bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gaps and future of human-centered artificial intelligence in ophthalmology: Future Vision Forum consensus statement
Authors: Ting Daniel Shu Wei , Humayun Mark S. , Huang Suber S. 
Published: July 2023

Purpose of review
The Future Vision Forum discussed the current state of Human Centered Computing and the future of data collection, curation, and collation in ophthalmology. Although the uptake of electronic health record (EHR) systems and the digitization of healthcare data is encouraging, there are still barriers to implementing a specialty-wide clinical trial database. The article identifies several critical opportunities, including the need for standardization of image metadata and data, the establishment of a centralized trial database, incentives for clinicians and trial sponsors to participate, and resolving ethical concerns surrounding data ownership.

Findings
Recommendations to overcome these challenges include the standardization of image metadata using the Digital Imaging and Communications in Medicine (DICOM) guidelines, the establishment of a centralized trial database that uses federated learning (FL), and the use of FL to facilitate cross-institutional collaboration for rare diseases. Forum faculty suggests incentives will accelerate artificial intelligence, digital innovation projects, and data sharing agreements to empower patients to release their data.

Summary
A specialty-wide clinical trial database could provide invaluable insights into the natural history of disease, pathophysiology, why trials fail, and improve future clinical trial design. However, overcoming the barriers to implementation will require continued discussion, collaboration, and collective action from stakeholders across the ophthalmology community.
Toward safer ophthalmic artificial intelligence via distributed validation on real-world data
Authors: Nath Siddharth , Rahimy Ehsan , Kras Ashley , Korot Edward 
Published: July 2023

Purpose of review
The current article provides an overview of the present approaches to algorithm validation, which are variable and largely self-determined, as well as solutions to address inadequacies.

Recent findings
In the last decade alone, numerous machine learning applications have been proposed for ophthalmic diagnosis or disease monitoring. Remarkably, of these, less than 15 have received regulatory approval for implementation into clinical practice. Although there exists a vast pool of structured and relatively clean datasets from which to develop and test algorithms in the computational ‘laboratory’, real-world validation remains key to allow for safe, equitable, and clinically reliable implementation. Bottlenecks in the validation process stem from a striking paucity of regulatory guidance surrounding safety and performance thresholds, lack of oversight on critical postdeployment monitoring and context-specific recalibration, and inherent complexities of heterogeneous disease states and clinical environments. Implementation of secure, third-party, unbiased, pre and postdeployment validation offers the potential to address existing shortfalls in the validation process.

Summary
Given the criticality of validation to the algorithm pipeline, there is an urgent need for developers, machine learning researchers, and end-user clinicians to devise a consensus approach, allowing for the rapid introduction of safe, equitable, and clinically valid machine learning implementations.
Platelet mitochondria: the mighty few
Authors: Ajanel Abigail , Campbell Robert A. , Denorme Frederik 
Published: July 2023

Purpose of review
Platelet mitochondrial dysfunction is both caused by, as well as a source of oxidative stress. Oxidative stress is a key hallmark of metabolic disorders such as dyslipidemia and diabetes, which are known to have higher risks for thrombotic complications.

Recent findings
Increasing evidence supports a critical role for platelet mitochondria beyond energy production and apoptosis. Mitochondria are key regulators of reactive oxygen species and procoagulant platelets, which both contribute to pathological thrombosis. Studies targeting platelet mitochondrial pathways have reported promising results suggesting antithrombotic effects with limited impact on hemostasis in animal models.

Summary
Targeting platelet mitochondria holds promise for the reduction of thrombotic complications in patients with metabolic disorders. Future studies should aim at validating these preclinical findings and translate them to the clinic.
Critical Care Unit Characteristics and Extracorporeal Cardiopulmonary Resuscitation Survival in the Pediatric Cardiac Population: Retrospective Analysis of the Virtual Pediatric System Database
Authors: Lasa Javier J. , Guffey Danielle , Bhalala Utpal , Thiagarajan Ravi R. 
Published: July 2023

OBJECTIVES:
Existing literature provides limited data about ICU characteristics and pediatric extracorporeal cardiopulmonary resuscitation (E-CPR) outcomes. We aimed to evaluate the associations between patient and ICU characteristics, and outcomes after E-CPR in the pediatric cardiac population.

DESIGN:
Retrospective analysis of the Virtual Pediatric System database (VPS, LLC, Los Angeles, CA).

SETTING:
PICUs categorized as either cardiac-only versus mixed ICU cohort type.

PATIENTS:
Consecutive cardiac patients less than 18 years old experiencing cardiac arrest in the ICU and resuscitated using E-CPR.

INTERVENTIONS:
None.

MEASUREMENTS AND MAIN RESULTS:
Event and time-stamp filtering identified E-CPR events. Patient, hospital, and event-related variables were aggregated for independent and multivariable mixed effects logistic regression to assess the association between ICU cohort type and survival. Among ICU admissions in the VPS database, 2010–2018, the prevalence of E-CPR was 0.07%. A total of 671 E-CPR events (650 patients) comprised the final cohort; congenital heart disease (84%) was the most common diagnosis versus acquired heart diseases. The majority of E-CPR events occurred in mixed ICUs (67%, n = 449) and in ICUs with greater than 20 licensed bed capacity (65%, n = 436). Survival to hospital discharge was 51% for the overall cohort. Independent logistic regression failed to reveal any association between survival to hospital discharge and ICU type (ICU type: cardiac ICU, odds ratio [OR], 1.01; 95% CI, 0.71–1.44; p = 0.95). However, multivariable logistic regression revealed an association between cardiac surgical patients and greater odds for survival (OR, 2.03; 95% CI, 1.40–2.95; p < 0.001). Also, there was an association between ICUs with capacity greater than 20 (vs not) and lower survival odds (OR, 0.65; 95% CI, 0.43–0.96).

CONCLUSIONS:
The overall prevalence of E-CPR among critically ill children with cardiac disease observed in the VPS database is low. We failed to identify an association between ICU cohort type and survival. Further investigation into organizational factors is warranted.
Reboxetine Combination Therapy With Fluoxetine in Moderate to Severe Obsessive-Compulsive Disorder: A Placebo-Controlled, Double-Blind, Randomized Trial
Authors: Tilaki Erfaneh Hajian , Hasanzadeh Alireza , Shalbafan Mohammadreza , Moghaddam Hossein Sanjari , Shamabadi Ahmad , Boroon Mahsa , Akhondzadeh Shahin 
Published: July 2023

Objectives
Reboxetine is a potent and selective norepinephrine reuptake inhibitor that was effective in combination with citalopram for resistant obsessive-compulsive disorder (OCD). This study aims to assess its effectiveness and tolerability in combination with fluoxetine in treating OCD.

Methods
In this 2-center, placebo-controlled, and double-blind, randomized clinical trial, 76 patients with OCD were assigned into 2 parallel groups to receive fluoxetine (up to 80 mg/d) plus placebo (F + P) or fluoxetine (up to 80 mg/d) plus reboxetine (F + R) (10 mg twice daily) for 10 weeks. Participants were assessed with the Yale-Brown Obsessive Compulsive Scale (Y-BOCS) at baseline and weeks 5 and 10.

Results
A total of 76 patients completed the trial. There was no significant difference between the 2 groups in baseline Y-BOCS scores. General linear model repeated-measures showed significant effects on time-treatment interaction on total Y-BOCS (F = 6.33, df = 1.42, P = 0.006) and obsession subscale scores (F = 10.39, df = 1.48, P < 0.001), and insignificance on compulsion subscale scores (F = 1.86, df = 1.24, P = 0.173). Reboxetine combination therapy demonstrated a higher partial and complete treatment response rate (P < 0.01) according to the Y-BOCS total scores. There was no significant difference between the 2 groups in the frequency of adverse effects.

Conclusions
Reboxetine combination therapy with fluoxetine can effectively improve symptoms in patients with OCD in a short period of treatment. However, further studies with larger sample sizes and longer follow-up periods are needed to confirm these findings.
This trial was registered with the Iranian Registry of Clinical Trials (www.irct.ir; No IRCT20090117001556N129).
Impaired Retinal Capillary Function in Patients With Alzheimer Disease
Authors: Cipolla Jack A. , Jiang Hong , Simms Ava-Gaye Y. , Baumel Bernard , Rundek Tatjana , Wang Jianhua 
Published: July 2023

Background:Extensive evidence indicates that vasculopathy, especially the level of microcirculation, contributes to neurodegeneration in Alzheimer disease (AD). However, it is not easy to directly monitor cerebral microcirculation. The retinal microvasculature has been proposed as a surrogate measure to study cerebral vascular changes. Indeed, decreased retinal microvascular network densities were reported in patients with AD. We sought to determine the retinal capillary function (RCF, the efficiency of blood flow transferring in the capillary network) in patients with AD.Methods:Twenty patients (age 60–84 years, mean ± SD: 72.8 ± 7.7 years) with AD and 14 age-matched cognitively normal controls (CN, age 62–81 years, mean ± SD: 68.6 ± 6.7 years.) were recruited. There were no differences in vascular risk factors, including smoking, hypertension, hyperlipidemia, Type 2 diabetes, and cardiovascular disease, between the groups. One eye of each subject in both groups was imaged. Retinal blood flow (RBF) was measured using a retinal function imager, and retinal capillary density (RCD, expressed as fractal dimension Dbox) was measured using optical coherence tomography angiography. RCF was defined as the ratio of RBF to RCD.Results:RCF was 1.62 ± 0.56 nl/s/Dbox (mean ± SD) in the AD group, which was significantly lower than that (2.56 ± 0.25 nl/s/Dbox,P< 0.01) in the CN group. The change of RCF in the AD group represented 28% lower than in the CN group. RCF was significantly and positively correlated with RBF in the AD group (r = 0.98,P< 0.05) and in the CN group (r = 0.65,P< 0.05).Conclusions:Our study is the first to demonstrate impaired retinal capillary function in patients with AD. The alteration of RCF was mainly due to decreased retinal blood flow, which is transferred by the capillary network. The RCF may be developed as a biomarker of impaired cerebral microcirculation in patients with AD.
Pavement crack detection based on point cloud data and data fusion
Authors: Dong Qiao , Wang Sike , Chen Xueqin , Jiang Wanqi , Li Ruiqi , Gu Xingyu 
Published: July 2023

The three-dimensional detection in point cloud data for pavement cracks has drawn the attention of many researchers recently. In the field of pavement surface point cloud detection, the key tasks include the identification of pavement cracks and the extraction of the location and size information of pavement cracks. Based on the point cloud data of pavement surface, we developed two methods to directly extract and detect cracks, respectively. The first method is based on the improved sliding window algorithm by combining the random sample consensus (RANSAC) technique to directly extract the crack information from point clouds. The second method is developed based on YOLOv5 to process the two-dimensional images transformed from point cloud data for automatic pavement crack detection. We also attempted to fuse the point cloud images with greyscale images as input for the YOLOv5. Analysis results show that the improved sliding window algorithm efficiently extracts pavement cracks with less noise, and the YOLOv5-based method obtains a good detection of pavement cracks.This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
Falling weight deflectometer dispersion curve method for pavement modulus calculation
Authors: Wang Xue , Huang Hai , Zhang Kun , Shen Shihui 
Published: July 2023

The falling weight deflectometer (FWD) test is a common non-destructive testing method for evaluating the structural capacity of pavements. At present, data processing of the FWD test mainly focuses on the deflection data, while paying less attention to the deflection-time history. Because a FWD is equipped with impulse loads and geophones, which allow for the generation and capture of surface wave signal propagation, it is hypothesized that Rayleigh wave dispersion theory can be applied to calculate the modulus profile along the pavement depth by analysing the dispersive properties of the deflection signal measured during FWD tests. To test this hypothesis, we develop a new methodology for the FWD test and data analysis, referred to as the FWD dispersion curve method. We first introduce the concept of the new method, followed by an illustration of the procedure and the experimental set-up. Case studies on three concrete pavement segments are then presented to evaluate the effectiveness of the FWD dispersion curve method. Modifications to the existing FWD device are further recommended for the impact loading sources and signal collection process so that the modulus of a much shallower layer, such as the concrete slab and upper asphalt layers, can be obtained.
This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Machine learning-based identification of segment joint failure in underground tunnels
Authors: Jin Zhisheng , Yan Zihai , Fu Haoran , Bian Xuecheng 
Published: July 2023

Shield tunnels that reside deep within soft soil are subject to longitudinal differential settlement and structural deformation during long-term operation. Longitudinal deformation can be classified into two modes: bending and dislocation deformation. The failure of bolts and engineering treatment techniques differ between these two modes. Therefore, it is imperative to accurately identify the tunnel's longitudinal deformation mode to determine the validity of the segment joint and implement appropriate engineering treatment. Traditional methods for detecting dislocation or opening suffer from high labour costs. To address this issue, this study presents an innovative identification method using a back-propagation neural network (BPNN) to detect segment joint failure in underground tunnels. First, this study collects the tunnel settlement curves of various subways located in the East China soft soil area, and it calculates tunnel settlement-dislocation and settlement-opening datasets using the equivalent axial stiffness model. A corresponding BPNN regression model is subsequently established, and the new settlement curve is the input to this regression model to predict the dislocation and opening, thereby determining the validity of the segment joint. The efficiency of this method is demonstrated through its successful application to the Hangzhou Metro Tunnel.
This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Machine learning-based rail corrugation recognition: a metro vehicle response and noise perspective
Authors: Cai Xiaopei , Tang Xueyang , Chang Wenhao , Wang Tao , Lau Albert , Chen Zhipei , Qie Luchao 
Published: July 2023

Rail corrugation is a common problem in metro lines, and its efficient recognition is always an issue worth studying. To recognize the wavelength and amplitude of rail corrugation, a particle probabilistic neural network (PPNN) algorithm is developed. The PPNN is incorporated with the particle swarm optimization algorithm and the probabilistic neural network. On the basis of the above, the in-vehicle noise characteristics measured in the field are used to recognize normal rail wavelengths of 30 and 50 mm. A stepwise moving window search algorithm suitable for selecting features with a fixed order was developed to select in-vehicle noise features. Sound pressure levels at 400, 500, 630 and 800 Hz of in-vehicle noise are fed into the PPNN, and the average accuracy can reach 96.43%. The bogie acceleration characteristics calculated by the multi-body dynamics simulation model are used to recognize normal rail amplitudes of 0.1 and 0.2 mm. The bogie acceleration is decomposed by the complete ensemble empirical mode decomposition with adaptive noise, and a reconstructional signal is obtained. The energy entropy of the reconstructional signal is fed into the PPNN, and the average accuracy can reach 95.40%.
This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)
Authors: Ma Kaiyue , Huang Shushu , Ng Kenneth K. , Lake Nicole J. , Joseph Soumya , Xu Jenny , Lek Angela , Ge Lin , Woodman Keryn G. , Koczwara Katherine E. , Cohen Justin , Ho Vincent , O’Connor Christine L. , Brindley Melinda A. , Campbell Kevin P. , Lek Monkol 
Published: July 2023

SummaryInterpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genesFKRPandLARGE1, we generated functional scores for all possible coding single nucleotide variants, which aid in resolving clinically reported variants of uncertain significance. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.
Occupational dermatoses in health care personnel using PPE during the COVID pandemic
Authors: Asati Dinesh P , Baheti Kapil , Kaur Maninder , Patra Suman , Singhal Kritika 
Published: July 2023

AbstractBackgroundA sudden surge of occupation-associated dermatoses among the healthcare workers (HCWs) serving COVID-19 patients have been witnessed recently due to increased usage of PPE (PPE) kits and increased frequency of hygiene practices, with a significant impact on their quality of life and compromised efficacy at work. Hence, this study was conducted to measure the prevalence of occupational dermatoses among HCWs serving Covid-19 patients using PPE kits and hygiene practices and their impact on quality of life.MethodsHCWs of all cadres were screened for occupation-associated dermatoses. Cases with occupational dermatosis were further evaluated regarding using a PPE kit, and DLQI was calculated.Results19% of HCWs had dermatoses associated with PPE and hygiene practices. Hands were most affected, followed by the face, nasal bridge, and facial skin in contact with goggles. 48% had Mathias score >/= 4. Most cases had reported some impact on their quality of life. A significant association could be established between frequency of hand washing >/= 10 times/day with hand dermatitis (p=0.000).ConclusionThe use of PPE has significantly raised cases of occupational dermatosis among HCWs. Repeated hand washing and hand sanitizer use has increased the incidence of hand dermatitis.
Clinical manifestations and mortality among hospitalized COVID-19 patients in Tanzania, 2021-2022
Authors: Osati Elisha , Shayo Grace A. , Nagu Tumaini , Sangeda Raphael Z , Moshiro Candida , Adams Naveeda , Ramadhani Athumani , Wanja Bahati , Muniko Albert , Seni Jeremia , Nicholaus Mary A. , Kilonzo Kajiru G , Nyaisonga Gervas , Mbije Christian , Meda John , Rainer Denis , Nkya Martha , Mhame Paulo , Samwel Lucy , Vumilia Liggyle , Shekalaghe Seif , Makubi Abel 
Published: July 2023

BackgroundThere have been differential mortality rates from Corona Virus Disease of 2019 (COVID-19) in different parts of the world. It is not clear whether the clinical presentation does also differ, thus the need for this study in a Sub-Saharan African country. The aim of this study was to describe clinical manifestations and outcome of patients diagnosed with COVID-19 in selected tertiary hospitals in Tanzania.Methods and FindingsA retrospective analysis of archived data from 26thMarch, 2021 to 30thSeptember, 2022 was done for adults aged ≥18 years who were admitted in five tertiary-level hospitals in Tanzania. Information collected included socio-demographic, radiological and clinical characteristics of the patients as well as outcome of the admission (discharge vs death). Categorical variables were presented as frequencies and proportions and compared using Chi square test. Logistic regression was used to assess the relationship between COVID-19 mortality and the collected variables. Out of 1387 COVID-19 patients, approximately 52% were males. The median age was 60 years [(IQR)= (19-102)). The most common symptoms were dyspnea (943,68%), cough (889, 64%), fever (597,43%) and fatigue (570, 41%). In hospital mortality was (476, 34%). Mortality significantly increased with increasing age, being the most in age >90 years [aOR (95% CI) =6.72 (1.94-20.81), P<0.001. Other predictors of death were not possessing a health insurance, [aOR (95% CI) = 2.78 (2.09-3.70), P<0. 001], dyspnea [aOR (95% CI) = 1.40(1.02-2.06), P=0.03]; chest pain, [aOR (95% CI) = 1.78 (1.12-3.21), P=0.03]; HIV positivity, [aOR (95% CI) = 4.62 (2.51-8.73), P<0.001]; neutrophilia, [aOR (95% CI) = 1.02 (1.01 – 1.03), P=0.02]; none use of ivermectin, [aOR (95% CI) = 1.46 (1.09 – 2.22), P=0.02] and non-use of steroid, [aOR (95% CI) = 1.40 (1.2 – 2.5), P=0.04]. Retrospective nature of this study which based on documented patients records, with a large number of patients left out of the analysis due to missed data, this might in a way affect the results of the present study.ConclusionsThe most common presenting symptoms were dyspnea, cough and fever, just as what was common elsewhere in the world. Mortality increased significantly with age, in HIV-infected patients, in those without a health insurance, those presenting with dyspnea, chest pain, or neutrophilia and those who did not use steroid or ivermectin. Clinicians should actively look for the predictors of mortality and take appropriate management to reduce mortality.