bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep and Transfer Learning based methods for Left Ventricle segmentation from cardiac MRI images to identify cardiovascular ailments
Authors: Bhan Anupama , Mangipudi Parthasarathi , Goyal Ayush 
Published: April 2023

Abstract
In order to diagnose cardiovascular disease (CVD) in its early stages, the position of the Left Ventricle (LV) and relevant parameters associated with it plays significant role in the medical field. The timely diagnosis of CVDs works as a lifesaver in many cases. In the earlier days, the position and functioning of the LV was assessed by the error-prone manual methods. Nowadays, the newer and smart technologies have allowed the medical practitioners to make use of auto-segmentation methods for diagnosis of heart problems in early stages. It is difficult to assess the functioning of the LV due to some listed reasons a) a bigger span and changing the size of LV in MRI scanning , b) Varied myocardial and blood-pool fragments, c) Similarity in shape between the LV and other body organs and d) Noise in images. Hence assessing the LV for the accurate identification of echocardiographic parameters still remains the challenge for diagnosing CVDs. Many researchers deploy methods based on Machine learning (ML) and deep learning (DL) to get accurate results for LV segmentation (LVS0. It helps in segmenting the LV and revealing the clearer parts of the image for better classification and diagnosis. In this research study, three methods are deployed for the segmentation of LV images namely CNN based U-Net Model , VGG 16 and ResNet 152. These methods have been implemented for the segmentation of the images obtained from MRI scan to explore the position of left ventricle and problems in LV which leads to other lethal heart ailments. These approaches help in the identification of cardiac parameters related to CVDs. The proposed algorithms are compared using standard performance metrics to assess the output and viability of the projected techniques as discussed in the result section of this article. The Blockchain database has been considered as the source of input images and this research is applicable universally due to its nature of considering latest technologies to identify CVDs. The results of DL algorithms (DLA) reveal that the CNN-based U-Net Model outperforms the other two methods (VGG 16 and ResNet 152) for accurate identification of CVDs from the LV segmentation techniques.
Mechanisms of BMPR2 in the Development and Progression of Osteoporosis: An Experimental Study in Mice
Authors: Yingzhu Qiu , Yao Sun , Kun Sun 
Published: April 2023

Abstract
Introduction: Osteoporosis is a bone disease characterized by reduced bone mass, disrupted microarchitecture, and deteriorated bone strength, leading to increased fracture risk. The dysfunction of bone cells is associated with various factors, making it important to study the regulatory mechanisms of bone cells for prevention and treatment of osteoporosis. BMPR2 is a critical molecule in the BMP signaling pathway, regulating osteoblast proliferation, differentiation, and bone formation processes. Decreased BMPR2 expression is associated with osteoporosis, but its specific mechanisms are unclear.
Materials and Methods: Twelve-week-old C57BL/6 mice were divided into experimental and control groups, with 10 mice in each group. The experimental group was injected with BMPR2 siRNA into the left tibia, while the control group was injected with PBS. Osteoblasts were isolated and cultured, and their proliferation and differentiation were detected using MTT assay and ALP staining. The experimental data were statistically analyzed using SPSS 20.0 software.
Results: BMPR2 siRNA significantly reduced osteoblast proliferation (P<0.05) and differentiation (P<0.01). The bone density of the experimental group of mice was significantly decreased (P<0.01).
Conclusion: BMPR2 plays an important role in the development and progression of osteoporosis, regulating osteoblast proliferation, differentiation, and bone formation processes. Targeting BMPR2 may provide new therapeutic strategies for the prevention and treatment of osteoporosis.
Multiple factors causing alignment instability in childhood concomitant strabismus
Authors: Guo Yanan , Guan Yuehan , Li Li , Jiang Jingjing 
Published: April 2023

Abstract
Purpose:To investigate the potential factors related to alignment instability in childhood concomitant strabismus.
Design: Prospective cohort study.
Methods: In total, 198 children with concomitant strabismus (57 esotropia, 141 exotropia) underwent repeat prism and alternate cover tests (PACTs) by two orthoptists who were certified by the study, and sensory tests were all performed once. We defined the alignment measurement as stable if the absolute value of the measurement difference between two orthoptists was within 10 prism diopters (PD) at distance or at near, and unstable if the difference was 10 PD or greater at distance or at near. We analyzed the relationship between the measurement variability and sensory results, patient age, and angle of deviations.
Results: The mean age of the esotropia and exotropia patients was 68.5 ± 26.3 months (range, 36–164 months) and 96.1 ± 33.6 months (range, 22–200 months), respectively, and there was a significant difference in suppression related alignment instability, both at distance (P= 0.005) and at near (P = 0.039). Anisometropia also showed a significant difference at distance (P = 0.033) for alignment instability, and there was no significant statistical effect of age on measurement variability in our study. Alignment instability is positively associated with the angle of deviation, especially at distance (P = 0.019 for exotropia, P= 0.002 for esotropia).
Conclusion: Clinical characteristics are proven related to alignment instability, and suppression is an important factor for alignment instability in childhood concomitant strabismus. Nevertheless, it would be entirely reasonable to consider alignment instability in relation to other factors, such as anisometropia and a large angle of strabismus, when evaluating patient deviations.
Trial registration: Chinese Clinical Trial Registry, ChiCTR2200062404, Registered 4 August 2022.
Boosting Question Answering Systems with Multi-Label Classification Techniques
Authors: Chen YiHui , Lu Eric Jui-Lin , Jin-De  
Published: April 2023

Abstract

DBpedia is one of the most resourceful link databases today, and to access information in DBpedia databases, we need to use query syntax (e.g., SPARQL). However, not all users know SPARQL, so we must use a natural language query system to translate the user's query into the corresponding query syntax. It is costly and time-consuming for the query system to generate query syntax. Therefore, this paper proposes generating query syntax from natural language. Two multi-label learning methods are used for question transformation: Binary Relevance (BR) and Classifier Chains (CC). To predict all the labels that match the query intentions, we use Recurrent Neural Networks (RNNs) to build a multi-label classifier for generating RDF triples. To better consider the relationship between RDF triples, the Binary Relevance is integrated into an ensemble learning approach to propose an Ensemble BR. The experiments perform better than the other research to improve the query accuracy.
Examining the governance arrangements for healthcare worker COVID-19 protection in Kenya: A Scoping Review
Authors: Kazungu Jacob , Kagwanja Nancy N , Wang Huihui , Chuma Jane , Munge Kenneth 
Published: April 2023

Abstract

Background
Healthcare workers (HCWs) face a high risk of infection during pandemics or public health emergencies as demonstrated in the ongoing COVID-19 pandemic. Understanding how governments respond can inform public health control measures and support health system functioning. An economic impact analysis examining HCW COVID-19 infections in Kenya and three other countries estimated that the total economic costs related to HCW COVID-19 infections costs and deaths in Kenya were US$113.2 million (range US$35.8-US$246.1). We examined the governance arrangements for and implementation of HCW protection during the COVID-19 pandemic in Kenya between March 2020 and March 2021.
Methods
We conducted a scoping review of 44 policy and legislative documents and reports on HCW protection and 22 media articles. We adopted the transparency, accountability, participation, integrity and capacity (TAPIC) governance framework to analyse and summarize our findings into policy gaps and implementation challenges. We followed the guidance of the Preferred Reporting Items for Systematic reviews and Meta-analysis extension for Scoping Reviews (PRSIMA-ScR).
Results
Policy design gaps included inadequate provisions for emerging threats, inconsistencies with the devolved context and inadequate structures to monitor, inform and respond to HCW COVID-19 infections. Implementation challenges were attributed to inadequate quantity and quality of PPE, difficulty in accessing medical care for HCWs, delays in HCW remuneration, insufficient infection prevention and control measures, the top-down application of plans, difficulties in working in a decentralized context, and pre-existing public finance management (PFM) bottlenecks.
Conclusion
Implementation of HCW protection during the COVID-19 pandemic and beyond could leverage the revamping of current legislation on labour relations to reflect devolved governance and develop a broader and long-term approach to occupational health and safety implementation that considers all HCWs. Improvements in PFM arrangements coupled with increased investment in the health sector and attention to efficient use of resources will also impact positively on HCW protection.
Changes in trace elements and metallothioneins levels and their relationship with clinical, biochemical, and inflammatory parameters in patients with COVID-19 during the early ICU phase
Authors: Herrera-Quintana Lourdes , Vázquez-Lorente Héctor , Gamarra-Morales Yenifer , Molina-López Jorge , Adam Vojtech , Richtera Lukas , Planells Elena 
Published: April 2023

Abstract
Background
The levels of trace elements and Metallothioneins (MTs) could play a key role in modulating the inflammatory reaction and pathogenesis of COVID-19. Their effects on clinical variables are of interest for the characterization and management of this disease. Thus, the present study aimed to investigate the association between circulating Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn), and MTs levels, as well as their relationship with clinical, biochemical, and inflammatory parameters in critical care patients with COVID-19 at early Intensive Care Unit (ICU) phase.
Methods
A total of 86 critically ill patients with COVID-19 were monitored from the first day of admission to the ICU until the third day of stay. Clinical parameters were retrieved from the hospital database. Biochemical and inflammatory parameters were analyzed following enzymatic colorimetry and immunoassay procedures. Serum samples were used to assess mineral levels by inductively coupled plasma-mass spectrometry and MTs levels by differential pulse voltammetric.
Results
Levels of Cu and MTs decreased (all P ≤ 0.046) after 3 days of ICU stay, increasing the prevalence of Cu deficient values from 50–65.3% (P = 0.015) on the third day of ICU stay. Fe and Zn were shown to have a predictive value for mortality and severity. Changes in Fe were directly related to changes in Cu and Mn (all r ≥ 0.266; P ≤ 0.019). In contrast, changes in MTs were inversely related to changes in Mn and albumin (all r≥–0.255; P ≤ 0.039).
Conclusions
The present study indicated a risk of trace element deficiencies related to different biochemical and clinical parameters. We suggest monitoring the mineral status and performing nutritional interventions, when appropriate, that could help to improve the altered parameters, such as inflammatory conditions and, thus, the prognosis in critically ill patients with COVID-19.
Network Pharmacology Prediction and Experimental Verification of Rhubarb-Peach Kernel Promoting Apoptosis in Endometriosis
Authors: Liao Zi , Lei Ya , Peng Li , Fu Xianyun , Wang Wei , Yang Dan 
Published: April 2023

Abstract
Background
" Rhubarb-Peach Kernel " herb pair (RP) one of the most frequently used drug pairs, has been used in traditional medicine in China to treat inflammation and diseases associated with pain. Although it is widely used clinically and has a remarkable curative effect, the mechanism of RP treatment for endometriosis (EMs) remains unclear due to its complicated components. The aim of this study was to investigate the anti-endometriosis effect of RP, with emphasis on apoptosis via network pharmacology prediction, molecular docking and experimental verification.
Methods
The related ingredients and targets of RP in treating EMs were screened out using Traditional Chinese Medicine Systems Pharmacology (TCMSP), Tool for Molecular mechanism of Traditional Chinese Medicine (BATMAN-TCM), and GeneCards database. The data of the protein-protein interaction (PPI) network was obtained by the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING) Database. The Metascape database was adopt for Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis. After that, the molecular docking of the main active ingredients and apoptosis targets was performed. Finally, the pro-apoptotic effect of RP was verified in hEM15a cells.
Results
A total of 32 RP compounds were collected. Forty-two matching targets were picked out as the correlative targets of RP in treating EMs. Among these, 18 hub targets including P53, CASP3 were recognized by the PPI network. KEGG enrichment analysis discovered that the regulation of apoptosis was one of the potential mechanisms of RP against EMs. Anthraquinone compounds, flavonoids, and triterpenes in RP were identified as crucial active ingredients, involved in the pro-apoptotic effect, which was confirmed subsequently by molecular docking. Additionally, it was verified that RP treatment promoted apoptosis and inhibited the proliferation of EMs cells (assessed by MTT and Flow cytometry). Moreover, the induction of apoptosis in treated EMs cells can be due to the regulation of apoptosis-related protein expression, including P53, BAX, and CASP3.
Conclusions
Our results highlight the potential of RP as the target to treat EMs by promoting apoptosis. Anthraquinones, flavonoids and triterpenoids are the possible pro-apoptotic components in RP.
An application of mist generator as a way to reduce particulate matter during high concentration episodes at urban forests
Authors: Yoo Sin-Yee , Kim Taehee , Choi Sumin , Park Chan-Ryul , Song Dong-Ha 
Published: April 2023

Abstract
A mist generator is designed to prevent particulate matter (PM) damage to trees by reducing PM by combining mist and PM during high PM episodes. In addition, mist can minimize the dry heat damage caused by high temperatures and low relative humidity during high PM episodes. Herein, we tested the effects of PM reduction and microclimate condition change with mist generators in urban forests with high PM episodes. We measured changes in microclimate conditions and the concentration of PM before, during, and after mist spraying in urban parks (Yangjae Citizen Forest, YCF; Cheongdam Road Park, CRP) from May 6 to 8, 2020. PM changes in YCF and CRP were observed immediately after mist spraying and were found to return to the previous concentrations. At the morning rush hour and lunch, mist, high wind speed, and low relative humidity conditions were related to the increase of the mist movement, resulting in increasing PM (2.5–10 µm) and the deposition of these PM. During the evening rush hour, high relative humidity and low wind speed affected PM concentrations more than mist. However, no significant differences were observed in the microclimate conditions after mist spraying. Despite these preliminary results, these findings indicate that mist may reduce PM and PM damage to trees during high PM episodes.
Landslide detection by deep learning based semantic segmentation using Sentinel-1 intensity images
Authors: Ito Keisho , Kinoshita Yohei 
Published: April 2023

Abstract
Fast-moving landslides associated with earthquakes and/or heavy rains can cause significant human and socioeconomic damage. Satellite synthetic aperture radar (SAR) can observe wide areas regardless of the presence of clouds and sunlight, and thus is a promising tool for detecting landslides immediately after a disaster occurrence. Recently, the application of deep learning-based semantic segmentation to satellite optical imagery is active for the landslide detection, but its application to SAR imagery is still limited. Here we investigated landslide detection capability of deep learning-based semantic segmentation using pre- and post-disaster Sentinel-1 SAR intensity images. We used U-Net as the deep learning model and validated the method on four disaster cases composed of two earthquake and two heavy rain events. In the cases of the 2018 Hokkaido Eastern Iburi earthquake and the July 2017 Northern Kyushu heavy rainfall, detection models trained in the same single case or on multiple cases could identify large landslides and spatial concentrations of damage areas. The model trained in the 2018 Hokkaido Eastern Iburi earthquake provided the highest Kappa coefficient (0.624) when we applied it to the same case. The proposed method could detect landslides that had not been detected by emergency aerial observation, demonstrating the effectiveness of the combination of satellite SAR and deep learning for the landslide detection. However, the detection performance of trained models was degraded when they were applied to cases with different characteristics in terms of topography, vegetation and landslide occurrence mechanisms from training cases. Therefore, our results indicated that detection models should be trained in various cases for improving versatility. For future practical use, further validations by other disaster cases using various kinds of satellite SARs are needed.
The natural product co-evolved pyrroloquinoline quinone gene enhances their production when heterologously expressed in a variety of Streptomycetes
Authors: Wang Xinran , Chen Ningxin , Cruz-Morales Pablo , Zhong Biming , Zhang Yangming , Acharya Suneil , Li Zhibo , Deng Huaxiang , Luo Xiaozhou , Keasling Jay 
Published: April 2023

Abstract
Streptomyces has the largest repertoire of natural product biosynthetic gene clusters (BGCs), yet developing a universal engineering paradigm for different Streptomyces strains is challenging. That some bacteria and fungi are more adept than others at synthesizing natural products implies the existence of key genes co-evolved with the BGCs for high productivity, which may provide a potential universal strategy for Streptomyces to produce more target products. We show here that genes co-evolved with natural product BGCs in Streptomyces can be identified by pan-genomic analysis. Among the 598 genes that co-evolved with polyketide BGCs was the gene cluster encoding for the co-factor pyrroloquinoline quinone (PQQ). When the PQQ gene cluster was engineered into 11 Streptomyces strains it enhanced production of naturally-produced polyketides with up to 40 fold and activated silent gene clusters. This study provides a new engineering paradigm for improving polyketide production and discovering new biosynthetic gene clusters and potential therapies.
An unusual clinical presentation of cervical extradural meningiomas and detected with neuromuscular ultrasound: A case report and literature review
Authors: Hsieh Pei-Chen , Lu Johnny Chuieng-Yi , Huang Shih-Chiang , Kuo Hung-Chou 
Published: April 2023

Abstract
Background: Extradural meningiomas are exceedingly rare, with most occurring in the thoracic region. If the tumor occurs in the cervical region, it may invade the adjacent nerve root and brachial plexus. Usually, the patient may not be diagnosed until presenting with signs of myelopathy, such as progressive paresis and numbness.
Case presentation: We report a 64-year-old male patient with neck pain followed by numbness and very mild weakness of the left hand for 6 months. The general neurological examination was grossly normal expect for mild grasping weakness over left side. Neuromuscular ultrasound revealed a lesion over the left 7th cervical root. The patient received subtotal tumor excision followed by radiotherapy for residual tumor. Histopathology revealed atypical meningioma (WHO grade II).
Conclusion: Neuromuscular ultrasound allowed for detection and diagnosis cervical brachial plexus tumor at an early stage before significant focal neurological defects developed.
Validating applicability of strain data calibrated in geodetic range on the seismic frequency band by examining Rayleigh wave amplitude ratio
Authors: Yabe Suguru , Imanishi Kazutoshi , Matsumoto Norio 
Published: April 2023

Abstract
Although borehole strain observatories provide useful information about crustal deformation, they need to be calibrated because their highly sensitive observations are affected by structural heterogeneity around the sensors used for settlements. The Geological Survey of Japan developed a strain observation network in southwest Japan to monitor slow-slip events in the Nankai Trough. In this study, the vertical axial strain data of this network as well as the horizontal strain of two newly constructed stations were calibrated using the method used in a previous study. Although this calibration was performed in the tidal frequency band, this study confirmed that the calibration is also valid in the frequency band of seismic surface waves at ~ 0.01 Hz. This fact enables us to use strain data not only for geodetic analysis of crustal deformation but also for the analysis of surface waves from moderate regional earthquakes. This study shows that synthetic strain waveforms are consistent with observed ones, suggesting the usefulness of strain data in centroid moment tensor analysis to improve the resolution of seismic moment tensors.
Comparison of different prognostic scores in estimating short- and long-term mortality in COVID-19 patients above 60 years old in a university hospital in Belgium
Authors: Hauwermeiren Carmen Van , Claessens Marie , Berland Mathilde , Dumoulin Baptiste , Lieten Siddharta , Surquin Murielle , Benoit Florence 
Published: April 2023

Abstract
PURPOSE: Multiple scoring systems were used for risk stratification in COVID-19 patients. The objective was to determine among 6 scores which performed the best in predicting short-and long-term mortality in hospitalized COVID-19 patients ≥ 60 years.
METHODS: An observational, retrospective cohort study conducted between 21/10/2020 - 20/01/2021. 6 scores were calculated (Clinical Frailty Scale (CFS), Charlson Comorbidity Index (CCI), 4C Mortality Score (4CMS), NEWS score (NEWS), quick-SOFA score (qSOFA), and Quick COVID-19 Severity Index (qCSI)). We included unvaccinated hospitalized patients with COVID-19 ≥ 60 years old in Brugmann hospital, detected by PCR and/or suggestive CT thorax images. Old and nosocomial infections, and patients admitted immediately at the intensive care unit were excluded.
RESULTS: 199 patients were included, mean age was 76.2 years (60 - 99). 56 patients (28%) died within 1 year after the first day of hospitalization. The 4CMS predicted the best intrahospital, 30 days and 6 months mortality, with area under the ROC curve (AUROC) 0.695 (0.59-0.8), 0.76 (0.66-0.86) and 0.73 (0.64-0.82) respectively. The CCI came right after with respectively AUROC of 0.68 (0.58-0.77), 0.74 (0.66-0.82) and 0.72 (0.64-0.8). To predict mortality at 12 months after hospitalization, the CCI had the highest AUROC with 0.74 (0.66-0.81), before the 4CMS with 0.695 (0.61-0.78). CONCLUSION: Among 6 scores, the 4CMS was the best to predict intrahospital, 30-day and 6-month mortality. To predict mortality at 12 months, CCI had the best performance before 4CMS. This reflects the importance of considering comorbidities for short- and long-term mortality after COVID 19.