bluuebunny/crossref_metadata_2025_split huggingface dataset:
Internal jugular vein tumor thrombus in papillary thyroid cancer: our institution’s experience and a systematic review of the literature
Authors: Laurin Bryce J. , Ballard Robert , Malik Ifthikar , Mitchell Janeil 
Published: February 2025

Papillary thyroid tumor thrombosis of the internal jugular vein (IJV) is a rarely observed phenomenon with fewer than 30 cases reported to date. The clinical features and underlying pathogenesis of tumor thrombosis are not well-elucidated. A PRISMA-compliant systematic review was conducted, yielding 20 studies eligible for analysis. Additionally, we describe a case of papillary thyroid cancer (PTC) tumor thrombus involving the IJV with solitary metastasis to the ipsilateral kidney. The majority of patients in the cohort presented in an asymptomatic state (n = 14) with variable timepoints in diagnosis: preoperative (n = 9), intraoperatively (n = 1), and postoperative period (n =11), up to 30 years post-thyroidectomy. Primary tumor sizes ranged widely, with a mean of 4.22 cm ± 2.64cm. Most patients (85.7%) presented with nodal involvement and a few (n =4) had distant metastases with pulmonary involvement most commonly reported. Open tumor thrombectomy was performed in 10 (52.6%) cases and extensive vascular reconstruction was required in 8 (42%). Adjuvant treatment including radioactive iodine ablation (36.8%) and external beam radiation (21.1%) was also employed. Patient clinical factors, presentation, diagnosis, and management of PTC vascular tumor thrombus are heterogeneous. Tumor thrombus occurred in patients with solitary, small primary tumors and patients with heavy locoregional disease burden and presents as isolated and extensive thrombotic burden, the latter requiring complex open cardiovascular reconstruction in some patients. The rarity of the disease and diverse clinical presentation reporting remains a challenge in the understanding of pathogenesis, optimal management, and outcomes in PTC-related thrombosis.
Integrated spatial cost-benefit analysis of large-scale mangrove conservation and restoration in Indonesia
Authors: van Zanten Boris , Brander Luke McKinnon , Castaneda Juan-Pablo , Herrera Diego , Kaczan David , Brown Benjamin M. , Smith Ian Andrew , Jongman Brenden 
Published: February 2025

Economic development in Indonesia has resulted in reduced poverty but has also been accompanied by significant pressure on natural capital, including its mangrove forests. In recognition of the role of mangroves in providing coastal protection and the delivery of other ecosystem services, the Government of Indonesia has engaged in several policy actions, among others the use of nature based solutions and has set a target to restore or enhance the protection of 600,000 ha of mangroves by 2025. The objective of the analysis presented in this paper is to inform the design and development of a national-scale mangrove conservation and restoration policy for Indonesia through a spatially explicit cost-benefit analysis. The analytical framework involves the integration of maps, data and models to estimate the costs and benefits of mangrove restoration at a high spatial resolution. On the benefit side, we make use of meta-analytic value transfer methods to value changes in fisheries, raw materials, coastal protection, carbon sequestration and avoided emissions, and mangrove tourism. On the cost side, we assess the opportunity and implementation costs using country specific data. Through a spatial overlay of cost and benefit estimates, cost-benefit indicators are calculated per district for investing in additional mangrove restoration and conservation. Using a discount rate of 5.5% and a 30-year project lifetime, the benefit-cost ratios of mangrove conservation and restoration are found to be &gt;1 in most districts indicating positive social returns on investment. We find that mangrove conservation generally has higher benefit-cost ratios than restoration due to both the higher cost of restoration implementation and the additional time that it takes for restored mangroves to deliver ecosystem services. The spatially explicit framework to estimate district-level costs and benefits reveals tradeoffs with agriculture and aquaculture and enables the evaluation of investment scenarios and the spatial prioritisation of investments in mangrove conservation and restoration across Indonesia. Strategic allocation of conservation and restoration investments across districts can potentially significantly increase the economic viability of this nature-based solution.
Case Report: Long-term suppression of relapses by dimethyl fumarate in a relapsing pediatric patient with myelin oligodendrocyte glycoprotein antibody–associated disease manifesting as acute disseminated encephalomyelitis, unilateral optic neuritis, and seizure episodes
Authors: Tahara Masayuki , Awaya Tomonari , Saito Keisuke , Sawada Hideyuki 
Published: February 2025

BackgroundMyelin oligodendrocyte glycoprotein (MOG) antibody-associated diseases (MOGAD), which has been recognized as a distinct entity in patients with neuromyelitis optica spectrum disorders, often presents with acute disseminated encephalomyelitis (ADEM) symptoms in pediatric patients. Appropriate treatment based on accurate diagnosis is challenging in relapsing pediatric patients with MOGAD.Case PresentationAn 11-year-old girl experienced relapses four times, exhibiting brainstem symptoms, an ADEM episode, seizures, and optic neuritis (ON). She was initially diagnosed with multiple sclerosis and received interferon beta-1a therapy with a mild effect on relapse suppression. She was then transferred from the pediatric department to the department of neurology of our hospital. Two months before her referral visit, she experienced left optic neuritis, and her annualized relapse rate reached 0.6. She desired to switch from the injectable treatment to oral dimethyl fumarate (DMF) administration. At that time, she was found to be seropositive for MOG antibody, but after that had no relapses for more than five years. Moreover, her seropositivity for serum MOG-antibody turned out to be seronegative.ConclusionsDMF showed long-term effects on suppressing relapses in a pediatric patient with MOGAD, revealing its potential as a treatment option for such patients.
Prognostic nutritional index as an independent risk factor for disease progression in patients with IgA nephropathy
Authors: Wang Siqing , Zhou Huan , Dong Lingqiu , Qin Wei 
Published: February 2025

BackgroundImmunoglobulin A nephropathy (IgAN), a common primary glomerulonephritis worldwide, has been investigated, and complex factors are involved in disease progression. A group of evidence emerged that nutrition status plays a nonsubstitutable role in the management of chronic kidney disease. Meanwhile, a novel marker of nutrition and inflammation, the prognostic nutritional index (PNI), has been studied in various diseases. Whether PNI can predict the renal outcome of patients with IgAN remains unclear. Thus, we aimed to evaluate the relationships between PNI and clinicopathologic features, renal progression and renal prognosis in patients with IgAN.MethodsA total of 1,377 patients with biopsy-proven IgAN were recruited for this retrospective study. All patients were divided into two groups based on the cutoff value of PNI: the high group (PNI ≥ 47.1, n = 886) and the low group (PNI &lt; 47.1, n = 491). Our study endpoint was end-stage renal disease [estimated glomerular filtration rate (eGFR) &lt; 15 mL/min/1.73 m2 or performance of renal replacement therapy]. A correlation test was conducted to explore the relationship between PNI and other important clinicopathologic parameters. The predictive value was determined by the area under the receiver operating characteristic curve (AUROC). Kaplan–Meier and Cox proportional hazards analyses were performed to assess the value of PNI in predicting renal progression and prognosis.ResultsThe correlation test revealed that PNI was positively associated with eGFR (r = 0.16, p &lt; 0.001) and negatively related to 24-h proteinuria (r = −0.387, p &lt; 0.001). Multivariate Cox regression analysis indicated that low PNI was an independent risk factor for IgAN patients even after adjusting for important clinical and pathological parameters (HR, 0.664; 95% CI, 0.443–0.994; p = 0.047). Kaplan–Meier analysis showed that low PNI was significantly correlated with severe renal outcome in patients with IgAN (p &lt; 0.001). Moreover, the subgroup analyses of Kaplan–Meier survival demonstrated that low PNI predicted severe renal prognosis in different types of IgAN patients when considering the level of glomerular filtration rate, 24 h proteinuria and hemoglobin.ConclusionPNI is associated with renal function and pathologic lesions in IgAN patients and could be a novel marker for the evaluation of renal progression and prognosis.
Case report: The first case of Mpox in a patient with HIV in Burundi
Authors: Sabushimike Doriane , Nimburanira Marc , Freeman Esther , Simbarariye Déo , Nzeyimana Armel , Enbiale Wendemagegn 
Published: February 2025

IntroductionMpox is a viral disease that primarily affects individuals living in endemic regions. The 2022 outbreak notably impacted HIV-positive individuals, who were disproportionately affected. This report describes the first confirmed case of Mpox in Burundi, involving an HIV-positive patient with advanced disease. The case was confirmed by the national laboratory during the early stages of the 2024 outbreak.Case presentationA 50-year-old woman presented with pustular and necrotic skin lesions, vulval ulcers, and systemic symptoms. She was diagnosed with HIV upon initial evaluation and, despite an initial misdiagnosis, was ultimately confirmed to have a co-infection of Mpox. Complications included secondary bacterial infection, severe pain, and prolonged healing.ConclusionHIV coinfection can exacerbate Mpox, leading to severe clinical presentations. Therefore, early recognition and screening for HIV in Mpox patients are essential.
The association between adult IgA vasculitis and cancer: a prospective observational study
Authors: Hočevar Alojzija , Jurčič Vesna , Rotar Žiga 
Published: February 2025

ObjectivesCancer has been reported as a potential trigger for IgA vasculitis (IgAV) in adults; however, data on this topic are scarce. The aim of our study was to examine the frequency and location of cancer in adults with IgAV.MethodsWe included 295 IgAV patients diagnosed between January 2010 and June 2021 and followed at our secondary/tertiary rheumatology centre. Cancer episodes were recorded and classified into three groups according to their temporal relation to IgAV: (1) cancer diagnosed prior to IgAV; (2) cancer diagnosed concurrently with IgAV; and (3) cancer diagnosed during IgAV follow-up. The term IgAV associated with cancer (IgAV-CA) was used for IgAV cases presenting while cancer was active or diagnosed within a three-year period before or after the IgAV diagnosis.ResultsForty-seven IgAV patients (15.9%) developed a total of 56 different cancers. Of these, 43 cancers (76.8%) preceded the IgAV diagnosis, 3 cancers (5.3%) were diagnosed concurrently with IgAV, and 10 cancers (17.9%) were diagnosed during the IgAV follow-up. Twenty-two IgAV patients (7.5%) who developed a total of 24 cancers (42.9%) could be classified as IgAV-CA cases. The three most frequently diagnosed cancers in the IgAV-CA group were prostatic, renal and bladder cancer, together accounting for 50% of cancers. Age and smoking were associated with concurrent cancers, and arthritis represented a risk factor for cancer development during IgAV follow-up.ConclusionIgAV cases associated with cancer represent 7% of adult IgAV cases. In elderly patients, careful examination of the urogenital tract is warranted when cancer is suspected.
Composite quantile regression approach to batch effect correction in microbiome data
Authors: Park Jiwon , Park Taesung 
Published: February 2025

BackgroundBatch effects refer to data variations that arise from non-biological factors such as experimental conditions, equipment, and external factors. These effects are considered significant issues in the analysis of biological data since they can compromise data consistency and distort actual biological differences, which can severely skew the results of downstream analyses.MethodIn this study, we introduce a new approach that comprehensively addresses two types of batch effects: “systematic batch effects” which are consistent across all samples in a batch, and “nonsystematic batch effects” which vary depending on the variability of operational taxonomic units (OTUs) within each sample in the same batch. To address systematic batch effects, we apply a negative binomial regression model and correct for consistent batch influences by excluding fixed batch effects. Additionally, to handle nonsystematic batch effects, we employ composite quantile regression. By adjusting the distribution of OTUs to be similar based on a reference batch selected using the Kruskal-Walis test method, we consider the variability at the OTU level.ResultsThe performance of the model is evaluated and compared with existing methods using PERMANOVA R-squared values, Principal Coordinates Analysis (PCoA) plots and Average Silhouette Coefficient calculated with diverse distance-based metrics. The model is applied to three real microbiome datasets: Metagenomic urine control data, Human Immunodeficiency Virus Re-analysis Consortium data, and Men and Women Offering Understanding of Throat HPV study data. The results demonstrate that the model effectively corrects for batch effects across all datasets.
Prognostic value of glycolipid metabolism index on complications and mechanical ventilation in intensive care unit patients with intracerebral hemorrhage: a retrospective cohort study using the MIMIC-IV database
Authors: Zeng Yile , Lin Long , Chen Jianlong , Cai Shengyu , Lai Jinqing , Hu Weipeng , Liu Yiqi 
Published: February 2025

ObjectiveThis study aimed to evaluate the predictive capability of glycolipid metabolism index (triglyceride-glucose index, TyG; atherogenic index of plasma, AIP; triglyceride to high-density lipoprotein cholesterol ratio, TG/HDL-C; and non-HDL-C to HDL-C ratio, NHHR) for complications and ventilator use in patients with intracerebral hemorrhage (ICH) admitted to the intensive care unit (ICU).MethodsPatients with ICH requiring ICU admission were selected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Outcomes assessed included incidence of complications and use of ventilator support. Multivariate logistic regression, receiver operating characteristic (ROC) analysis, and restricted cubic spline were employed to investigate the relationship between glycolipid metabolism index and clinical outcomes in ICH patients.ResultsA total of 733 patients were included. Multivariate logistic regression analysis revealed that elevated TyG, AIP, and TG/HDL-C levels were associated with increased incidence of complications and prolonged ventilator use. ROC curve analysis demonstrated that TyG (AUC 0.646) exhibited the strongest predictive ability for multiple complications in ICH patients. Further multiple regression analysis identified TG/HDL-C as an independent predictor of deep vein thrombosis, while TyG, AIP, and TG/HDL-C independently predicted pulmonary embolism, and TyG, AIP, NHHR, and TG/HDL-C independently predicted acute kidney injury. Moreover, ventilator use further heightened the risk of multiple complications in ICU patients with elevated glycolipid metabolism index.ConclusionGlycolipid metabolism index represent promising and readily accessible biomarkers for predicting multiple complications and ventilator use in ICU patients with ICH.
Correlation analysis of upper limb muscle activation in the frequency domain in wheelchair fencers
Authors: Błaszczyszyn Monika , Piechota Katarzyna , Borysiuk Zbigniew , Kręcisz Krzysztof , Zmarzły Dariusz 
Published: February 2025

BackgroundThe study includes a correlation analysis of EMG signals of upper limb muscle activity in wheelchair fencers. The aim of the study was to investigate neuromuscular conduction in wheelchair fencers using the EMG signal from their upper limb muscles.MethodsWavelet transform analysis was used to examine the biosignals. The recorded EMG signals were subjected to time-frequency transformations. The scalograms were determined using the continuous wavelet transform. Based on the analysis, time-frequency coherence maps were extracted to determine validation in the frequency bands: 2–16 Hz, 17–30 Hz, and 31–60 Hz. The study participants were 16 wheelchair fencers, members of the Polish Paralympic Team, in two disability categories: 7 in category A and 9 in category B. Coherence was calculated for frequencies up to 60 Hz.ResultsThe analysis revealed the individual time-dependent coherence between two signals for different frequencies during the work cycle of the antagonist muscles of the arm (biceps/triceps) and forearm (flexor/extensor carpi radialis). A significant difference in alpha coherence (2–16 Hz) occurred in the group of forearm muscles in the frequency band of 2–16 Hz, both for G (p = 0.042) and M (p = 0.031) parameters (G: A - 0.08 Hz, B - 0.04 Hz; M: A - 0.51 and B - 0.42). Its peaks were observed during the fencing action cycle. Some differences in gamma coherence were also found in the EMG signals of the forearm muscles in the 31–60 Hz frequency band were statistically significant (p = 0.031): 0.43 in group A and 0.36 in group B.ConclusionThe results showed the neuromuscular conduction, where alpha coherence reflects the reticulospinal tract responsible for the excitation of the distal muscles of the wrist and hand, while gamma coherence results from cortical signals. It is related to efferent conduction and reflects corticomuscular coupling. Frequency domain coherence analysis determines the strength of intermuscular synchronization, allowing a comprehensive investigation of the neural mechanisms underlying motor recovery. It maps separate neural pathways for arm and hand control.
Prescription patterns of supportive care medications among children receiving chemotherapy treatments at a major referral hospital in Tanzania: where are we in managing chemotherapy-induced toxicities?
Authors: Katabalo Deogratias M. , Abraham Melina , Kidenya Benson R. , Liwa Antony , Schroeder Kristin 
Published: February 2025

BackgroundCancer chemotherapy is a treatment that systematically kills cancer cells but causes expected side effects, known as chemotherapy-induced toxicities. These toxicities are managed with supportive care medications. This study aimed to determine the prescription patterns of supportive care medications in children receiving chemotherapy at a major referral hospital in Tanzania.MethodologyA hospital-based descriptive cross-sectional study was conducted at Bugando Medical Centre (BMC). The study analyzed 104 prescription slips of pediatric cancer patients receiving chemotherapy and qualitatively assessed national guidelines and disease-specific protocols used in guiding treatment. Data were cleaned in Microsoft Excel, analyzed using STATA version 15, and presented as frequencies, percentages, and narrative summaries.ResultsOndansetron (84.6%) and pre-hydration normal saline (20.2%) were the most prescribed pre-chemotherapy supportive care medications. Similarly, oral ondansetron (80.8%) and post-hydration normal saline (22.1%) were the most prescribed post-chemotherapy medications. Few prescriptions included a combination of antiemetics, fluids, and proton pump inhibitors for regimens with multiple chemotherapeutic agents. National cancer treatment guidelines lacked detailed sections on supportive care medications, leaving prescribing decisions to clinicians, while Burkitt’s lymphoma and nephroblastoma protocols offered more detailed guidance.ConclusionAntiemetics and hydration fluids dominated supportive care prescriptions. Significant gaps were identified in the inclusion of supportive care in national guidelines, with reliance on disease-specific protocols. These findings highlight the need for standardized, evidence-based supportive care guidelines tailored to resource-limited settings.
Deep learning model for the early prediction of pathologic response following neoadjuvant chemotherapy in breast cancer patients using dynamic contrast-enhanced MRI
Authors: Lv Meng , Zhao BinXin , Mao Yan , Wang Yongmei , Su Xiaohui , Zhang Zaixian , Wu Jie , Gao Xueqiang , Wang Qi 
Published: February 2025

PurposeThis study aims to investigate the diagnostic accuracy of various deep learning methods on DCE-MRI, in order to provide a simple and accessible tool for predicting pathologic response of NAC in breast cancer patients.MethodsIn this study, we enrolled 313 breast cancer patients who had complete DCE-MRI data and underwent NAC followed by breast surgery. According to Miller-Payne criteria, the efficacy of NAC was categorized into two groups: the patients achieved grade 1-3 of Miller-Payne criteria were classified as the non-responders, while patients achieved grade 4-5 of Miller-Payne criteria were classified as responders. Multiple deep learning frameworks, including ViT, VGG16, ShuffleNet_v2, ResNet18, MobileNet_v2, MnasNet-0.5, GoogleNet, DenseNet121, and AlexNet, were used for transfer learning of the classification model. The deep learning features were obtained from the final fully connected layer of the deep learning models, with 256 features extracted based on DCE-MRI data for each patient of each deep learning model. Various machine-learning techniques, including support vector machine (SVM), K-nearest neighbor (KNN), RandomForest, ExtraTrees, XGBoost, LightGBM, and multiple-layer perceptron (MLP), were employed to construct classification models.ResultsWe utilized various deep learning models to extract features and subsequently constructed machine learning models. Based on the performance of different machine learning models’ AUC values, we selected the classifiers with the best performance. ResNet18 exhibited superior performance, with an AUC of 0.87 (95% CI: 0.82 - 0.91) and 0.87 (95% CI: 0.78 - 0.96) in the train and test cohorts, respectively.ConclusionsUsing pre-treatment DCE-MRI images, our study trained multiple deep models and developed the best-performing DLR model for predicting pathologic response of NAC in breast cancer patients. This prognostic tool provides a dependable and impartial basis for effectively identifying breast cancer patients who are most likely to benefit from NAC before its initiation. At the same time, it can also identify those patients who are insensitive to NAC, allowing them to proceed directly to surgical treatment and prevent the risk of losing the opportunity for surgery due to disease progression after NAC.