bluuebunny/crossref_metadata_2025 huggingface dataset:
Assessing the Interpretation of Molecular Test Results in the Diagnosis of Bloodstream Infections
Authors: Słabisz Natalia , Leśnik Patrycja , Żybura-Wszoła Katarzyna , Dudek-Wicher Ruth , Nawrot Urszula , Majda Jacek
Published: May 2024

A retrospective study at the 4th Military Clinical Hospital in Wroclaw, Poland, assessed PCR testing alongside blood cultures to guide antimicrobial therapy decisions in hospitalized patients, to determine how much time the results of the molecular tests preceded conventional methods. Among 118 patients, Staphylococcus aureus (37%) and Escherichia coli (21%) were the most common bloodstream infection agents. Blood cultures utilized the BacT/ALERT 3D system, and molecular diagnostics were conducted using the FilmArray platform with the BIOFIRE BCID2 panel. Methicillin susceptibility was observed in 66% of S. aureus strains, while 26% of Gram-negative bacilli exhibited an ESBL phenotype. Therapeutic decisions based on molecular test results were often incorrect for S. aureus infections, particularly MSSA (64.5%), but generally accurate for Gram-negative bacilli. The median times from positive blood culture to BCID2 and pathogen identification/susceptibility were 10 h and 52 h, respectively. Molecular diagnostics facilitated faster initiation of appropriate antibiotic therapy, highlighting the need to educate medical staff on proper interpretation. Consulting within an antimicrobial stewardship program (ASP) could enhance the benefits of implementing molecular methods in bloodstream infection diagnostics.
Osteochondritis Dissecans of the Elbow in Overhead Athletes: A Comprehensive Narrative Review
Authors: Matković Andro , Ferenc Thomas , Dimnjaković Damjan , Jurjević Nikolina , Vidjak Vinko , Matković Branka
Published: May 2024

Osteochondritis dissecans (OCD) of the elbow mainly occurs in overhead athletes (OHAs). This narrative review aimed to comprehensively analyze the epidemiological data, etiological factors, clinical and imaging features, treatment options, and outcomes of OHAs with the diagnosis of elbow OCD. A literature search was performed in PubMed/MEDLINE, Scopus, and Web of Science. Individuals with elbow OCD were usually 10–17 years of age with incidence and prevalence varying between studies, depending on the sport activity of the patients. The etiology of OCD lesions is multifactorial, and the main causes are believed to be repetitive trauma, the biomechanical disproportion of the articular surfaces, poor capitellar vascular supply, and inflammatory and genetic factors. Athletes usually presented with elbow pain and mechanical symptoms. The mainstay for the diagnosis of elbow OCD is MRI. The treatment of elbow OCD lesions should be conservative in cases of stable lesions, while various types of surgical treatment are suggested in unstable lesions, depending mainly on the size and localization of the lesion. The awareness of medical practitioners and the timely diagnosis of OCD lesions in OHAs are key to favorable outcomes.
Innovative Machine Learning Strategies for Early Detection and Prevention of Pregnancy Loss: The Vitamin D Connection and Gestational Health
Authors: Sufian Md Abu , Hamzi Wahiba , Hamzi Boumediene , Sagar A. S. M. Sharifuzzaman , Rahman Mustafizur , Varadarajan Jayasree , Hanumanthu Mahesh , Azad Md Abul Kalam
Published: May 2024

Early pregnancy loss (EPL) is a prevalent health concern with significant implications globally for gestational health. This research leverages machine learning to enhance the prediction of EPL and to differentiate between typical pregnancies and those at elevated risk during the initial trimester. We employed different machine learning methodologies, from conventional models to more advanced ones such as deep learning and multilayer perceptron models. Results from both classical and advanced machine learning models were evaluated using confusion matrices, cross-validation techniques, and analysis of feature significance to obtain correct decisions among algorithmic strategies on early pregnancy loss and the vitamin D serum connection in gestational health. The results demonstrated that machine learning is a powerful tool for accurately predicting EPL, with advanced models such as deep learning and multilayer perceptron outperforming classical ones. Linear discriminant analysis and quadratic discriminant analysis algorithms were shown to have 98 % accuracy in predicting pregnancy loss outcomes. Key determinants of EPL were identified, including levels of maternal serum vitamin D. In addition, prior pregnancy outcomes and maternal age are crucial factors in gestational health. This study’s findings highlight the potential of machine learning in enhancing predictions related to EPL that can contribute to improved gestational health outcomes for mothers and infants.
MRI Assessed Placental Location as a Diagnostic Tool of Placental Invasiveness and Maternal Peripartum Morbidity
Authors: Bourgioti Charis , Konidari Marianna , Eleftheriades Makarios , Theodora Marianna , Nikolaidou Maria Evangelia , Zafeiropoulou Konstantina , Tzavara Chara , Fotopoulos Stavros , Daskalakis George , Moulopoulos Lia Angela
Published: May 2024

Placenta accreta spectrum (PAS) disorder is one of the leading causes of peripartum maternal morbidity and mortality; its early identification during pregnancy is of utmost importance to ensure the optimal clinical outcome. The aim of the present study is to investigate the possible association of the presence and type/location of placenta previa on MRI with PAS and maternal peripartum outcome. One hundred eighty-nine pregnant women (mean age: 35 years; mean gestational age: 32 weeks) at high risk for PAS underwent a dedicated placental MRI. All women underwent a C-section within 6 weeks from the MRI. All MRIs were evaluated by two experienced genitourinary radiologists for presence, type (complete/partial vs. marginal/low lying), and location (anterior vs. anterior-posterior vs. posterior) of placenta previa. Statistical analysis was performed for possible association of type/location of previa with placental invasiveness and peripartum outcomes. Intraoperative information was used as a reference standard. Complete/partial previa was detected in 143/189 (75.6%) and marginal/low lying previa in 33/189 (17.5%) women; in 88/189 (46.6%) women, the placenta had anterior–posterior, in 54/189 (28.6%) anterior and in 41/189 (21.7%) posterior. Complete/partial previa had an at least 3-fold probability of invasiveness and was more frequently associated with unfavorable peripartum events, including massive intraoperative blood loss or hysterectomy, compared to low-lying/marginal placenta. Posterior placental location was significantly associated with lower rates of PAS and better clinical outcomes. In conclusion, the type and location of placenta previa shown with MRI seems to be associated with severity of complications during delivery and should be carefully studied.
Examination of Cardiac Activity with ECG Monitoring Using Heart Rate Variability Methods
Authors: Georgieva-Tsaneva Galya , Gospodinova Evgeniya , Cheshmedzhiev Krasimir
Published: May 2024

The paper presents a system for analyzing cardiac activity with the possibility of continuous and remote monitoring. The created sensor mobile device monitors heart activity by means of the convenient and imperceptible registration of cardiac signals. At the same time, the behavior of the human body is also monitored through the accelerometer and gyroscope built into the device, thanks to which it is possible to signal in the event of loss of consciousness or fall (in patients with syncope). Conducting real-time cardio monitoring and the analysis of recordings using various mathematical methods (linear, non-linear, and graphical) enables the research, accurate diagnosis, timely assistance, and correct treatment of cardiovascular diseases. The paper examines the recordings of patients diagnosed with arrhythmia and syncope recorded by electrocardiography (ECG) sensors in real conditions. The obtained results are subjected to statistical analysis to determine the accuracy and significance of the obtained results. The studies show significant deviations in the patients with arrhythmia and syncope regarding the obtained values of the studied parameters of heart rate variability (HRV) from the accepted normal values (for example, the root mean square of successive differences between normal heartbeats (RMSSD) in healthy individuals is 24.02 ms, while, in patients with arrhythmia (6.09 ms) and syncope (5.21 ms), it is much lower). The obtained quantitative and graphic results identify some possible abnormalities and demonstrate disorders regarding the activity of the autonomic nervous system, which is directly related to the work of the heart.
Novel Variants Linked to the Prodromal Stage of Parkinson’s Disease (PD) Patients
Authors: Badawy Marwa T. , Salama Aya A. , Salama Mohamed
Published: May 2024

Background and objective: The symptoms of most neurodegenerative diseases, including Parkinson’s disease (PD), usually do not occur until substantial neuronal loss occurs. This makes the process of early diagnosis very challenging. Hence, this research used variant call format (VCF) analysis to detect variants and novel genes that could be used as prognostic indicators in the early diagnosis of prodromal PD. Materials and Methods: Data were obtained from the Parkinson’s Progression Markers Initiative (PPMI), and we analyzed prodromal patients with gVCF data collected in the 2021 cohort. A total of 304 participants were included, including 100 healthy controls, 146 prodromal genetic individuals, 21 prodromal hyposmia individuals, and 37 prodromal individuals with RBD. A pipeline was developed to process the samples from gVCF to reach variant annotation and pathway and disease association analysis. Results: Novel variant percentages were detected in the analyzed prodromal subgroups. The prodromal subgroup analysis revealed novel variations of 1.0%, 1.2%, 0.6%, 0.3%, 0.5%, and 0.4% for the genetic male, genetic female, hyposmia male, hyposmia female, RBD male, and RBD female groups, respectively. Interestingly, 12 potentially novel loci (MTF2, PIK3CA, ADD1, SYBU, IRS2, USP8, PIGL, FASN, MYLK2, USP25, EP300, and PPP6R2) that were recently detected in PD patients were detected in the prodromal stage of PD. Conclusions: Genetic biomarkers are crucial for the early detection of Parkinson’s disease and its prodromal stage. The novel PD genes detected in prodromal patients could aid in the use of gene biomarkers for early diagnosis of the prodromal stage without relying only on phenotypic traits.
Diffusion-Weighted Imaging for Skin Pathologies of the Breast—A Feasibility Study
Authors: Skwierawska Dominika , Laun Frederik B. , Wenkel Evelyn , Kapsner Lorenz A. , Janka Rolf , Uder Michael , Ohlmeyer Sabine , Bickelhaupt Sebastian
Published: May 2024

Several breast pathologies can affect the skin, and clinical pathways might differ significantly depending on the underlying diagnosis. This study investigates the feasibility of using diffusion-weighted imaging (DWI) to differentiate skin pathologies in breast MRIs. This retrospective study included 88 female patients who underwent diagnostic breast MRI (1.5 or 3T), including DWI. Skin areas were manually segmented, and the apparent diffusion coefficients (ADCs) were compared between different pathologies: inflammatory breast cancer (IBC; n = 5), benign skin inflammation (BSI; n = 11), Paget’s disease (PD; n = 3), and skin-involved breast cancer (SIBC; n = 11). Fifty-eight women had healthy skin (H; n = 58). The SIBC group had a significantly lower mean ADC than the BSI and IBC groups. These differences persisted for the first-order features of the ADC (mean, median, maximum, and minimum) only between the SIBC and BSI groups. The mean ADC did not differ significantly between the BSI and IBC groups. Quantitative DWI assessments demonstrated differences between various skin-affecting pathologies, but did not distinguish clearly between all of them. More extensive studies are needed to assess the utility of quantitative DWI in supplementing the diagnostic assessment of skin pathologies in breast imaging.
Seeing and Sensing the Hepatorenal Syndrome (HRS): The Growing Role of Ultrasound-Based Techniques as Non-Invasive Tools for the Diagnosis of HRS
Authors: Tăluță Cornelia , Ștefănescu Horia , Crișan Dana
Published: May 2024

More than half of patients hospitalized with liver cirrhosis are dealing with an episode of acute kidney injury; the most severe pattern is hepatorenal syndrome due to its negative prognosis. The main physiopathology mechanisms involve renal vasoconstriction and systemic inflammation. During the last decade, the definition of hepatorenal syndrome changed, but the validated criteria of diagnosis are still based on the serum creatinine level, which is a biomarker with multiple limitations. This is the reason why novel serum and urinary biomarkers have been intensively studied in recent years. Meanwhile, the imaging studies that use shear wave elastography are using renal stiffness as a surrogate for an early diagnosis. In this article, we focus on the physiopathology definition and highlight the novel tools used in the diagnosis of hepatorenal syndrome.
Inferior Frontal Sulcal Hyperintensities on Brain MRI Are Associated with Amyloid Positivity beyond Age—Results from the Multicentre Observational DELCODE Study
Authors: Dörner Marc , Seebach Katharina , Heneka Michael T. , Menze Inga , von Känel Roland , Euler Sebastian , Schreiber Frank , Arndt Philipp , Neumann Katja , Hildebrand Annkatrin , John Anna-Charlotte , Tyndall Anthony , Kirchebner Johannes , Tacik Pawel , Jansen Robin , Grimm Alexander , Henneicke Solveig , Perosa Valentina , Meuth Sven G. , Peters Oliver , Hellmann-Regen Julian , Preis Lukas , Priller Josef , Spruth Eike Jakob , Schneider Anja , Fliessbach Klaus , Wiltfang Jens , Jessen Frank , Rostamzadeh Ayda , Glanz Wenzel , Schulze Jan Ben , Schiebler Sarah Lavinia Florence , Buerger Katharina , Janowitz Daniel , Perneczky Robert , Rauchmann Boris-Stephan , Teipel Stefan , Kilimann Ingo , Laske Christoph , Munk Matthias H. , Spottke Annika , Roy-Kluth Nina , Wagner Michael , Frommann Ingo , Lüsebrink Falk , Dechent Peter , Hetzer Stefan , Scheffler Klaus , Kleineidam Luca , Stark Melina , Schmid Matthias , Ersözlü Ersin , Brosseron Frederic , Ewers Michael , Schott Björn H. , Düzel Emrah , Ziegler Gabriel , Mattern Hendrik , Schreiber Stefanie , Bernal Jose
Published: May 2024

Inferior frontal sulcal hyperintensities (IFSHs) on fluid-attenuated inversion recovery (FLAIR) sequences have been proposed to be indicative of glymphatic dysfunction. Replication studies in large and diverse samples are nonetheless needed to confirm them as an imaging biomarker. We investigated whether IFSHs were tied to Alzheimer’s disease (AD) pathology and cognitive performance. We used data from 361 participants along the AD continuum, who were enrolled in the multicentre DELCODE study. The IFSHs were rated visually based on FLAIR magnetic resonance imaging. We performed ordinal regression to examine the relationship between the IFSHs and cerebrospinal fluid-derived amyloid positivity and tau positivity (Aβ42/40 ratio ≤ 0.08; pTau181 ≥ 73.65 pg/mL) and linear regression to examine the relationship between cognitive performance (i.e., Mini-Mental State Examination and global cognitive and domain-specific performance) and the IFSHs. We controlled the models for age, sex, years of education, and history of hypertension. The IFSH scores were higher in those participants with amyloid positivity (OR: 1.95, 95% CI: 1.05–3.59) but not tau positivity (OR: 1.12, 95% CI: 0.57–2.18). The IFSH scores were higher in older participants (OR: 1.05, 95% CI: 1.00–1.10) and lower in males compared to females (OR: 0.44, 95% CI: 0.26–0.76). We did not find sufficient evidence linking the IFSH scores with cognitive performance after correcting for demographics and AD biomarker positivity. IFSHs may reflect the aberrant accumulation of amyloid β beyond age.
Utility of CT Radiomics and Delta Radiomics for Survival Evaluation in Locally Advanced Nasopharyngeal Carcinoma with Concurrent Chemoradiotherapy
Authors: Huang Yen-Cho , Huang Shih-Ming , Yeh Jih-Hsiang , Chang Tung-Chieh , Tsan Din-Li , Lin Chien-Yu , Tu Shu-Ju
Published: May 2024

Background: A high incidence rate of nasopharyngeal carcinoma (NPC) has been observed in Southeast Asia compared to other parts of the world. Radiomics is a computational tool to predict outcomes and may be used as a prognostic biomarker for advanced NPC treated with concurrent chemoradiotherapy. Recently, radiomic analysis of the peripheral tumor microenvironment (TME), which is the region surrounding the gross tumor volume (GTV), has shown prognostic usefulness. In this study, not only was gross tumor volume (GTVt) analyzed but also tumor peripheral regions (GTVp) were explored in terms of the TME concept. Both radiomic features and delta radiomic features were analyzed using CT images acquired in a routine radiotherapy process. Methods: A total of 50 patients with NPC stages III, IVA, and IVB were enrolled between September 2004 and February 2014. Survival models were built using Cox regression with clinical factors (i.e., gender, age, overall stage, T stage, N stage, and treatment dose) and radiomic features. Radiomic features were extracted from GTVt and GTVp. GTVp was created surrounding GTVt for TME consideration. Furthermore, delta radiomics, which is the longitudinal change in quantitative radiomic features, was utilized for analysis. Finally, C-index values were computed using leave-one-out cross-validation (LOOCV) to evaluate the performances of all prognosis models. Results: Models were built for three different clinical outcomes, including overall survival (OS), local recurrence-free survival (LRFS), and progression-free survival (PFS). The range of the C-index in clinical factor models was (0.622, 0.729). All radiomics models, including delta radiomics models, were in the range of (0.718, 0.872). Among delta radiomics models, GTVt and GTVp were in the range of (0.833, 0.872) and (0.799, 0.834), respectively. Conclusions: Radiomic analysis on the proximal region surrounding the gross tumor volume of advanced NPC patients for survival outcome evaluation was investigated, and preliminary positive results were obtained. Radiomic models and delta radiomic models demonstrated performance that was either superior to or comparable with that of conventional clinical models.
Intraoperative Ultrasound: Bridging the Gap between Laparoscopy and Surgical Precision during 3D Laparoscopic Partial Nephrectomies
Authors: Mihai Ionela , Dura Horatiu , Teodoru Cosmin Adrian , Todor Samuel Bogdan , Ichim Cristian , Grigore Nicolae , Mohor Cosmin Ioan , Mihetiu Alin , Oprinca George , Bacalbasa Nicolae , Tanasescu Denisa , Bratu Dan Georgian , Boicean Adrian , Oros Bogdan , Hasegan Adrian
Published: May 2024

The use of 3D laparoscopic partial nephrectomy has emerged as a cornerstone in the surgical arsenal for addressing renal tumors, particularly in managing challenging cases characterized by deeply seated tumors embedded within the renal parenchyma. In these intricate scenarios, the utilization of intraoperative ultrasound (IOUS) acquires paramount importance, serving as an indispensable tool for guiding and meticulously monitoring the surgical process in real time. To further explore the efficacy of IOUS-guided techniques, we conducted a retrospective study comparing outcomes in patients who underwent partial nephrectomy with IOUS guidance (n = 60) between 2020 and 2022 with a cohort from 2018 to 2019 without IOUS guidance (n = 25). Our comprehensive analysis encompassed various post-operative parameters, including the duration until food resumption, analgesia requirements, and length of the hospital stay. While these parameters exhibited comparable outcomes between the two groups, notable distinctions emerged in the intraoperative metrics. The IOUS-guided cohort demonstrated significantly reduced blood loss, a shorter median operative duration, and diminished ischemia time (p = 0.001). These compelling findings underscore the undeniable benefits of IOUS-guided techniques in not only facilitating the attainment of negative surgical margins but also in enhancing procedural safety and precision, thereby contributing to improved patient outcomes in the management of renal tumors.
Long-Term Effects of 0.1 mg Recombinant-Human-Thyrotropin-Stimulated Fixed-Dose Radioiodine Therapy in Patients with Recurrent Multinodular Goiter after Surgery
Authors: Angelopoulos Nicholas , Iakovou Ioannis , Effraimidis Grigoris , Livadas Sarantis
Published: May 2024

(1) Background: After thyroid malignancy is ruled out, treatment options for multinodular goiter patients include surgery, levothyroxine suppressive therapy, and 131-I therapy. Surgery effectively reduces goiter size but carries risks of surgical and anesthetic complications. 131-I therapy is the only nonsurgical alternative, but its effectiveness diminishes with goiter size and depends on iodine sufficiency. This study aimed to assess the efficacy and safety of 0.1 mg rhTSH as an adjuvant to a fixed dose of 131-I therapy in patients with a recurrence of large multinodular goiter, several years after the initial thyroidectomy. (2) Methods: 14 patients (13 females and 1 male), aged 59.14 ± 15.44 (range, 35–78 years) received 11mciu of 131-I, 24 h after the administration of 0.1 mg rhTSH. The primary endpoint was the change in thyroid volume (by ultrasound measurements) as well as in the diameter of the predominant nodule during a follow-up period of 10 years. Secondary endpoints were the alterations in thyroid function and potential adverse effects. (3) Results: A significant decrease in the volume of initial thyroid remnants (32.16 ± 16.66 mL) was observed from the first reevaluation (at 4 months, 23.12 ± 11.59 mL) as well as at the end of the follow-up period (10 years, 12.62 ± 8.76 mL), p < 0.01. A significant reduction in the dominant nodule was also observed (from 31.71 ± 10.46 mm in the beginning to 26.67 ± 11.05 mm). (4) Conclusions: Further investigation is needed since this approach could be attractive in terms of minimizing the potential risks of reoperation in these patients.
Emerging Trends in Left Ventricular Thrombus: A Comprehensive Review of Non-Ischemic and Ischemic Cardiopathies, Including Eosinophilic Myocarditis, Chagas Cardiomyopathy, Amyloidosis, and Innovative Anticoagulant Approaches
Authors: Colle Benjamin , Demeure Fabian , Higny Julien , Benoit Martin , Henry Jean-Philippe , Michaux Isabelle , Robaye Benoit , Xhaët Olivier , Gabriel Laurence , Guedes Antoine , Blommaert Dominique , Dulieu Nathalie , Berners Yannick , Wery Fabian , Droogmans Steven , Cosyns Bernard , Luchian Maria-Luiza
Published: May 2024

This comprehensive review explores the intricate aspects of left ventricular thrombus (LVT), a potential complication in both ischemic and non-ischemic cardiomyopathies. It provides a thorough understanding of left ventricular thrombus, revealing its uncommon incidence in the general population (7 cases per 10,000 patients), predominantly linked to ischemic heart diseases (ICMs) at an 80% prevalence rate. Diagnostic tools, notably transthoracic echocardiography (TTE) and cardiac magnetic resonance imaging (CMR), demonstrate varying sensitivity but remain indispensable in specific clinical contexts related to LVT as non-invasive diagnostic modalities. A detailed comparison between ICM patients and those with non-ischemic cardiomyopathy (NICM) who have left ventricular thrombus reveals subtle distinctions with significant clinical implications. This analysis underscores the importance of these imaging techniques in distinguishing between the two conditions. Additionally, we explored the occurrence of LVT in specific non-ischemic cardiomyopathies, including Takotsubo syndrome, hypertrophic cardiomyopathy, eosinophilic myocarditis, Chagas disease, cardiac amyloidosis, and several other conditions. The article further delves into anticoagulation strategies, thoroughly examining their impact on LVT regression and patient outcomes. Pharmacological interventions, with a focus on direct oral anticoagulants, emerge as promising alternatives; however, there is insufficient information on their efficiency and safety, especially in NICM population. In conclusion, this review highlights the complex nature of LVT, incorporating a range of etiopathogenic factors, diagnostic complexities, and evolving therapeutic approaches. It emphasizes the pressing need for ongoing research in this field.
Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer
Authors: Englisz Aleksandra , Smycz-Kubańska Marta , Mielczarek-Palacz Aleksandra
Published: May 2024

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers’ efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage.
Mucin-Producing Lobular Breast Carcinoma Metastasis to an Ovarian Fibroma: Histopathological and Immunohistochemical Analysis of a Rare Case and Literature Review
Authors: Faur Alexandra Corina , Gurban Camelia Vidiţa , Dăescu Ecaterina , Tîrziu Răzvan Vlad , Lazăr Daniela Cornelia , Ghenciu Laura Andreea
Published: May 2024

Breast cancer stands as the primary cause of cancer-related mortality among women worldwide, often presenting with distant metastases upon diagnosis. Ovarian metastases originating from breast cancer represent a range of 3–30% of all ovarian neoplasms. Case Report: Herein, we present the histopathological, histochemical, and immunohistochemical findings of a rare case involving mucin-producing lobular breast carcinoma metastasizing to an ovarian fibroma in an 82-year-old female previously diagnosed with lobular breast carcinoma. Histopathological examination of the excised tissues revealed a biphasic neoplasm characterized by tumor cells expressing AE-1/AE-3 cytokeratin, mammaglobin, GCDFP-15, inhibin, and calretinin. Positive mucin staining was observed using histochemical techniques, and reticulin fibers were demonstrated using the Gordon–Sweets technique. A final diagnosis of mucin-producing lobular breast carcinoma metastatic to a benign ovarian fibroma was rendered. Conclusion: The occurrence of metastatic breast carcinoma overlaid on an ovarian tumor represents a rare and diagnostically challenging scenario.
Effects of High-Dose Cyclophosphamide on Ultrastructural Changes and Gene Expression Profiles in the Cardiomyocytes of C57BL/6J Mice
Authors: Nishikawa Takuro , Miyahara Emiko , Yamazaki Ieharu , Ikawa Kazuro , Nakagawa Shunsuke , Kodama Yuichi , Kawano Yoshifumi , Okamoto Yasuhiro
Published: May 2024

The pathogenesis of cyclophosphamide (CY)-induced cardiotoxicity remains unknown, and methods for its prevention have not been established. To elucidate the acute structural changes that take place in myocardial cells and the pathways leading to myocardial damage under high-dose CY treatments, we performed detailed pathological analyses of myocardial tissue obtained from C57BL/6J mice subjected to a high-dose CY treatment. Additionally, we analysed the genome-wide cardiomyocyte expression profiles of mice subjected to the high-dose CY treatment. Treatment with CY (400 mg/kg/day intraperitoneally for two days) caused marked ultrastructural aberrations, as observed using electron microscopy, although these aberrations could not be observed using optical microscopy. The expansion of the transverse tubule and sarcoplasmic reticulum, turbulence in myocardial fibre travel, and a low contractile protein density were observed in cardiomyocytes. The high-dose CY treatment altered the cardiomyocyte expression of 1210 genes (with 675 genes upregulated and 535 genes downregulated) associated with cell–cell junctions, inflammatory responses, cardiomyopathy, and cardiac muscle function, as determined using microarray analysis (|Z-score| > 2.0). The expression of functionally important genes related to myocardial contraction and the regulation of calcium ion levels was validated using real-time polymerase chain reaction analysis. The results of the gene expression profiling, functional annotation clustering, and Kyoto Encyclopedia of Genes and Genomes pathway functional-classification analysis suggest that CY-induced cardiotoxicity is associated with the disruption of the Ca2+ signalling pathway.
Dual-Driven Learning-Based Multiple-Input Multiple-Output Signal Detection for Unmanned Aerial Vehicle Air-to-Ground Communications
Authors: Li Haihan , He Yongming , Zheng Shuntian , Zhou Fan , Yang Hongwen
Published: May 2024

Unmanned aerial vehicle (UAV) air-to-ground (AG) communication plays a critical role in the evolving space–air–ground integrated network of the upcoming sixth-generation cellular network (6G). The integration of massive multiple-input multiple-output (MIMO) systems has become essential for ensuring optimal performing communication technologies. This article presents a novel dual-driven learning-based network for millimeter-wave (mm-wave) massive MIMO symbol detection of UAV AG communications. Our main contribution is that the proposed approach combines a data-driven symbol-correction network with a model-driven orthogonal approximate message passing network (OAMP-Net). Through joint training, the dual-driven network reduces symbol detection errors propagated through each iteration of the model-driven OAMP-Net. The numerical results demonstrate the superiority of the dual-driven detector over the conventional minimum mean square error (MMSE), orthogonal approximate message passing (OAMP), and OAMP-Net detectors at various noise powers and channel estimation errors. The dual-driven MIMO detector exhibits a 2–3 dB lower signal-to-noise ratio (SNR) requirement compared to the MMSE and OAMP-Net detectors to achieve a bit error rate (BER) of 1×10−2 when the channel estimation error is −30 dB. Moreover, the dual-driven MIMO detector exhibits an increased tolerance to channel estimation errors by 2–3 dB to achieve a BER of 1×10−3.
Estimating Spatio-Temporal Building Power Consumption Based on Graph Convolution Network Method
Authors: Vontzos Georgios , Laitsos Vasileios , Charakopoulos Avraam , Bargiotas Dimitrios , Karakasidis Theodoros E.
Published: May 2024

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.
Relative and Combined Impacts of Climate and Land Use/Cover Change for the Streamflow Variability in the Baro River Basin (BRB)
Authors: Kassaye Shimelash Molla , Tadesse Tsegaye , Tegegne Getachew , Hordofa Aster Tesfaye , Malede Demelash Ademe
Published: May 2024

The interplay between climate and land use/cover significantly shapes streamflow characteristics within watersheds, with dominance varying based on geography and watershed attributes. This study quantifies the relative and combined impacts of land use/cover change (LULCC) and climate change (CC) on streamflow variability in the Baro River Basin (BRB) using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated with observed streamflow data from 1985 to 2014 and projected the future streamflow from 2041 to 2070 under two Shared Socio-Economic Pathway (i.e., SSP2-4.5 and SSP5-8.5) scenarios, based on the ensemble of four Coupled Model Intercomparison Project (CMIP6) models. The LULCC was analyzed through Google Earth Engine (GEE) and predicted for the future using the Land Change Modeler (LCM), revealing reductions in forest and wetlands, and increases in agriculture, grassland, and shrubland. Simulations show that the decrease in streamflow is attributed to LULCC, whereas an increase in flow is attributed to the impact of CC. The combined impact of LULCC and CC results in a net increase in streamflow by 9.6% and 19.9% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, compared to the baseline period. Our findings indicate that climate change outweighs the impact of land use/cover (LULC) in the basin, emphasizing the importance of incorporating comprehensive water resources management and adaptation approaches to address the changing hydrological conditions.
Incubation and Larval Development Durations of Sterlet (A. ruthenus LINNAEUS 1758) in River Water Rearing under Near-Natural Conditions
Authors: Eichhorn Heidrun , Friedrich Thomas
Published: May 2024

Day-degrees [°D], also known as accumulated thermal units (ATUs), have been used to predict the duration of early development for fish. For most sturgeon species, the available data in the literature were determined under temperature-constant conditions. However, there is a lack of information on ATUs of early development for sturgeons under natural or near-natural temperature conditions. The aim of this study was to observe the duration of incubation and the duration from hatch until feeding of sterlets (Acipenser ruthenus) under near-natural conditions. This study was embedded within the LIFE-Sterlet and LIFE-Boat 4 Sturgeon project and data were gathered from the year 2017 to 2023. The rearing of fish larvae took place in the project hatchery container with Danube water without biological, chemical, or thermal water treatment to simulate natural conditions. Temperatures [°C] were monitored on a daily basis and day-degrees were calculated by summing the temperature over time. Results indicated slower larval development than described in the literature. Hatching started earliest after 7 days at 106 °D and latest after 151 °D. Feeding started between 155 and 271 °D. The findings of this study provide valuable insights into the temperature-dependent development of sterlet larvae under near-natural conditions and can assist in the design of optimal rearing of sturgeons for conservation efforts.
Exploring the Macroeconomic Effects of Renewable Energy in Tajikistan: An Empirical Analysis
Authors: Sultonov Mirzosaid , Hasanov Behruz , Valizoda Pirumsho , Inagaki Fumiaki
Published: May 2024

Tajikistan holds the eighth position globally in terms of hydropower potential, estimated at 527 terawatt-hours (TWh), with a technically exploitable capacity of 317 TWh. Only 4–6 percent of this immense potential is currently utilized. In this paper, employing a combination of the Johansen cointegration test, vector autoregression, and the Granger-causality test on annual data from 1993 to 2021, we examine the causality relationship between electricity production and key macroeconomic variables, including gross domestic product (GDP), GDP per capita, exports, imports, final consumption, capital investment, and employment, in Tajikistan. The empirical findings reveal a positive unidirectional causality from electricity production to exports and imports. A positive bidirectional or feedback causality is found between electricity production and variables such as GDP, GDP per capita, final consumption, and employment. No causality relationship between electricity production and variables such as trade openness and capital investment is observed. The exploration of complex causal relationships between electricity production and key macroeconomic variables in Tajikistan, as revealed in this study, offers a modest yet meaningful addition to academic discourse. It presents insights that may inform policymakers and stakeholders, albeit with a recognition of the limitations inherent in the findings. These insights could potentially guide the formulation of sustainable development strategies and shed light on the underutilized potential of the country’s hydropower resources.
The “Better Book” Approach to Addressing Equity in Statistics: Centering the Motivational Experiences of Students from Racially Marginalized Backgrounds for Widespread Benefit
Authors: Sutter Claudia C. , Jackson Matthew C. , Givvin Karen B. , Stigler James W. , Son Ji Y.
Published: May 2024

Although improving racial equity in critical college courses such as introductory statistics is a laudable goal, making research-based progress toward that aim in a scalable manner remains a challenge. To translate psychological insights to benefit racially marginalized students, we implemented the “Better Book” approach, where instructors, researchers, and developers work together to improve an online textbook used in introductory statistics. The “Better Book” approach to equity assumes that racially marginalized students are a “canary in the coal mine”, alerting us to systemic issues that can affect a broader array of students. We started by finding places in the textbook where racially marginalized students reported higher perceptions of costs (the effort and time required to learn the content) than non-marginalized students. Then we drew upon suggestions from users to redesign the textbook where gaps in cost perceptions peaked. We then analyzed data from both the original and redesigned versions of the textbook to evaluate the impact on students who were subsequently enrolled in the course. Results showed that perceptions of cost were dramatically reduced in the experience of racially marginalized students but also the redesign resulted in an improved experience for all students.
CamGNN: Cascade Graph Neural Network for Camera Re-Localization
Authors: Wang Li , Jia Jiale , Dai Hualin , Li Guoyan
Published: May 2024

In response to the inaccurate positioning of traditional camera relocation methods in scenes with large-scale or severe viewpoint changes, this study proposes a camera relocation method based on a cascaded graph neural network to achieve accurate scene relocation. Firstly, the NetVLAD retrieval method, which has advantages in image feature representation and similarity calculation, is used to retrieve the most similar images to a given query image. Then, the feature pyramid is employed to extract features at different scales of these images, and the features at the same scale are treated as nodes of the graph neural network to construct a single-layer graph neural network structure. Secondly, a top–down connection is used to cascade the single-layer graph structures, where the information of nodes in the previous graph is fused into a message node to improve the accuracy of camera pose estimation. To better capture the topological relationships and spatial geometric constraints between images, an attention mechanism is introduced in the single-layer graph structure, which helps to effectively propagate information to the next graph during the cascading process, thereby enhancing the robustness of camera relocation. Experimental results on the public dataset 7-Scenes demonstrate that the proposed method can effectively improve the accuracy of camera absolute pose localization, with average translation and rotation errors of 0.19 m and 6.9°, respectively. Compared to other deep learning-based methods, the proposed method achieves more than 10% improvement in both average translation and rotation accuracy, demonstrating highly competitive localization precision.
Resilient Integrated Control for AIOT Systems under DoS Attacks and Packet Loss
Authors: Cao Xiaoya , Wang Wenting , Chen Zhenya , Wang Xin , Yang Ming
Published: May 2024

This paper addresses bandwidth limitations resulting from Denial-of-Service (DoS) attacks on Artificial Intelligence of Things (AIOT) systems, with a specific focus on adverse network conditions. First, to mitigate the impact of DoS attacks on system bandwidth, a novel model predictive control combined with a dynamic time-varying quantization interval adjustment technique is designed for the encoder–decoder architecture of AIOT systems. Second, the network state is modeled to represent a Markov chain under suboptimal network conditions. Furthermore, to guarantee the stability of AIOT systems under random packet loss, a Kalman filter algorithm is applied to precisely estimate the system state. By leveraging the Lyapunov stability theory, the maximum tolerable probability of random packet loss is determined, thereby enhancing the system’s resilient operation. Simulation results validate the effectiveness of the proposed method in dealing with DoS attacks and adverse network conditions.
A Section Location Method of Single-Phase Short-Circuit Faults for Distribution Networks Containing Distributed Generators Based on Fusion Fault Confidence of Short-Circuit Current Vectors
Authors: Xu Shoudong , Ouyang Jinxin , Chen Jiyu , Xiong Xiaofu
Published: May 2024

To ensure safe and stable operation, accurate fault localization within active distribution networks is required, and this has attracted much attention. Influenced by many factors such as the control strategy, control performance, initial state of the distributed generators, and distribution network topology, it is still difficult to reliably locate complex and variable single-phase short-circuit faults relying only on a single feature quantity, while localization methods incorporating intelligent algorithms are affected by the choice of a priori samples and the fact that the solution process is a black-box model. To address this challenge, in this work, an expression for the single-phase short-circuit current vector of a distribution network containing distributed generators is derived, and the differences in magnitude and phase angle of the short-circuit current vectors upstream and downstream of the fault point are analyzed. Based on measurement theory, a fault confidence distribution function that reacts to the relative size of the current magnitude difference and phase angle difference is established, and the fusion fault confidence of the short-circuit current vector is constructed with the help of evidence theory. Finally, a method of locating single-phase short-circuit faults in distribution networks that contain distributed generators is proposed. The simulation results show that the ratio of the fusion fault confidence of the short-circuit current vector between faulted and non-faulted sections under the influence of different distributed generator capacities, fault locations, and transition resistances differ significantly. The proposed single-phase short-circuit fault localization method is both adaptive and physically interpretable and has clear boundaries, sound sensitivity, and engineering practicability.
Research on Frequency Discrimination Method Using Multiplicative-Integral and Linear Transformation Network
Authors: Wang Pengcheng , Yan Sen , Li Xiuhua
Published: May 2024

In this paper, a frequency discrimination method using a multiplicative-integral and linear transformation network is proposed. In this method, two preset differential frequency signals and frequency modulation signals are transformed by multiplication and integration, and then the instantaneous frequency parameters of the frequency modulation signal are accurately analyzed by the linear transformation network to restore the original modulation signal. Compared with the phase discriminator, the simulation results show that this method has a higher frequency discrimination bandwidth. In addition, this method has better anti-noise performance, and the frequency discrimination distortion caused by noise with a different Signal-to-Noise Ratio is reduced by 33.80% on average compared with the phase discriminator. What is more, the carrier center frequency error has little influence on the frequency discrimination quality of this method, which solves the problem that most common frequency discriminators are seriously affected by the carrier center frequency error. This method requires a low accuracy of carrier center frequency, which makes it extremely suitable for digital frequency discrimination technology and can meet the needs of various frequency discrimination occasions.
SpikeExplorer: Hardware-Oriented Design Space Exploration for Spiking Neural Networks on FPGA
Authors: Padovano Dario , Carpegna Alessio , Savino Alessandro , Di Carlo Stefano
Published: May 2024

One of today’s main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs.