bluuebunny/crossref_metadata_2025_split huggingface dataset:
FPGA-Based Image Denoising Using Adaptive Vector Directional Distance Filter: Performance Analysis and Comparison
Authors: K Arunkumar , N Nalini , P Mangayarkarasi , Albert Anitha Juliette 
Published: November 2023

Abstract

Image processing is essential in a wide range of applications, like medical imaging and remote sensing, where precise visual scene interpretation is critical. However, noise is typically introduced during the process of creating, editing, transmitting, and storing color images, diminishing their visual quality and the efficacy of later image processing operations. Digital image processing relies heavily on noise reduction, also known as denoising, which seeks to get rid of unwanted noise without altering the quality of the image's essential details. In this research, we show how to use a hardware/software codesign strategy to efficiently construct the Adaptive Vector Directional Distance Filter (AVDDF) on a Field-Programmable Gate Array (FPGA). To streamline the development process, we use a high-level synthesis (HLS) pipeline with the Xilinx Vivado HLS tool. The software module is written in C/C++ and runs on a high-performance Reduced Instruction Set Computer (RISC). Comparisons are done with well-established denoising techniques like the Vector Directional Distance Filter (VDDF) and the Vector Directional Filter (VDF) to evaluate the effectiveness of the AVDDF technique. For performance comparisons, evaluation criteria such as the Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE) are used. In addition, the performance of the FPGA is measured in terms of its Look-Up Tables (LUTs), Flip-Flops (FFs), BRAM_18K (Block RAMs), and DSP48E (Digital Signal Processing Blocks) using Verilog. The area, power, and delay performance of the suggested ASIC implementation is evaluated as well. The findings of this study prove that the AVDDF denoising method is effective in enhancing image quality while keeping crucial image properties unchanged.
Preservation of the posterior septum and stump for anatomical posterior cruciate ligament reconstruction of the tibial tunnel reference position
Authors: Chen Zhen , Niu Yingzhen , Jin Lingpeng , Zheng Yi , Zhang Zhuangdai , Wang Fei , Chen Baicheng , Dong Jiangtao 
Published: November 2023

Abstract

Background:The purpose was to explore theanatomicposition relationship and locate the lowest tunnel within the the PCL tibial insertion while intactly preserved the PCL stump, posterior septum and capsule.Methods:Twelve fresh unpaired adult human cadaveric knees were used to observe the spatial anatomical position between the PCL tibial insertion, posterior septum and posterior capsule. Then, the distal reflexion of the posterior capsule and the posterior septum were intactly preserved and used as references to search for the center of the tibial insertion (CP) and lowest tibial tunnel (SP). And measured the distance of the SP and CP points and PCL tibial insertion by anatomical measurements and CT scan.Results:In twelve specimens,the PCL tibial insertions were fully located within the posterior medial compartment of the knee. The distance of the CP from the tibial plateau articular surface was (6.5±0.2) mm, and the horizontal and vertical distances from the posterior septum were (5.9±0.3) and (5.4±0.4) mm, while the SP from them were (9.5±0.4), (4.3±0.3) and (4.1±0.3) mm, respectively. All the tibial tunnels established centered around the SP retained the intact posterior wall, which was (1.6±0.3) mm to the distal reflexion of the posterior capsule. The SP/CP was (17.6±5.6) % and (49.9±0.3) % closer to the inferior boundary, while (49.3±13.2) % and (33.9±6.8) % relative to the lateral boundary of the insertion.Conclusion:ThePCL tibial insertion was located in the posterior medial compartment of the knee, with the posterior septum and distal reflexion of the posterior capsule as its lateral and inferior boundaries. Using the posterior septum and posterior capsule as reference marks, a low tibial tunnel with high repeatability can be established in the PCL tibial insertion without damaging the posterior septum.
Computational repurposing of Trimethoprim: molecular docking and molecular dynamics simulation studies suggest a potential inhibition of colchicine-binding site.
Authors: Osama Salma , Shahin Mai I. , Elrazaz Eman Z. , Abouzid Khaled A. M. 
Published: November 2023

Abstract
Cancer is a major global health concern, and its incidence is projected to rise in the coming years. Drug repurposing, the identification of new uses for existing drugs, offers a promising approach to accelerating the development of potent and less toxic anti-cancer agents. This in silico study explored the possible repurposing of trimethoprim, an approved synthetic antimicrobial, as a colchicine-binding site (CBS) inhibitor using molecular docking and molecular dynamics (MD) simulations. Trimethoprim shares structural similarities and pharmacophoric features with colchicine and the combretastatins, potent antimitotic agents that target the CBS. The docking results showed that trimethoprim achieved a good binding affinity to the CBS, with an average CDOCKER_ENERGY of -33.75 kcal/mol. The MD simulations (100 nanoseconds) confirmed the stability of the trimethoprim-tubulin complex, with a root mean square deviation (RMSD) of less than 2.5 Å for the protein backbone. The root mean square fluctuation (RMSF) of the binding site residues increased, indicating their increased flexibility. The radius of gyration (Rg) also increased within acceptable limits, suggesting that the protein unfolds to accommodate trimethoprim binding. The binding energy calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) approach was − 27.3 kcal/mol, which further confirms the docking results. Overall, the findings of this study provide preliminary evidence that trimethoprim has the potential to be repurposed as a CBS inhibitor. Further in vitro and in vivo studies are needed to validate its efficacy and safety as a potential anti-cancer agent.
The Significance of CSI and Paper-Level Classification System in Coping with the Challenges Brought by Ultra-Highly Cited Papers to Journal Evaluation
Authors: Liu Yahui , Zhang Jiandong , Yang Liying , Shen Zhesi 
Published: November 2023

Abstract
The COVID-19 pandemic resulted in a surge in the production and high citation rates of related publications, and these ultra-highly cited papers brought grave challenges to journal evaluation. So it is significant to test the performance of bibliometric indicators during the crisis and assess their ability to adapt to rapidly evolving research landscapes. The CAS Journal Ranking, one of the most widely used journal ranking systems in China, is committed to accurately revealing the average impact of journals and enhancing the robustness of evaluation results. This study focused on the response of the CAS Journal Ranking system to the ultra-highly cited papers related to COVID-19. We compared the journal impact factor (JIF), category normalized citation impact (CNCI), and CAS’s indicator - the field normalized citation success index (FNCSI) - under journal-level and paper-level classification systems by assessing changes in indicator values and examining ranking mobility of journals. The results indicate combining FNCSI and CWTS paper-level classification system yields a robust indicator in coping with the challenges brought by COVID-19 papers. The combination is effective because FNCSI measure reduces the enormous impact of COVID-19 papers, while CWTS paper-level classification system groups the majority of COVID-19 papers into the “coronavirus” category, preventing distortion of citation normalization of other groups. By revealing the pros and cons of various indicators, we hope to emphasize the relative suitability and dependence on the context. and inform future improvements to scientific journal evaluation systems and methodologies.
Translation and validation of the “Multidimensional Scale of Perceived Social Support” in the Greek general population
Authors: Katsiroumpa Aglaia , Moisoglou Ioannis , Konstantakopoulou Olympia , Vraka Irene , Gallos Parisis , Tsiachri Maria , Tsakalaki Anastasia , Galanis Petros 
Published: November 2023

Abstract
Background
Social support is an external factor that can improve individuals’ adaptation to stressful life events. Valid tools are necessary to measure social support in the general population.
Objective
To translate and validate the “Multidimensional Scale of Perceived Social Support” (MSPSS) in a sample of the Greek general population.
Methods
We translated the MSPSS in Greek using the forward-backward translation method. We assessed test-retest reliability, internal reliability, face validity, construct validity, and concurrent validity. First, we calculated intraclass correlation coefficients and Cronbach’s coefficient alpha to assess the reliability of the MSPSS. Then, we employed confirmatory factor analysis to assess the construct validity of the MSPSS. Finally, we used the Patient Health Questionnaire-4 (PHQ-4), the Brief Resilience Scale (BRS), the single item burnout measure, and the COVID-19 burnout scale (COVID-19-BS) to assess the concurrent validity of the MSPSS.
Results
Intraclass correlation coefficients for the three subscales of the MSPSS (family, friends, and significant others subscales) and the total scale score ranged from 0.862 to 0.914 (p < 0.001 in all cases). Confirmatory factor analysis supported the three factors structure (family, friends, and significant others subscales) of the original version of the MSPSS. Cronbach’s coefficients alpha for the family, friends, and significant others subscales and the total scale were 0.888, 0.935, 0.935, and 0.941 respectively. Concurrent validity of the MSPSS was perfect since we found statistically significant correlations between the MSPSS and PHQ-4 (r = -0.181, p < 0.001), BRS (r = 0.199, p < 0.001), single item burnout measure (r = -0.068, p = 0.015), and COVID-19-BS (r = -0.136, p < 0.001).
Conclusions
The MSPSS is a reliable and valid tool to measure social support in the general population. Measurement of social support among individuals is necessary to improve this external psychological factor.
Evaluating Latency in Fifth-Generation Vehicle-To-Tverything Communications Using Adaptive Neuro-Fuzzy Inference System Model
Authors: Sayedahmed Hamdy A.M. , Elgamal Emadeldin M. , Hefny Hesham A. 
Published: November 2023

Abstract
The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks and switch-modes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90% for UE in VoIP.
Predictors of Treatment Outcome and Clinical Profile among Guillain-
Barre Syndrome Patients in South India
Authors: Joseph Nitin , Shrigiri Soumya 
Published: November 2023

Background:
Guillain-Barre syndrome (GBS) is one of the principal causes of
acute neuromuscular weakness and paralysis worldwide. Its clinic-epidemiological profile
and factors influencing its treatment outcomes in developing countries are very minimally
studied.

Objective:
The study aimed to study the risk factors, clinical presentation, management,
and predictors of treatment outcomes among GBS patients admitted in two tertiary care
hospitals.

Materials and Methods:
Medical records of 121 inpatients with GBS confirmed based
on the Brighton criteria over the recent five-year period from June 2017 to May 2022 were
examined. Assessment of the severity of GBS was done using the Hughes functional grading
scale.

Results:
The mean age at onset was 36.8±18.9 years. The majority of the patients [82 (67.8%)]
were males. Antecedent illnesses within 1 month of onset of GBS were present among 34
(28.1%) patients. The majority of them developed respiratory tract illnesses [13 (38.2%)].
Recurrent history of GBS was observed among 4 (3.3%) patients. The median time gap between
the onset of antecedent illnesses and the onset of GBS was 5 days (IQR 3, 10). The most
common symptom among GBS patients was the weakness of the muscles of the extremities [117
(96.7%)]. The pattern of progression of weakness among 53 (45.3%) of these patients was from
the lower to upper limbs. The most common sign noted was hypotonia in 64 (52.9%) patients.
Complications due to GBS were observed among 12 (9.9%) patients. The most common
complication was respiratory distress in 11 (91.7%) patients, followed by autonomic
dysfunctions in 8 (66.7%). Albuminocytological dissociation in cerebrospinal fluid was noted
among 48 (39.7%) patients. The majority of patients in nerve conduction studies had
acute inflammatory demyelinating polyneuropathy [61 (50.4%)]. The majority of the GBS
patients [68 (56.2%)] were treated using intravenous immunoglobulin (IVIG). 95 (78.5%)
patients improved with treatment at the time of discharge. In multivariable analysis, the absence
of antecedent illnesses (p =0.029), Brighton’s diagnostic certainty levels 1 and 2 of GBS (p
=0.024), and being kept on IVIG treatment (p =0.05) were associated with improvement in
disease condition among the patients.

Conclusion:
Appropriate diagnosis of GBS using both clinical and laboratory evidence and
providing appropriate treatment along with more supervision among GBS patients with a history
of antecedent illnesses will help improve their prognosis at the time of discharge.
Evaluation of Enhanced Cytotoxicity Effect of Repurposed Drug Simvastatin/Thymoquinone Combination against Breast Cancer Cell Line
Authors: Kumari Pallavi , Dang Shweta 
Published: November 2023

Introduction:
Repurposing of drugs for their anticancer potential is gaining a lot of
importance in drug discovery.

Aims:
The present study aims to explore the potential of Simvastatin (SIM), a drug used in the
treatment of high cholesterol, and Thymoquinone (Nigella Sativa) (THY) for its anti-cancer activity
on breast cancer cell lines. Thymoquinone is reported to have many potential medicinal
properties exhibiting antioxidant, antiinflammatory, anti-cancer, activities like inhibition of tissue
growth and division.

Methods:
In this analysis, we explored the inhibitory effects of the combination of Simvastatin
ad Thymoquinone on two breast cancer cell lines viz MCF-7 and MDA-MB-231 cells. The combined
effect of Simvastatin and Thymoquinone on Cell viability, Colony formation, Cell migration,
and orientation of more programmed cell death in vitro was studied. Cell cycle arrest in the
G2/M phase was concomitant with the combined effect of SIM and THY persuading apoptosis
and generating reactive oxygen species (ROS).

Results:
The cell cycle arrest with combined treatment was observed that only 1.8% and 1.1%
cells gated in G2/M phase in MCF-7 & MDA-MB-231 cell. An increased apoptosis was observed
when cells were treated in combination which was about 76.20% and 58.15 % respectively
for MCF-7 and MDA-MB-231 cells.

Conclusion:
It was concluded that the combined effect of simvastatin and thymoquinone stimulates
apoptosis in breast cancer cells.
Symptoms and Outcomes of COVID-19 in Elderly Recipients of Influenza and COVID-19 Vaccines
Authors: Mohammadkhah Fatemeh , Amirhossein Moslehi Seyed , Larijani Fatemeh , Ziapour Arash , Chaboksavar Fakhreddin , Asaeiyan Hamidreza , Hossein Ashrafi Kamiar , Fotokian Zahra 
Published: November 2023

Background:
Health providers and policymakers aim to prevent or reduce the severity of disease outcomes globally.

Objective:
The present study aimed to explore the outcomes and common symptoms of COVID-19 in the elderly population vaccinated against influenza and COVID-19 in hospitals of Ramsar and Tonekabon cities in Iran.

Methods:
To participate in the present cross-sectional descriptive study, 11 qualified subjects were selected through a census. The data collection instruments included the “Demographic and clinical questionnaire” and “Registration form of sampled patients with acute disease syndrome”. The data were analyzed in SPSS 24 using descriptive statistics (frequency).

Results:
The average age of the participants was 70.63±5.85 years. As the results showed, mild cardiovascular symptoms were found in 6 participants (54.5%), lower respiratory system symptoms in 5 (45.5%), nervous system symptoms in 4 (36.4%), and auxiliary symptoms in 4 (36.4%). No cardiac, renal, blood or central nervous system outcomes were observed in the elderly, and the mortality rate was 0%. The findings also showed a reduction in clinical symptoms and severity of outcomes, an improvement in clinical findings, and no mortality among the elderly.

Conclusion:

The present findings supported measures aimed at increasing the coverage of influenza and COVID-19 vaccination in people, especially the elderly.
Factors Influencing the Retention of Midwives in Rural Areas of Bhutan: A National Cross-sectional Study
Authors: Jurmin Karma , Jariya Wutthichai 
Published: November 2023

Background:
The retention of midwives in rural areas is pivotal in achieving sustainable development goals, particularly those concerning maternal and child health. Though the human resource planning policy mandates at least one midwife in every primary healthcare facility (PHCF), Bhutan has not achieved this target.

Objective:
This study has aimed to explore the factors that influence the retention of midwives in rural areas of Bhutan.

Methods:
A national cross-sectional study was conducted in 2022 among all 165 midwives working in rural PHCFs across 20 districts in Bhutan. The questionnaire was designed based on the World Health Organization framework that was developed for rural health workforce retention. Data were collected online using a structured closed-ended self-administered questionnaire and were then analysed using descriptive statistics and linear regression.

Results:
The analysis revealed that the midwives had a high retention intention. Age, monthly income, work experience, personal origins and values, family and community aspects, working and living conditions, career-related opportunities, financial incentives, and mandatory service were positively and statistically significantly correlated with retention. Financial incentives and working and living conditions were the predictors that strongly influenced the retention of midwives in rural areas.

Conclusion:
This study indicated that the retention of midwives was high and was influenced by multiple factors. Financial incentives and working and living conditions were highly significant factors in attracting and retaining midwives in rural PHCFs.
Early Prediction of Covid-19 Samples from Chest X-ray Images using Deep Learning Approach
Authors: Sudheesh K V , Kiran  , Gururaj Harinahalli Lokesh , Ravi Vinayakumar , Almeshari Meshari , Alzamil Yasser 
Published: November 2023

Aims:
In this study, chest X-ray (CXR) and computed tomography (CT) images are used to analyse and detect COVID-19 using an unsupervised deep learning-based feature fusion approach.

Background:
The reverse transcription-polymerase chain reaction (RT-PCR) test, which has a reduced viral load, sampling error, etc., is used to detect COVID-19, which has sickened millions of people worldwide. It is possible to check chest X-rays and computed tomography scans because the majority of infected persons have lung infections. The COVID-19 diagnosis can be made early using both CT and CXR imaging modalities, which is an alternative to the RT-PCR test.

Objective:
The manual diagnosis of CXR pictures and CT scans is labor and time-intensive. Many AI-based solutions are being investigated to tackle this problem, including deep learning-based detection models, which can be utilized to assist the radiologist in making a more accurate diagnosis. However, because of the demand for specialized knowledge and high annotation costs, the amount of annotated data available for COVID-19 identification is constrained. Additionally, the majority of current cutting-edge deep learning-based detection models use supervised learning techniques. Because a tagged dataset is not required, we have investigated various unsupervised learning models for COVID-19 identification in this work.

Methods:
In this study, we suggest a COVID-19 detection method based on unsupervised deep learning that makes use of the feature fusion technique to improve performance. Based on this an automated CNN model is built for the detection of COVID-19 samples from healthy and pneumonic cases using chest X-ray images.

Results:
This model has scored an accuracy of about 99% for the classification between covid positive and covid negative. Based on this result further classification will be done for pneumonic and non-pneumonic which has scored an accuracy of 94%.

Conclusion:
On both datasets, the COVID-19 detection method based on feature fusion and deep unsupervised learning showed promising results. Additionally, it outperforms four well-known unsupervised methods already in use.