bluuebunny/crossref_metadata_2025_split huggingface dataset:
Are current machine learning applications comparable to radiologist classification of degenerate and herniated discs and Modic change? A systematic review and meta-analysis
Authors: Compte Roger , Granville Smith Isabelle , Isaac Amanda , Danckert Nathan , McSweeney Terence , Liantis Panagiotis , Williams Frances M. K. 
Published: May 2023

Abstract
Introduction
Low back pain is the leading contributor to disability burden globally. It is commonly due to degeneration of the lumbar intervertebral discs (LDD). Magnetic resonance imaging (MRI) is the current best tool to visualize and diagnose LDD, but places high time demands on clinical radiologists. Automated reading of spine MRIs could improve speed, accuracy, reliability and cost effectiveness in radiology departments. The aim of this review and meta-analysis was to determine if current machine learning algorithms perform well identifying disc degeneration, herniation, bulge and Modic change compared to radiologists.

Methods
A PRISMA systematic review protocol was developed and four electronic databases and reference lists were searched. Strict inclusion and exclusion criteria were defined. A PROBAST risk of bias and applicability analysis was performed.

Results
1350 articles were extracted. Duplicates were removed and title and abstract searching identified original research articles that used machine learning (ML) algorithms to identify disc degeneration, herniation, bulge and Modic change from MRIs. 27 studies were included in the review; 25 and 14 studies were included multi-variate and bivariate meta-analysis, respectively. Studies used machine learning algorithms to assess LDD, disc herniation, bulge and Modic change. Models using deep learning, support vector machine, k-nearest neighbors, random forest and naïve Bayes algorithms were included. Meta-analyses found no differences in algorithm or classification performance. When algorithms were tested in replication or external validation studies, they did not perform as well as when assessed in developmental studies. Data augmentation improved algorithm performance when compared to models used with smaller datasets, there were no performance differences between augmented data and large datasets.

Discussion
This review highlights several shortcomings of current approaches, including few validation attempts or use of large sample sizes. To the best of the authors' knowledge, this is the first systematic review to explore this topic. We suggest the utilization of deep learning coupled with semi- or unsupervised learning approaches. Use of all information contained in MRI data will improve accuracy. Clear and complete reporting of study design, statistics and results will improve the reliability and quality of published literature.
Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting
Authors: Lupidi Marco , Danieli Luca , Fruttini Daniela , Nicolai Michele , Lassandro Nicola , Chhablani Jay , Mariotti Cesare 
Published: May 2023

Abstract
Aim
Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.

Methods
It was an observational cross-sectional study including 256 eyes of 256 consecutive patients. The sample included both diabetic and non-diabetic patients. Each patient received a 50°, macula centered, non-mydriatic fundus photography and, after pupil dilation, a complete fundus examination by an experienced retina specialist. All images were after analyzed by a skilled operator and by the AI algorithm. The results of the three procedures were then compared.

Results
The agreement between the operator-based fundus analysis in bio-microscopy and the fundus photographs was of 100%. Among the DR patients the AI algorithm revealed signs of DR in 121 out of 125 subjects (96.8%) and no signs of DR 122 of the 126 non-diabetic patients (96.8%). The sensitivity of the AI algorithm was 96.8% and the specificity 96.8%. The overall concordance coefficient k (95% CI) between AI-based assessment and fundus biomicroscopy was 0.935 (0.891–0.979).

Conclusions
The Aurora fundus camera is effective in a first line screening of DR. Its in-built AI software can be considered a reliable tool to automatically identify the presence of signs of DR and therefore employed as a promising resource in large screening campaigns.
Enabling same-day delivery using a drone resupply model with transshipment points
Authors: Moshref-Javadi Mohammad , Van Cauwenberghe Kristof P. , McCunney Brent A. , Hemmati Ahmad 
Published: May 2023

AbstractWe study a hybrid logistics delivery model which incorporates trucks and drones. In this model, named the Drone Resupply Model with Transshipment Points (DRMTP), drones are used to resupply transshipment points where trucks visit to pick up packages for delivery to customers. A simulation model is developed to simulate the delivery operations. The model is evaluated in two case studies in an urban area and a suburban area in Massachusetts, USA. Using this resupply model, the average delivery time is reduced compared to the conventional truck-only delivery model. The experimental results in the suburban region show that the DRMTP model with one transshipment point achieves 36% lower delivery time compared to the truck-only model. In the urban region, the model with four transshipment points obtains 66% delivery time reduction compared to the truck-only model. The results indicate that the proposed model can be used to enhance the efficiency of the last-mile delivery operations.
Cortical and Subcortical Changes in MEG Activity Reflect Parkinson’s Progression over a Period of 7 Years
Authors: Boon Lennard I. , Hillebrand Arjan , Schoonheim Menno M. , Twisk Jos W. , Stam Cornelis J. , Berendse Henk W. 
Published: May 2023

AbstractIn this study of early functional changes in Parkinson’s disease (PD), we aimed to provide a comprehensive assessment of the development of changes in both cortical and subcortical neurophysiological brain activity, including their association with clinical measures of disease severity. Repeated resting-state MEG recordings and clinical assessments were obtained in the context of a unique longitudinal cohort study over a seven-year period using a multiple longitudinal design. We used linear mixed-models to analyze the relationship between neurophysiological (spectral power and functional connectivity) and clinical data. At baseline, early-stage (drug-naïve) PD patients demonstrated spectral slowing compared to healthy controls in both subcortical and cortical brain regions, most outspoken in the latter. Over time, spectral slowing progressed in strong association with clinical measures of disease progression (cognitive and motor). Global functional connectivity was not different between groups at baseline and hardly changed over time. Therefore, investigation of associations with clinical measures of disease progression were not deemed useful. An analysis of individual connections demonstrated differences between groups at baseline (higher frontal theta, lower parieto-occipital alpha2 band functional connectivity) and over time in PD patients (increase in frontal delta and theta band functional connectivity). Our results suggest that spectral measures are promising candidates in the search for non-invasive markers of both early-stage PD and of the ongoing disease process.
Oridonin attenuates the progression of atherosclerosis by inhibiting NLRP3 and activating Nrf2 in apolipoprotein E-deficient mice
Authors: Wang Lei , Zhao Xiaoqi , Ding Jiawen , Liu Yutong , Liu Han , Zheng Lei , Zhao Hongting , Sun Zichen , Li Kuanyu , Cai Jing , Qiao Tong 
Published: May 2023

AbstractOridonin, a well-known traditional Chinese herbal medicinal product isolated from Isodon rubescens (Hemsl.) H.Hara, has many potential properties, including anti-inflammatory and antioxidant activities. However, there is no evidence whether oridonin have a protective effect on atherosclerosis. This study focused on the effects of oridonin on oxidative stress and inflammation generated from atherosclerosis. The therapeutic effect on atherosclerosis was evaluated by intraperitoneal injection of oridonin in a high-fat fed ApoE−/− mouse model. We isolated mouse peritoneal macrophages and detected the effect of oridonin on oxidized low-density lipoprotein-induced lipid deposition. Oil red O staining, Masson's staining, dihydroethidium fluorescence staining, immunohistochemical staining, western blotting analysis, immunofluorescence, enzyme-linked immunosorbent assay and quantitative real-time PCR were used to evaluate the effect on atherosclerosis and explore the mechanisms. Oridonin treatment significantly alleviated the progression of atherosclerosis, reduced macrophage infiltration and stabilized plaques. Oridonin could significantly inhibit inflammation associated with NLRP3 activation. Oridonin significantly reduced oxidative stress by blocking Nrf2 ubiquitination and degradation. We also found that oridonin could prevent the formation of foam cells by increasing lipid efflux protein and reducing lipid uptake protein in macrophages. Oridonin has a protective effect on atherosclerosis in ApoE−/− mice, which may be related to the inhibition of NLRP3 and the stabilization of Nrf2. Therefore, oridonin may be a potential therapeutic agent for atherosclerosis.
Guilt, Shame and Academic Misconduct
Authors: Curtis Guy J. 
Published: May 2023

AbstractMoral and self-conscious emotions like guilt and shame can function as internal negative experiences that punish or deter bad behaviour. Individual differences exist in people’s tendency to experience guilt and shame. Being disposed to experience guilt and/or shame may predict students’ expectations of their emotional reactions to engaging in immoral behaviour in the form of academic misconduct, and thus dissuade students from intending to engage in this behaviour. In this study, students’ (n = 459) guilt and shame proneness, their expectations of feeling guilt and shame if they engaged in academic misconduct, and their intentions to engage in academic misconduct were measured. Three of the four facets of the guilt and shame proneness scale [GASP: Guilt–Negative-Behavior-Evaluation (NBE), Guilt-Repair, Shame–Negative-Self-Evaluation (NSE)] had significant negative correlations with academic misconduct intentions, and these relationships were mediated by anticipating shame and guilt related to engaging in academic misconduct. These results suggest that for some students expecting to experience negative moral emotions when engaging in academic misconduct may protect them from breaching ethical assessment rules.
Determining the degree of crystallization of aripiprazole during cold crystallization using thermo-optical analysis and differential scanning calorimetry
Authors: Osiecka-Drewniak N. , Juszyńska-Gałązka Ewa 
Published: May 2023

AbstractThe full characteristic of the crystallization process of aripiprazole crystal is given. Our research reveals that a degree of crystallinity calculated from the analyses of data of light intensity passing through the sample (Thermo-Optical Analysis based on Polarized Light Microscopy—POM), the Canny edge detection algorithm, and Differential Scanning Calorimetry—DSC, are in good agreement. The parameters of Avrami model, the dimensionality of the crystal growth, and the characteristic crystallization temperature, obtained from POM and DSC methods coincide. The obtained values of the Avrami exponent n suggest that the crystal growth is three-dimensional.
RETRACTED ARTICLE: MiR-125 family improves the radiosensitivity of head and neck squamous cell carcinoma
Authors: Wang Qi-Wei , Sun Ya-Nan , Tan Li-Jun , Zhao Jian-Nan , Zhou Xiao-Jie , Yu Tian-Jiao , Liu Jiang-Tao 
Published: May 2023

Abstract
Background
MiRNAs can affect the radiosensitization of head and neck squamous cell carcinoma (HNSCC). We aimed to analyze the function of miR-125 family members in HNSCC using The Cancer Genome Atlas (TCGA) and determine their effect on radiation in laryngeal squamous cell cancer (LSCC).

Methods
First, we systematically analyzed the role of the miR-125 family in HNSCC using the TCGA database and found that miR-125a-5p is associated with radiotherapy. We then performed comprehensive enrichment analysis of miR-125a-5p and predicted target genes. Then, we performed transfection, cell proliferation assays, reverse transcription polymerase chain reaction, apoptosis assays, micronucleus tests, and western blotting on hep-2 cells selected with puromycin.

Results
MiR-125 family members exhibited significantly different expression in HNSCC. They were significantly associated with tumor–node–metastasis staging, clinical stages, and histological grades. Radiation therapy had a statistically effect on miR-125 family members, except miR-125a-3p. Moreover, miR-125a-5p was related to overall survival in LSCC. Thus, we predicted 110 target genes and seven hub genes of miR-125a-5p. The proliferation rate of cells transfected with lentivirus vector expressing miR-125a-5p was significantly reduced compared to the other groups. The radiation effect was enhanced in cells transfected with miR-125a-5p. The ratio of apoptotic cells transfected and exposed to X-rays (10 Gy) was distinctly higher than that of the Ad-control group. Western blotting analysis revealed that miR-125a-5p upregulated the apoptotic regulators P53 and rH2AX. Thus, miR-125a-5p may increase radiosensitivity in LSCC via upregulation of pro-apoptotic genes.

Conclusions
MiR-125 family members could be prognostic biomarkers of HNSCC and improve HNSCC sensitivity to radiotherapy by activating P53. Upregulating miR-125a-5p via lentivirus vectors may be a novel strategy to strengthen the effect of radiotherapy on LSCC.
Versatile end effector for laparoscopic robotic scrub nurse
Authors: Wagner Lars , Kolb Sven , Looschen Christian , Bernhard Lukas , Fuchtmann Jonas , Berlet Maximilian , Fottner Johannes , Knoll Alois , Wilhelm Dirk 
Published: May 2023

Abstract
Purpose
Integrating robotic scrub nurses in the operating room has the potential to help overcome staff shortages and limited use of available operating capacities in hospitals. Existing approaches of robotic scrub nurses are mainly focused on open surgical procedures, neglecting laparoscopic procedures. Laparoscopic interventions offer great potential for the context-sensitive integration of robotic systems due to possible standardization. However, the first step is to ensure the safe manipulation of laparoscopic instruments.

Methods
A robotic platform with a universal gripper system was designed to pick up and place laparoscopic as well as da Vinci$$^{\circledR }$$

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instruments in an efficient workflow. The robustness of the gripper system was studied using a test protocol, which included a force absorption test to determine the operational safety limits of the design and a grip test to determine the system performance.

Results
The test protocol shows results regarding force and torque absorption capabilities of the end effector, which are essential when transferring an instrument to the surgeon to enable a robust handover. The grip tests show that the laparoscopic instruments can be safely picked up, manipulated and returned independent of unexpected positional deviations. The gripper system also enables the manipulation of da Vinci$$^{\circledR }$$

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instruments, opening the door for robot–robot interaction.

Conclusion
Our evaluation tests have shown that our robotic scrub nurse with the universal gripper system can safely and robustly manipulate laparoscopic and da Vinci$$^{\circledR }$$

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instruments. The system design will continue with the integration of context-sensitive capabilities.
Novel Approaches in the Management of Mucormycosis
Authors: Lamoth Frederic 
Published: May 2023

Abstract
Purpose of Review
Invasive mucormycosis (IM), caused by fungi of the order Mucorales, is one of the deadliest fungal infection among hematologic cancer patients. Its incidence is also increasingly reported in immunocompetent individuals, notably with the COVID-19 pandemic. Therefore, there is an urgent need for novel diagnostic and therapeutic approaches of IM. This review discusses the current advances in this field.

Recent Findings
Early diagnosis of IM is crucial and can be improved by Mucorales-specific PCR and development of lateral-flow immunoassays for specific antigen detection. The spore coat proteins (CotH) are essential for virulence of the Mucorales and may represent a target for novel antifungal therapies. Adjuvant therapies boosting the immune response, such as interferon-γ, anti-PDR1 or fungal-specific chimeric antigen receptor (CAR) T-cells, are also considered.

Summary
The most promising perspectives for improved management of IM consist of a multilayered approach targeting both the pathogen and the host immune system.
Does enrolling in finance-related majors improve financial habits? A case study of China’s college students
Authors: Huang Wei , Liao Xiaowei , Li Fan , Yao Panpan 
Published: May 2023

AbstractThere is a growing awareness of the importance of financial management skills. However, few studies have systematically examined this topic in China’s current higher education (HE) system. In this study, we examine whether enrolling in a finance-related program, either as a degree program or as an elective, improves students’ financial literacy and habits. We conducted a mixed-method study with a sample of 1334 students from seven universities in central China. Multivariate regression, matching methods, and the inverse–probability–weighting methods were used for quantitative analysis. The results indicate that college students are generally dissatisfied with their financial situation. Enrolling in finance-related majors has little effect on improving students’ financial habits, despite a substantial improvement in their financial literacy. In the current HE system, there is a significant lack of personal finance management training. Therefore, curriculum design and field practices regarding students’ personal finance management skills should be introduced to enhance their financial habits.
Formative assessment in maritime simulator-based higher education
Authors: Karahalil Meric , Lützhöft Margareta , Scanlan Joel 
Published: May 2023

AbstractAssessment in maritime simulator-based education has traditionally been informal and subjective based on instructor experience. Recent research suggests that a more objective and formal approach could be beneficial. Formative assessment has attracted significant attention from higher education institutions. Thus, this study aimed to examine the current practice of formative assessment in maritime simulator-based education, with a special emphasis on simulator instructors’ teaching methods. A qualitative approach with observations and interviews was used in the study since it could provide deep insights into educators’ motivations and understandings of how to meet the educational needs of their students. The findings inform the formative assessment strategies may explicitly enhance maritime education.
A spatial–spectral adaptive learning model for textile defect images recognition with few labeled data
Authors: Zhang Yuan , Han Tao , Wei Bing , Hao Kuangrong , Gao Lei 
Published: May 2023

AbstractTextile defect recognition is a significant technique in the production processes of the textile industry. However, in the practical processes, it is hard to acquire large amounts of textile defect samples. Meanwhile, the textile samples with correct defect labels are rare. To address these two limitations, in this paper, we propose a novel semi-supervised graph convolutional network for few labeled textile defect recognition. First, we construct the graph convolutional network and convolution neural network to extract spectral features and spatial features. Second, the adaptive convolution structure is proposed to generate adaptive kernels according to their dynamically learned features. Finally, the spatial–spectral adaptive unified learning network (SSA-ULNet) is built for limited labeled defective samples, and graph-based semi-supervised learning is constructed. The textile defect recognition model can extract the textile image features through the image descriptors, enabling the whole network to be end-to-end trainable. To evaluate the proposed method, one public dataset and two unique self-built textile defect datasets are used to textile defect recognition. The evaluation results demonstrate that the proposed SSA-ULNet obviously outperforms existing state-of-the-art deep learning methods.
Learning spatio-temporal discriminative model for affine subspace based visual object tracking
Authors: Xu Tianyang , Zhu Xue-Feng , Wu Xiao-Jun 
Published: May 2023

AbstractDiscriminative correlation filters (DCF) with powerful feature descriptors have proven to be very effective for advanced visual object tracking approaches. However, due to the fixed capacity in achieving discriminative learning, existing DCF trackers perform the filter training on a single template extracted by convolutional neural networks (CNN) or hand-crafted descriptors. Such single template learning cannot provide powerful discriminative filters with guaranteed validity under appearance variation. To pinpoint the structural relevance of spatio-temporal appearance to the filtering system, we propose a new tracking algorithm that incorporates the construction of the Grassmannian manifold learning in the DCF formulation. Our method constructs the model appearance within an online updated affine subspace. It enables joint discriminative learning in the origin and basis of the subspace, achieving enhanced discrimination and interpretability of the learned filters. In addition, to improve tracking efficiency, we adaptively integrate online incremental learning to update the obtained manifold. To this end, specific spatio-temporal appearance patterns are dynamically learned during tracking, highlighting relevant variations and alleviating the performance degrading impact of less discriminative representations from a single template. The experimental results obtained on several well-known datasets, i.e., OTB2013, OTB2015, UAV123, and VOT2018, demonstrate the merits of the proposed method and its superiority over the state-of-the-art trackers.