primary outcomes. SETTING: This pilot feasibility study included participants from the APT Webstudy, the remote assessment arm of the Trial-ready cohort for Preclinical and Prodromal AD (TRC-PAD) Platform. Novel design included collection of electronic consent, use of community laboratories for plasma collection, mass spectrometry-based biomarker assay, and telephone communication of plasma biomarker screening eligibility. PARTICIPANTS: Participants invited to the AlzMatch pilot feasibility study were active in the APT Webstudy, 50 years of age or older, resided within 50 miles of both a Quest Diagnostics Patient Services Center (a national diagnostic laboratory with convenient locations for sample collection and processing) and one of six TRC-PAD vanguard clinical trial sites, had no self-reported dementia diagnosis, were able to communicate in English and engaged with the APT Webstudy within the prior 6 months. MEASUREMENTS: Primary feasibility outcomes were completion of electronic consent (e-consent) for invited participants and collection of usable blood samples. Additional feasibility outcomes included invitation response rate, plasma biomarker eligibility status (based on amyloid beta-42/40 [Aß42/40] concentration ratio), ApoE proteotype, and trial inclusion criterion), and completion of telephone contact to learn eligibility to screen for a study. RESULTS: 300 APT Webstudy participants were invited to consent to the AlzMatch study. The AlzMatch e-consent rate was 39% (n=117) (95% CI of 33.5%-44.5%) overall, which was higher than the expected rate of 25%. Similar consent rates were observed across participants based on self-defined sex (41% Female (n=75), 37% Male (n=42)) and race and ethnicity (37% from underrepresented groups (URG) (n=36), 40% not from URG (n=79)). Among those that consented (n=117), plasma was successfully collected from 74% (n=87) (95% CI of 66%-82%), with similar rates across sex (76% Female (n=57), 71% Male (n=30)) and race and ethnicity (75% URG (n=27) and 75% not from URG (n=59)). 60% (n=51) of participants with plasma biomarker results were eligible to screen for future preclinical AD trials. CONCLUSION: Electronic consent of participants through an online registry, blood sample collection at community-based centers, plasma biomarker quantification and reporting, and biomarker assessments for study eligibility were all feasible with similar engagement rates across demographic groups. Although this pilot was a small and selective sample, participants engaged and consented at higher than expected rates. We conclude that collecting blood at community laboratories for biomarker analyses may improve accessibility beyond research, and may facilitate broader access for clinical use of AD plasma biomarkers. Based on our results, an expanded version of the AlzMatch study is underway, which involves expanding invitations to additional APT Webstudy participants and clinical trial sites.

Assessing Telehealth in Palliative Care: A Systematic Review of the Effectiveness and Challenges in Rural and Underserved Areas.

The integration of telehealth into palliative care has garnered significant attention due to its potential to enhance both access and quality of care, particularly for patients in rural and underserved areas. This interest stems from the need to address geographical and logistical barriers that traditionally hinder palliative care delivery. Despite its potential benefits, the effectiveness of telehealth and the challenges associated with its implementation remain underexplored, necessitating further investigation. This study aims to critically evaluate the effectiveness of telehealth in palliative care by focusing on several key areas: its impact on access to care, symptom management, patient satisfaction, and cost-effectiveness. To achieve this, a systematic review was conducted, synthesizing data from various studies that investigated telehealth interventions within palliative care settings. The review employed a comprehensive search strategy across electronic databases, concentrating on randomized controlled trials (RTCs) published between 2014 and 2024. To ensure the reliability of the findings, low-quality and unrelated studies were excluded, and the remaining studies were meticulously analyzed for bias and methodological quality. The review's findings indicate that telehealth significantly enhances access to palliative care, allowing patients to receive timely and appropriate care without the need for extensive travel. It also improves symptom management and patient satisfaction, aligning to provide patient-centered care. Additionally, telehealth is cost-effective by reducing expenses associated with travel and in-person visits. These benefits highlight telehealth's potential to address some of the critical challenges in palliative care delivery. Despite its advantages, implementing telehealth in palliative care is not without challenges. Technological barriers, such as inadequate infrastructure and device limitations, pose significant hurdles. Integration issues, including the need for seamless incorporation into existing care systems, and varying levels of digital literacy among patients and caregivers, also impact the effectiveness of telehealth. Addressing these challenges is crucial for optimizing telehealth's implementation. Ensuring that telehealth solutions are accessible, user-friendly, and well-integrated into care practices is essential for fully leveraging its potential benefits.

An ICT-enabled community oriented primary care intervention in mining communities during COVID-19 (2019-2022): perceived changes in the role and place of community health workers.

BACKGROUND: This is a study of service provider perceptions of the place, role and practices of CHWs in a four-year, large-scale private sector funded, public service ICT-enabled COPC intervention with rural and remote mining communities. Like all South African communities, apart from large mining house employees and some contractors, most people use available public healthcare services and private traditional as well as limited allopathic private sector providers. In addition to the limitations of facility centred primary healthcare and a fragmented health care system, the many negative health effects of mining on the communities, go unattended. METHODS: This is a rapid, qualitative pragmatic study. Using site and participation convenience sampling, 37 semi-structured individual or group interviews were conducted with 57 stakeholders from 38 of the 135 intervention PHC facilities. Using a data driven, inductive approach, the results were analysed thematically in terms of perceived changes in the role and place of CHWs. RESULTS: CHWs registered 42 490 households and captured the demographic and social profiles as well as the health status of over 154 910 individuals using AitaHealth™. These data provided healthcare professionals and managers with knowledge about community demographics, at-risk groups and vulnerable individuals. The intervention changed the locational focus of CHW practice and expanded their scope of work and competencies in household comprehensive health education, advice and care. It led to a growth in community and professional confidence in CHWs as trusted members of mining community PHC teams and to more focused and efficient clinic work. CONCLUSION: This ICT-enabled COPC intervention adopted a comprehensive approach to healthcare delivery that started by including CHWs in PHC teams and locating them in communities. Inclusive and systematic continuous learning, clinically-led CHW service support and ICT-enabled information technology engendered trust in CHWs as competent PHC members, and grew community confidence in them and the PHC system as a whole. Although health, care and other professionals and workers valued the changes the intervention brought to their work as well as people's lives in underserved and vulnerable mining communities, its sustainability is contingent on the vagaries of political will and financial commitment.

Landsat-based spatiotemporal estimation of subtropical forest aboveground carbon storage using machine learning algorithms with hyperparameter tuning.

Introduction: The aboveground carbon storage (AGC) in forests serves as a crucial metric for evaluating both the composition of the forest ecosystem and the quality of the forest. It also plays a significant role in assessing the quality of regional ecosystems. However, current technical limitations introduce a degree of uncertainty in estimating forest AGC at a regional scale. Despite these challenges, remote sensing technology provides an accurate means of monitoring forest AGC. Furthermore, the implementation of machine learning algorithms can enhance the precision of AGC estimates. Lishui City, with its rich forest resources and an approximate forest coverage rate of 80%, serves as a representative example of the typical subtropical forest distribution in Zhejiang Province. Methods: Therefore, this study uses Landsat remote sensing images, employing backpropagation neural network (BPNN), random forest (RF), and categorical boosting (CatBoost) to model the forest AGC of Lishui City, selecting the best model to estimate and analyze its forest AGC spatiotemporal dynamics over the past 30 years (1989-2019). Results: The study shows that: (1) The texture information calculated based on 9×9 and 11×11 windows is an important variable in constructing the remote sensing estimation model of the forest AGC in Lishui City; (2) All three machine learning techniques are capable of estimating forest AGC in Lishui City with high precision. Notably, the CatBoost algorithm outperforms the others in terms of accuracy, achieving a model training accuracy and testing accuracy R2 of 0.95 and 0.83, and RMSE of 2.98 Mg C ha-1 and 4.93 Mg C ha-1, respectively. (3) Spatially, the central and southwestern regions of Lishui City exhibit high levels of forest AGC, whereas the eastern and northeastern regions display comparatively lower levels. Over time, there has been a consistent increase in the total forest AGC in Lishui City over the past three decades, escalating from 1.36×107 Mg C in 1989 to 6.16×107 Mg C in 2019. Discussion: This study provided a set of effective hyperparameters and model of machine learning suitable for subtropical forests and a reference data for improving carbon sequestration capacity of subtropical forests in Lishui City.

Accuracy and role of consumer facing wearable technology for continuous monitoring during endoscopic procedures.

Background: Consumer facing wearable devices capture significant amounts of biometric data. The primary aim of this study is to determine the accuracy of consumer-facing wearable technology for continuous monitoring compared to standard anesthesia monitoring during endoscopic procedures. Secondary aims were to assess patient and provider perceptions of these devices in clinical settings. Methods: Patients undergoing endoscopy with anesthesia support from June 2021 to June 2022 were provided a smartwatch (Apple Watch Series 7, Apple Inc., Cupertino, CA) and accessories including continuous ECG monitor and pulse oximeter (Qardio Inc., San Francisco, CA) for the duration of their procedure. Vital sign data from the wearable devices was compared to in-room anesthesia monitors. Concordance with anesthesia monitoring was assessed with interclass correlation coefficients (ICC). Surveys were then distributed to patients and clinicians to assess patient and provider preferences regarding the use of the wearable devices during procedures. Results: 292 unique procedures were enrolled with a median anesthesia duration of 34 min (IQR 25-47). High fidelity readings were successfully recorded with wearable devices for heart rate in 279 (95.5%) cases, oxygen in 203 (69.5%), and respiratory rate in 154 (52.7%). ICCs for watch and accessories were 0.54 (95% CI 0.46-0.62) for tachycardia, 0.03 (95% CI 0-0.14) for bradycardia, and 0.33 (0.22-0.43) for oxygen desaturation. Patients generally felt the devices were more accurate (56.3% vs. 20.0% agree, p < 0.001) and more permissible (53.9% vs. 33.3% agree, p < 0.001) to wear during a procedure than providers. Conclusion: Smartwatches perform poorly for continuous data collection compared to gold standard anesthesia monitoring. Refinement in software development is required if these devices are to be used for continuous, intensive vital sign monitoring.

Reactivation of stable neovascular age-related macular degeneration following treat-and-extend regimen discontinuation.

BACKGROUND: To describe the incidence and pattern of reactivation of neovascular age-related macular degeneration (nAMD) following successful treatment with treat-and-extend intravitreal anti-vascular endothelial growth factor therapy. METHODS: Consecutive patients with treated nAMD who did not require further treatment over a 6-month period and who attended their 3-monthly optical coherence tomography monitoring clinic in Moorfields Eye Hospital from 1 November 2019 to 31 January 2020 were included. Patients with diagnoses of macular neovascularization other than AMD, and patients with incomplete data were excluded. Baseline demographics recorded were age, sex, race, laterality, cause of macular neovascularization, drug, number of injections, and duration of treatment. Date, setting, symptoms, and time to retreatment were collected among patients with disease reactivation. RESULTS: The medical records of 286 patients were included. Most patients were female (64.3%), white (68.18%), and were receiving aflibercept monotherapy (55.2%). Mean number of injections at baseline was 17.79 ± 11.74 (range 3-62) with a mean treatment duration of 39.47 ± 30.68 months (range 2-139). Reactivation of AMD was identified in 32.2% of cases with 87% of recurrences identified via scheduled visit. The most common symptom was blurring of vision in 44.6%, while 39.1% were asymptomatic. Mean time from baseline to retreatment was 29.37 ± 22.40 months (range 5-104), with 20.7%, 73.9% and 88.04% of these patients requiring retreatment within 1, 3, and 5 years, respectively. CONCLUSIONS: Despite prior treatment with no reactivation in 6 months, 32.2% reactivate, 73.9% of which within 3 years. A significant proportion, 39.1%, reactivated without symptoms necessitating regular monitoring in the first 5 years.

Satellite data for environmental justice: a scoping review of the literature in the United States.

In support of the environmental justice (EJ) movement, researchers, activists, and policymakers often use environmental data to document evidence of the unequal distribution of environmental burdens and benefits along lines of race, class, and other socioeconomic characteristics. Numerous limitations, such as spatial or temporal discontinuities, exist with commonly used data measurement techniques, which include ground monitoring and federal screening tools. Satellite data is well poised to address these gaps in EJ measurement and monitoring; however, little is known about how satellite data has advanced findings in EJ or can help to promote EJ through interventions. Thus, this scoping review aims to (1) explore trends in study design, topics, geographic scope, and satellite datasets used to research EJ, (2) synthesize findings from studies that use satellite data to characterize disparities and inequities across socio-demographic groups for various environmental categories, and (3) capture how satellite data are relevant to policy and real-world impact. Following PRISMA extension guidelines for scoping reviews, we retrieved 81 articles that applied satellite data for EJ research in the United States from 2000 to 2022. The majority of the studies leveraged the technical advantages of satellite data to identify socio-demographic disparities in exposure to environmental risk factors, such as air pollution, and access to environmental benefits, such as green space, at wider coverage and with greater precision than previously possible. These disparities in exposure and access are associated with health outcomes such as increased cardiovascular and respiratory diseases, mental illness, and mortality. Research using satellite data to illuminate EJ concerns can contribute to efforts to mitigate environmental inequalities and reduce health disparities. Satellite data for EJ research can therefore support targeted interventions or influence planning and policy changes, but significant work remains to facilitate the application of satellite data for policy and community impact.

Self-supervised in-domain representation learning for remote sensing image scene classification.

Transferring the ImageNet pre-trained weights to the various remote sensing tasks has produced acceptable results and reduced the need for labeled samples. However, the domain differences between ground imageries and remote sensing images cause the performance of such transfer learning to be limited. The difficulty of annotating remote sensing images is well-known as it requires domain experts and more time, whereas unlabeled data is readily available. Recently, self-supervised learning, which is a subset of unsupervised learning, emerged and significantly improved representation learning. Recent research has demonstrated that self-supervised learning methods capture visual features that are more discriminative and transferable than the supervised ImageNet weights. We are motivated by these facts to pre-train the in-domain representations of remote sensing imagery using contrastive self-supervised learning and transfer the learned features to other related remote sensing datasets. Specifically, we used the SimSiam algorithm to pre-train the in-domain knowledge of remote sensing datasets and then transferred the obtained weights to the other scene classification datasets. Thus, we have obtained state-of-the-art results on five land cover classification datasets with varying numbers of classes and spatial resolutions. In addition, by conducting appropriate experiments, including feature pre-training using datasets with different attributes, we have identified the most influential factors that make a dataset a good choice for obtaining in-domain features. We have transferred the features obtained by pre-training SimSiam on remote sensing datasets to various downstream tasks and used them as initial weights for fine-tuning. Moreover, we have linearly evaluated the obtained representations in cases where the number of samples per class is limited. Our experiments have demonstrated that using a higher-resolution dataset during the self-supervised pre-training stage results in learning more discriminative and general representations.

DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.

Introduction: Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic methods are invasive, time-consuming, and costly. There is a need for non-invasive, efficient, and affordable solutions in diabetic foot care. Methods: We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. Additionally, deep-learning models were implemented to classify infection and ischemia in DFUs. The preliminary performance of the platform was tested on wound images acquired using a cell phone. Results: DFUCare achieved an F1-score of 0.80 and a mean Average Precision (mAP) of 0.861 for wound localization. For infection classification, we obtained a binary accuracy of 79.76%, while ischemic classification reached 94.81% on the validation set. The system successfully measured wound size and performed tissue color and textural analysis for a comparative assessment of macroscopic wound features. In clinical testing, DFUCare localized wounds and predicted infected and ischemic with an error rate of less than 10%, underscoring the strong performance of the platform. Discussion: DFUCare presents an innovative approach to wound care, offering a cost-effective, remote, and convenient healthcare solution. By enabling non-invasive and accurate analysis of wounds using mobile devices, this platform has the potential to revolutionize diabetic foot care and improve clinical outcomes through early detection of infection and ischemia.

Spectro-environmental factors integrated ensemble learning for urban river network water quality remote sensing.

Remote sensing water quality monitoring technology can effectively supplement the shortcomings of traditional water quality monitoring methods in spatiotemporal dynamic monitoring capabilities. At present, although the spectral feature-based remote sensing water quality inversion models have achieved many successes, there could still be a problem of insufficient generalization ability in monitoring the water quality of complex river networks in large cities. In this paper, we propose a spectro-environmental factors integrated ensemble learning model for urban river network water quality inversion. We analyzed the correlation between water quality parameters, spectral reflectance, and environmental factors based on an in-situ dataset collected in the northern part of Shanghai. Using the Hot Spot Analysis (Getis-Ord Gi*), we found that river network water quality parameters have different patterns in different urban functional zones. Furthermore, daily average temperature, total rainfall within the seven days, and several band combinations were also selected as the environmental and spectral features using factor analysis and Pearson correlation coefficient analysis. After the feature analysis, the spectro-environmental factors integrated ensemble learning model was trained. Compared with the spectral-based machine learning inversion models, the coefficients of determination R2 increased by about 0.50. Our model was also tested in three different test areas within and outside the in-situ sampling areas in Shanghai based on low-altitude multispectral remote sensing images. The R2 results for total phosphorus (TP), ammonia nitrogen (NH3-N), and chemical oxygen demand (COD) within the in-situ sampling areas were 0.52, 0.58, and 0.56 respectively. The mean absolute percentage error (MAPE) results were 53.36%, 63.95%, and 22.46% respectively. After adding the area outside the in-situ sampling areas, the R2 results for TP, NH3-N, and COD were 0.47, 0.47, and 0.53. The MAPE were 49.38%, 74.46%, and 20.49%. Our research provided a new remote sensing water quality inversion method to be utilized in complex urban river networks which exhibited solid accuracy and generalization ability.

A multiparametric heart failure score at baseline is associated with long-term outcome in patients with remotely monitored implantable cardioverter-defibrillators: A pooled analysis of nine clinical trials.

BACKGROUND: To predict worsening heart failure hospitalizations (WHFHs), the HeartInsight multiparametric algorithm calculates a Heart Failure (HF) Score based on temporal trends of physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators (ICDs). OBJECTIVE: We studied the association of the baseline HF Score (BHFScore), determined at algorithm activation, with long-term patient outcomes. METHODS: Data from nine clinical trials were pooled, including 1,841 ICD patients with a pre-implant ejection fraction ≤35%, NYHA class II/III, and no long-standing atrial fibrillation. Primary endpoint was a composite of death or WHFH. RESULTS: After a median follow-up of 631 days (interquartile range, 385-865), there were 243 WHFHs in 173 patients (9.4%) and 122 deaths (6.6%), 52 of which (42.6%) were cardiovascular. Primary endpoint occurred in 265 patients (14.4%). A multivariable time-to-first event analysis showed that a high BHFScore (>23, as determined by a time-dependent receiver operating characteristics curve analysis) was significantly associated with the occurrence of primary endpoint (adjusted hazard ratio [HR], 2.05; 95%-confidence interval [CI], 1.54-2.71; p<0.0001), all-cause death (HR, 2.37; CI, 1.56-3.58; p<0.0001), cardiovascular death (HR, 2.19; CI, 1.14-4.22; p=0.019), and WHFH (HR, 1.91; CI, 1.35-2.71; p=0.0003). In a hierarchical event analysis of all-cause death as the outcome with highest priority and WHFHs as repeated-event outcomes, the win-ratio was 2.47 (CI, 1.89-3.24; p<0.0001). CONCLUSIONS: Based on a retrospective analysis of clinical trial data with adjudicated events, baseline HF Score derived from device-monitored variables was able to stratify patients at higher long-term risk of death or WHFH.

Artificial intelligence based real-time prediction of imminent heart failure hospitalisation in patients undergoing non-invasive telemedicine.

Background: Remote patient management may improve prognosis in heart failure. Daily review of transmitted data for early recognition of patients at risk requires substantial resources that represent a major barrier to wide implementation. An automated analysis of incoming data for detection of risk for imminent events would allow focusing on patients requiring prompt medical intervention. Methods: We analysed data of the Telemedical Interventional Management in Heart Failure II (TIM-HF2) randomized trial that were collected during quarterly in-patient visits and daily transmissions from non-invasive monitoring devices. By application of machine learning, we developed and internally validated a risk score for heart failure hospitalisation within seven days following data transmission as estimate of short-term patient risk for adverse heart failure events. Score performance was assessed by the area under the receiver-operating characteristic (ROCAUC) and compared with a conventional algorithm, a heuristic rule set originally applied in the randomized trial. Results: The machine learning model significantly outperformed the conventional algorithm (ROCAUC 0.855 vs. 0.727, p < 0.001). On average, the machine learning risk score increased continuously in the three weeks preceding heart failure hospitalisations, indicating potential for early detection of risk. In a simulated one-year scenario, daily review of only the one third of patients with the highest machine learning risk score would have led to detection of 95% of HF hospitalisations occurring within the following seven days. Conclusions: A machine learning model allowed automated analysis of incoming remote monitoring data and reliable identification of patients at risk of heart failure hospitalisation requiring immediate medical intervention. This approach may significantly reduce the need for manual data review.

Towards full remote programming for deep brain stimulation in Parkinson's disease: A case series.

People with Parkinson's disease (PwPD) who undergo deep brain stimulation (DBS) surgery could benefit from remote programming (RP), which has proven to be both effective and economical. However, there is limited research on PwPD with DBS implants who have completed all programming sessions exclusively through remote means (full remote programming, FRP). This case report documents the experiences of five PwPD who underwent FRP, with four demonstrating improvements in motor symptoms, quality of life, and medication reduction. A total of 22 postoperative programming sessions were conducted, all via RP. FRP integrates RP with online consultations. Our findings contribute preliminary evidence supporting the feasibility and safety of FRP in the postoperative care of PwPD with DBS implants.

The Use of Remote Presence Robotic Tele-Presentation in Rural and Remote Canada: A Systematic Review.

Objective: One potential solution to limited health care in rural and remote regions is remote presence robotic tele-presentation to allow health care providers to care for patients in their home community via a robotic interface. We synthesized evidence regarding the use of remote presence robotic tele-presentation in rural and/or remote Canadian health settings. Methods: Medline, PubMed, and Embase were searched up to August 2023. Remote presence robotic tele-presentation refers to any robotic device used for the purpose of presenting and/or collecting patient information. Primary research was included if the patient was located in remote and/or rural Canada, featured remote presence robotic tele-presentation, and assessed patient, family, or clinician satisfaction, patient transport to nearby regional or urban center, health care costs, clinical outcomes, infrastructure outcomes, adverse events, or telementoring. Results: Six studies were included. Patients, nurses, and physicians all reported high levels of satisfaction when using the remote presence robotic tele-presentation. Fifty to sixty-three percent of patients were managed in their home community and did not require transfer to another center. Remote presence robotic sonography resulted in adequate imaging in 81% of first trimester ultrasound limited exams but was less useful for second trimester complete obstetric ultrasounds (20% adequate imaging). Two of eight laparoscopic colorectal surgeries had to be converted to open surgeries. Telerobotic ultrasound clinics resulted in a diagnosis in 70% of cases. Conclusions: Evidence suggests remote presence robotic tele-presentation is a safe and cost-effective approach to providing care in distant communities and can prevent some transfers and evacuations to tertiary hospitals.

Telehealth for Rare Disease Care, Research, and Education Across the Globe: A Review of the Literature by the IRDiRC Telehealth Task Force.

The International Rare Diseases Research Consortium (IRDiRC) Telehealth (TH) Task Force explored the use of TH for improving diagnosis, care, research, and education for rare diseases (RDs). The Task Force reviewed related literature published from January 2017 to August 2023, and identified various models and implementation strategies of TH for RD. The Task Force highlighted the reported values and benefits of using TH for RDs, along with the limitations and opportunities. The number of publications sharply increased since 2021, coinciding with the onset of the COVID-19 pandemic, which forced the rapid adoption of TH in many healthcare settings. One of the major benefits of TH for RDs lies in its capacity to surmount geographical barriers, which helps in overcoming the constraints posed by limited numbers and geographical dispersion of specialists. This was evident during the pandemic when TH was used to maintain a level of continued medical care and research when face-to-face visits were severely restricted. TH, through which clinical research can be decentralized, can also facilitate and enhance RD research by decreasing burden, expanding access, and enhancing efficiency. This will be especially beneficial when coupled with the adoption of digital health technologies, such as mobile health (mHealth) and wearable devices for remote monitoring (i.e., surveillance of outpatient data transmitted through devices), along with big data solutions. TH has also been shown to be an effective means for RD education and peer mentoring, enabling local health care providers (HCPs) to care for RD patients, which indirectly ensures that RD patients get the expertise and multidisciplinary care they need. However, limitations and weaknesses associated with using TH for RD care and research were also identified, including the inability to perform physical examinations and build relationships with HCPs. Therefore, TH has been recommended as a complement to, rather than substitute for, face-to-face consultations. There is also a concern that TH may lead to an amplification of health disparities and inequities related to social determinants of health for those with RDs due to lack of access to TH technologies, inadequate digital literacy, and geographical, socio-cultural, and linguistic barriers. Finally, the Task Force also discussed evidence and knowledge gaps that will benefit from future research efforts to help advance and expand the use of TH for RD care, research, and education.