bluuebunny/crossref_metadata_2025_split huggingface dataset:
Baricitinib ameliorates inflammatory and neuropathic pain in collagen antibody-induced arthritis mice by modulating the IL-6/JAK/STAT3 pathway and CSF-1 expression in dorsal root ganglion neurons
Authors: Makabe Kenta , Okada Hiroyuki , Tachibana Naohiro , Ishikura Hisatoshi , Ito Norihito , Tanaka Masaru , Chijimatsu Ryota , Terashima Asuka , Yano Fumiko , Asaka Meiko , Yanagihara Dai , Taketomi Shuji , Matsumoto Takumi , Tanaka Sakae , Omata Yasunori , Saito Taku 
Published: March 2024

Abstract

Background Janus kinase (JAK) inhibitors, such as baricitinib, are widely used to treat rheumatoid arthritis (RA). Clinical studies show that baricitinib is more effective at reducing pain than other similar drugs. Here, we aimed to elucidate the molecular mechanisms underlying the pain relief conferred by baricitinib, using a mouse model of arthritis.
Methods We treated collagen antibody-induced arthritis (CAIA) model mice with baricitinib, celecoxib, or vehicle, and evaluated the severity of arthritis, histological findings of the spinal cord, and pain-related behaviors. We also conducted RNA sequencing (RNA-seq) to identify alterations in gene expression in the dorsal root ganglion (DRG) following baricitinib treatment. Finally, we conducted in vitro experiments to investigate the direct effects of baricitinib on neuronal cells.
Results Both baricitinib and celecoxib significantly decreased CAIA and improved inflammatory pain as determined by grip strength, while only baricitinib remarkably suppressed neuropathic pain as determined by the von Frey test. CAIA induction of inflammatory cytokines in ankle synovium, including interleukin (IL)-1β and IL-6, was suppressed by treatment with either baricitinib or celecoxib. In contrast, RNA-seq analysis of the DRG revealed that baricitinib, but not celecoxib, restored gene expression alterations induced by CAIA to the control condition. Among many pathways changed by CAIA and baricitinib treatment, the interferon-alpha/gamma, JAK-signal transducer and activator of transcription 3 (STAT3), and nuclear factor kappa B (NF-κB) pathways were considerably decreased in the baricitinib group compared with the celecoxib group. Notably, only baricitinib decreased the expression of colony-stimulating factor 1 (CSF-1), a potent cytokine that causes neuropathic pain through activation of the microglia–astrocyte axis in the spinal cord. Accordingly, baricitinib prevented increases in microglia and astrocytes caused by CAIA. Baricitinib also suppressed JAK/STAT3 pathway activity and Csf1expression in cultured neuronal cells.
Conclusions Our findings demonstrate the effects baricitinib has on the DRG in relation to ameliorating inflammatory and neuropathic pain.
Performance Evaluation of Deep Learning-based Covid-19 Diagnosis Software: a Comprehensive Approach Using Convolutional Neural Networks and Ensemble Machine Learning
Authors: Ogunwale Yetunde Esther , Oyedemi Oluyemisi Adenike , Ajinaja Micheal Olalekan 
Published: March 2024

Abstract
Quick diagnosis of COVID-19 through chest X-ray images has gained significant attention due to its potential to aid in rapid screening. In this study, we presented a comprehensive approach utilizing convolutional neural networks (CNNs) for feature extraction from chest X-ray images, followed by an ensemble of classifiers including Decision Tree, Support Vector Machine, Random Forest, and AdaBoost for accurate classification. Our CNN architecture, trained on Google Colab with GPU runtime, comprises 20 layers incorporating Conv2D, MaxPooling2D, Dropout, and fully connected layers with ReLU activation function and a dropout threshold of 0.25, achieving an accuracy of 97.10%. By using a dataset that consists of 33,920 chest X-ray (CXR) images including 11,956 COVID-19, 11,263 Non-COVID infections (Viral or Bacterial Pneumonia), 10,701 Normal with Ground-truth lung segmentation masks provided for the entire dataset from the Kaggle COVID-19 Radiography Database. Our final ensemble classifier, employing Soft voting, attained a heightened accuracy of 97.51%. Moreover, to gain insights into the CNN's internal processes, we visualized intermediate layer activations. Subsequently, we deployed the final model using a Flask API for seamless integration into healthcare systems. Our approach promised efficient and accurate diagnosis of COVID-19 from chest X-ray images, facilitating timely patient management.
Unveiling the future of Ultrasound Chemical Bath Deposition method for flexible supercapacitive electrodes : Impact of cationic precursors on MnO2 flexible electrodes – a case study
Authors: Deshmane Abhinandan , Thakur Amarsingh , Salunkhe Dadasaheb , Bhosale Raghunath 
Published: March 2024

Abstract
MnO2 flexible electrodes (FEs) were synthesized by novel ultrasound chemical bath deposition (UCBD) using aqueous solutions of three different manganese (II) cationic precursors viz. manganese acetate, manganese chloride, and manganese nitrate. 1M aqueous NaOH solution was used as the oxidizer for each precursor. Under optimized preparative conditions, uniform MnO2 films grew on the substrates to form the FEs. XRD analysis confirmed the formation of crystalline MnO2, further corroborated by EDX. FESEM imaging revealed a transition in surface morphology from mud-like structures with manganese acetate to randomly arranged nanoflakes with manganese nitrate. Surface modification was confirmed by contact angle measurements. Cyclic voltammetry showed the specific capacitance (SC) varied with the Mn2+ cation source. Galvanostatic charge-discharge further confirmed this trend. The highest SC of 408.50 Fg-1 was exhibited by electrodes prepared with Mn(NO3)2.6H2O with 90.61 % retention in SC even after 2000 CV cycles.
A Literature Review of Food Analytics
Authors: Elragal Rawan , Elragal Ahmed , Habibipour Abdolrasoul 
Published: March 2024

Abstract

This research examines the potential use of modern technologies such as big data, data science, artificial intelligence, and machine learning, which have penetrated several aspects of our lives, to address food concerns and problems, forming the nowadays called food analytics. We discuss the potential use of such technologies in relation to food problems and shortages. We analyze the opportunities and challenges associated with the use of such technological advancements and the potential benefits for the global food system. We also provide a research agenda with future directions for the application of big data, data science, artificial intelligence, and machine learning to the food ecosystem.
Enhancing Real-time Detection of Strawberry Diseases: An Optimized SSE-YOLOv5 Model with Improved Accuracy and Small Object Detection
Authors: Liu Hai-chao , Liang Hao-yu , Liu Ying-hao , Qin Jiang-wei , Zhang Zhiguang , Yu Miao , Luo Yang 
Published: March 2024

Abstract
Strawberries are a highly valuable crop widely cultivated across China, making the strawberry industry a crucial component of the country's agricultural sector. Pests and diseases are significant factors affecting the strawberry industry. However, detecting these issues is complicated in real-world environments. Traditional methods, relying on manual inspection by workers, are highly random and inefficient. To achieve real-time detection of strawberry diseases and deploy the model on mobile devices, we propose an improved neural network, SSE-YOLOv5, which enhances detection accuracy while ensuring the model is lightweight. Firstly, we added a channel attention mechanism, SENet, to the original model. The channel attention mechanism offers an effective way to improve feature selection and enhance model performance. It can increase the model's accuracy and recall rate by automatically identifying and emphasizing important features. Additionally, it provides better interpretability, allowing us to see the features that the model focuses on during decision-making. When dealing with data with rich features and complex patterns, the channel attention mechanism shows superior adaptability. Finally, focusing on key features helps to reduce model complexity and the risk of overfitting. Secondly, we added a small object detection layer to improve the accuracy of detecting small targets. Compared with the YOLOv5 baseline model, the improved SSE-YOLOv5 model increased the mAP by 7.4%, reaching 76.3%. The experiments showed that the accuracy of the improved SSE-YOLOv5 model was 75.2%, and the recall rate was 69.8%. The model's detection performance is excellent and can meet the requirements for real-time detection of strawberry diseases.
Validity and reliability of the Pain Assessment in Impaired Cognition 15 (PAIC15) observation scale in persons with aphasia
Authors: de Vries Neeltje J. , Smaling H.J.A. , Steen J.T. van der , Achterberg W.P. 
Published: March 2024

Abstract
Background
The use of self-report pain scales in persons with aphasia can be challenging due to communication and cognitive problems. An observational scale may be used as an alternative. The aim of this study is to examine the validity and reliability of the observational Pain Assessment in Impaired Cognition (PAIC15) scale that was developed for people with dementia, in persons with aphasia.
Methods
In 14 Dutch nursing homes, persons with aphasia were observed during rest and transfer by two observers using the PAIC15. The PAIC15 comprises 15 items covering the three domains of facial expressions, body movements, and vocalizations. When able, the person completed four self-report pain scales after each observation. The observations were repeated within one week. For criterion validity, correlations between the PAIC15 and self-report pain scales were calculated and for construct validity, three hypotheses were tested. Reliability was determined by assessing internal consistency, and intra- and interobserver agreement.
Results
PAIC15 observations were obtained for 71 persons (mean age 75.5 years) with aphasia. Fair positive correlations (rest: 0.35–0.50; transfer: 0.38–0.43) were reported between PAIC15 and almost all self-report pain scales and, one of the three construct validity hypotheses was confirmed. Results showed acceptable internal consistency. Intraobserver agreement was high during transfer but not during rest, interobserver agreement was high on the three PAIC15 domains during transfer but not during rest.
Conclusions
Recognition of pain in persons aphasia using the PAIC15 observational scale showed mixed yet promising results in this clinical study.
Ketamine-Assisted Buprenorphine Initiation: A Pilot Case Series
Authors: Grande Lucinda A , Hutch Tom , Jack Keira , Mironov Wendy , Iwuoha Jessica , Muy-Rivera Martin , Grillo Jacob , Martin Stephen , Herring Andrew 
Published: March 2024

Abstract

Background:Many people with opioid use disorder who stand to benefit from buprenorphine treatment fail to initiate it due to experience with or fear of both spontaneous opioid withdrawal and buprenorphine-precipitated opioid withdrawal (BPOW). An effective means of minimizing withdrawal symptoms would reduce patient apprehensiveness, lowering the barrier to buprenorphine initiation. Ketamine, approved by the FDA as a dissociative anesthetic, reduced opioid dependence in preclinical studies. It completely resolved BPOW in case reports when infused at a sub-anesthetic level, yet dissociation, which is an altered cognitive state, occurred. However, most patients undergo buprenorphine initiation in the outpatient setting where altered mental status is undesirable. We explored the potential of short-term use of ketamine, self-administered sublingually at a lower, sub-dissociative dose to assist ambulatory patients undergoing transition to buprenorphine from pure mu-opioid agonists.Methods:Patients prescribed ketamine were either 1) seeking transition to buprenorphine from illicit fentanyl and highly apprehensive of BPOW or 2) undergoing transition to buprenorphine from illicit fentanyl or methadone and experiencing BPOW. We prescribed 4-8 doses of sublingual ketamine 16 mg (each dose bioequivalent to 3-6% of an anesthetic dose), monitored patients daily or near-daily, and adjusted buprenorphine and ketamine dosing based on patient response and prescriber experience.Results:37 patients were prescribed ketamine over 14 months. Ketamine reliably abolished fentanyl withdrawal symptoms for up to 72 hours, often prevented severe BPOW when used prophylactically, and often reduced or abolished BPOW. Of the 24 patients who confirmed trying ketamine, 16 (67%) completed buprenorphine initiation, including 12 (75%) of the last 16 patients. Of the last 12 patients who completed initiation, 11 (92%) achieved 30-day retention in treatment. We developed a ketamine protocol that allowed four of the last patients to undergo a smooth buprenorphine initiation over four days. Adverse effects including dissociation did not occur within the effective ketamine dose range.ConclusionsKetamine at a sub-dissociative dose was useful to prevent, reduce and/or treat painful opioid withdrawal symptoms during buprenorphine initiation in the outpatient setting. Further research is warranted to confirm these results and develop reliable protocols for a range of treatment settings.
Orbital Stability of Smooth Solitary Waves for the Novikov Equation
Authors: Ehrman Brett , Johnson Mathew , Lafortune Stephane 
Published: March 2024

Abstract

We study the orbital stability of smooth solitary wave solutions of the Novikov equation, which is a Camassa-Holm type equation with cubic nonlinearities. These solitary waves are shown to exist as a one-parameter family (up to spatial translations) parameterized by their asymptotic endstate, and are encoded as critical points of a particular action functional. As an importantstep in our analysis we must study the spectrum the Hessian of this action functional, which turns out to be a nonlocal integro-differential operatoracting on $L^2(\mathbb{R})$. We provide a combination of analytical and numerical evidence that the necessary spectral hypotheses alwaysholds for the Novikov equation. Together with a detailed study of the associated Vakhitov-Kolokolov condition, our analysis indicates that all smooth solitary wave solutions of the Novikov equation are nonlinearly orbitally stable.
Infantile leigh-like syndrome: A rare presentation of biotin-thiamine responsive basal ganglia disease
Authors: Malmurugan Yathwin Kanagavel , Manokaran Ranjith Kumar , Pasupathy Umpathy , Rangasami Rajeswaran 
Published: March 2024

Abstract

Background Biotin-thiamine-responsive basal ganglia disease (BTRBGD) is an extremely rare, inherited autosomal recessive neurometabolic disorder associated with episodes of sub-acute encephalopathy and seizures. It occurs due to impaired transport of thiamine across the blood-brain barrier and is considered as a treatable condition, if biotin and thiamine supplementation is initiated early. BTRBGD classically presents in childhood, but very rarely can also present as infantile form and adult Wernicke-like encephalopathy. The infantile form, referred to as infantile leigh-like syndrome, is considered the most severe presentation with high mortality rates despite vitamin supplementation. Case presentation : A 3-month-old child presented with irritability and infantile spasms was found to have a pathogenic variant inSLC19A3gene and diagnosed with infantile BTRBGD. MRI brain showed symmetrical T2 hyper-intensities in the bilateral cerebellar hemispheres, thalami, and the basal ganglia. MR spectroscopy revealed an elevated inverted doublet lactate peak, with video EEG was suggestive of multifocal epileptiform abnormality. Patient was diagnosed with infantile BTRBGD through exome sequencing and was started on high-dose biotin and thiamine supplementation, resulting in resolution of the symptoms. Conclusion Neurologists and radiologists need to be aware of and consider suspicion of the disease entity in encephalopathic infants having the characteristic MRI pattern showing bilateral hyper-intensities in the basal ganglia along with elevated lactate, and consider initiating them on empirical high-dose biotin and thiamine supplementation even before acquiring results of the genetic testing as early initiation of treatment can slow the progression of the disease and have a positive impact on patient outcomes.
“Broken souls” vs. “mad ax man” – The portrayal of depression and schizophrenia in the German media. A comparison between 2010 and 2020
Authors: Sittner Madeleine , Rechenberg Theresia , Speerforck Sven , Angermeyer Matthias Claus , Schomerus Georg 
Published: March 2024

Abstract
Purpose
Population studies show the stigma of depression to diminish, while the stigma of schizophrenia increases. To find out whether this widening gap is reflected in media portrayal of both disorders, this study compares the portrayal of depression and schizophrenia in German print media in 2010 vs. 2020.
Methods
We conducted a qualitative content analysis using a mixed deductive-inductive approach to establish a category system. In total, we analyzed 854 articles with the summative approach by Mayring.
Results
The study found a widening gap in the portrayal of schizophrenia and depression in German media between 2010 and 2020. Schizophrenia was depicted increasingly negative between 2010 and 2020, covering more negative stereotypes and focusing on its biological causes. Depression received increased attention and more neutral und professional coverage, with a greater emphasis on psychosocial causes and discussion of treatment options.
Conclusion
By showing a widening gap the study highlights how media may shape public views on mental illnesses and reflects public attitudes at the same time. It emphasizes the need for responsible reporting to combat stigma and promote understanding. Therefore, the authors recommend a balanced coverage that includes accurate professional information about all mental illnesses.
An Analysis of Multi-Criteria Performance in Deep Learning-Based Medical Image Classification: A comprehensive review
Authors: Hisaria Samridhi , Sharma Pratham , Gupta Raksha , Sumalatha Konatham 
Published: March 2024

Abstract
Objective: Investigate the potential of deep learning, specifically Convolutional Neural Networks (CNNs), for disease detection and classification in medical image analysis.
Methods and procedures: Review various CNN architectures (ResNet, EfficientNet, MobileNet, VGG, DenseNet) on retinal fundus and brain tumor datasets, evaluating performance through loss, AUROC, accuracy, and precision.
Results: Retinal fundus: ResNet 152 achieved highest accuracy and precision, but also highest loss. Precisions indicate potential for improvement. Brain tumor: VGG 16 achieved highest accuracy and precision, but EfficientNet models displayed poor differentiation between positive and negative classes. CheXpert: MobileNet demonstrated best overall performance. No single model consistently outperformed others across all datasets.
Conclusion: Deep learning shows promise for medical image analysis, with varied performances across different architectures and datasets. No single ”best” model exists, necessitating careful selection based on specific disease and data characteristics. Precision needs improvement in many cases, highlighting an area for further research. EfficientNet models require further investigation for brain tumor analysis.
Clinical and Translational Impact Statement: The paper explores transfer learning’s impact on translating deep learning for medical image analysis, potentially boosting clinical impact. By leveraging pre-trained models and overcoming data limitations, it aims to improve accuracy, efficiency, andgeneralizability, paving the way for real-world medical applications.
Five-year Trends of Fungal Infections in Cirrhotic Patients: Nationwide Study From 2016-2020
Authors: Choday Silpa , Alomar Talal , Wong Robert John 
Published: March 2024

Abstract
Background: Patients with cirrhosis are susceptible to infections due to abnormalities in humoral and cell-mediated immunity. Importantly, fungal infections are associated with delayed diagnosis and high mortality rates, emphasizing the importance of performing fungal cultures and maintaining elevated levels of suspicion in this patient population.
Methods: This retrospective cohort study analyzes cirrhotic patients readmitted with bacterial and fungal infections and investigates outcomes, including in-hospital mortality and hospital resource utilization. Data was acquired from the Nationwide Readmission Database (NRD) from 2016 to 2020.
Results: The study included 393,195 index hospitalizations, with 102,505 30-day and 157,079 90-day readmissions from 2016 to 2020. The number of readmissions for spontaneous bacterial peritonitis (SBP) at 30 days is 8,478 and Spontaneous fungal peritonitis (SFP) is 3,106. The 90-day readmission for SBP is 15,690 and SFP is 5,798. The mean age was 57.9 years. The mean length of stay (LOS) for SBP 30-day readmission is 9.4, while SFP has ranged from 14.9 to 32.3 for various fungal infections, with the highest being Aspergillosis. SFP also has increased hospital charges and mortality rates compared with SBP(P<0.001).
Conclusions: It is observed that there is increased mortality, LOS, and hospital costs for SFP compared to SBP. This study emphasized the importance of considering SFP in the diagnostic workup as fungal infections, particularly for patients who do not respond to antibiotic treatment for SBP. Early recognition and administration of antifungals can be associated with improved outcomes in SFP.
Association between cerebral small vessel disease burden and early neurological deterioration in isolated pontine infarction
Authors: song xiaowei , Hou Duoduo , Zhao Hongliang , Zhang Siyuan , Pengmao Zhuoma , Wu Jian 
Published: March 2024

Abstract

Objective
To investigate the association between cerebral small vessel disease burden and early neurological deterioration (END) in stroke patients with isolated pontine infarction.
Methods
A total of 107 acute isolated pontine infarct patients within 24 hours of symptoms onset were included from a comprehensive stroke center, the mean age is 67 years old. Cerebral small vessel disease burden on brain MRI including white matter hyperintensities (WMH), lacunes, cerebral microbleeds (CMB), and enlarged perivascular spaces (EPVS) were evaluated in each patient. END was defined as an increase of ≥ 1 point on the motor National Institutes of Health Stroke Scale (NIHSS) or ≥ 2 points on the total NIHSS score within 72 hours from admission. T-test, chi-square test, and logistic regression were used for statistical analysis.
Results
33.6% (36/107) of them occurred END. Patients in the END group have a higher percentage of hyperlipidemia (66.7% vs. 43.7%, p = 0.024). over 50% of infarction mechanism can be attributed to basilar artery branch disease in both groups (58.3% in the END group and 50.7% in the non-END group). In multivariate regression, neither cerebral small vessel disease total burden nor WMH, CMB, lacunes, and EPVS were associated with END (all P >0.05). Basilar artery intraplaque hemorrhage on vessel wall imaging is associated with END independently (OR = 3.233, 95% CI 1.032–10.123, p = 0.044).
Conclusion
Neither cerebral small vessel disease burden nor its individual imaging features were associated with END in isolated pontine infarction patients. Basilar artery intraplaque hemorrhage is an independent predictor of END in this population.