bluuebunny/crossref_metadata_2025 huggingface dataset:
Spoken Expressive Vocabulary in 2-Year-Old Children with Hearing Loss: A Community Study
Authors: Carew Peter , Shepherd Daisy A. , Smith Libby , Howell Tegan , Lin Michelle , Bavin Edith L. , Reilly Sheena , Wake Melissa , Sung Valerie
Published: July 2023

Through a cross-sectional community study of 2044 children aged 2 years, we (1) examine the impact of hearing loss on early spoken expressive vocabulary outcomes and (2) investigate how early intervention-related factors impact expressive vocabulary outcomes in children with hearing loss predominantly identified through universal newborn hearing screening. We used validated parent/caregiver-reported checklists from two longitudinal cohorts (302 children with unilateral or bilateral hearing loss, 1742 children without hearing loss) representing the same population in Victoria, Australia. The impact of hearing loss and amplification-related factors on vocabulary was estimated using g-computation and multivariable linear regression. Children with versus without hearing loss had poorer expressive vocabulary scores, with mean scores for bilateral loss 0.5 (mild loss) to 0.9 (profound loss) standard deviations lower and for unilateral loss marginally (0.1 to 0.3 standard deviations) lower. For children with hearing loss, early intervention and amplification by 3 months, rather than by 6 months or older, resulted in higher expressive vocabulary scores. Children with hearing loss demonstrated delayed spoken expressive vocabulary despite whole-state systems of early detection and intervention. Our findings align with calls to achieve a 1-2-3 month timeline for early hearing detection and intervention benchmarks for screening, identification, and intervention.
The Effect of Incubator Cover on Newborn Vital Signs: The Design of Repeated Measurements in Two Separate Groups with No Control Group
Authors: Çetin Kenan , Ekici Behice
Published: July 2023

(1) Background: During their stays in neonatal intensive care units (NICU), newborns are exposed to many stimuli that disrupt their physiological indicators. The aim of this study was to investigate the impact of the light–dark cycle created with and without an incubator cover on the vital signs of term and preterm newborns. (2) Methods: A repeated measures design was used in the study utilizing two separate groups, without a control group. The study included 91 neonates hospitalized in a NICU (44 term and 47 preterm). With and without an incubator cover, the newborns’ vital signs (heart rate (HR), respiratory rate (RR), oxygen saturation (SpO2), and body temperature (BT)) were measured. Three separate measurements were taken. (3) Results: The mean age of the newborns was 37.0 weeks. There was no significant difference between the HR and RR medians of the term and preterms in the incubator undraped and clad measurements (p > 0.05). At the first measurement, the SpO2 medians of the incubator-covered term and preterms were significantly higher than those of the incubator-covered term and preterms (p = 0.001). (4) Conclusions: The vital signs of the neonates demonstrated variable responses in the measurements when their incubators were covered vs. when they were not covered. However, more research on the effect of the light-dark cycle on their vital signs is required.
Comparative Life Cycle Assessment of Different Portland Cement Types in South Africa
Authors: Ige Oluwafemi E. , Olanrewaju Oludolapo A.
Published: July 2023

Cement has long been recognized as an energy- and emission-intensive construction material. Cement production has recently experienced significant growth despite its high energy consumption, resource usage, and carbon emissions. This study aims to assess and compare the life cycle assessment (LCA) of traditional Portland cement (CEM I) to those of three blended cement types (CEM II/B-L, CEM II/B-V, and CEM III/A), which assume mature technologies for reducing carbon emissions in South Africa, using LCA in compliance with ISO/TS 14071 and 14072. As its scope, the study employs the “cradle to gate” method, which considers the raw materials, fuel usage, electricity, transportation, and clinkering stages, using 1 kg of cement as the functional unit. The LCA analyses were performed using SimaPro 9.1.1.1 software developed by PRé Consultants, Amersfoort, Netherlands and impact assessments were conducted using the ReCiPe 2016 v1.04 midpoint method in order to compare all 18 impact categories of 1 kg of cement for each cement type. The assessment results show reductions in all impact categories, ranging from 7% in ozone depletion and ionizing radiation (CEM II/B-L) to a 41% reduction in mineral resource scarcity (CEM III/A). The impacts of global warming were reduced by 14% in the case of CEM II/B-L, 29% in the case of CEM II/B-V and 35% in the case of CEM III/A. The clinkering process was identified as the primary cause of atmospheric impacts, while resource depletion impacts were attributed to raw materials, fuels, and electricity processes, and toxicity impacts were primarily caused by raw materials. Alternative materials, like fly ash and ground granulated blast furnace slag (GGBFS), can significantly help to reduce environmental impacts and resource consumption in the cement industry.
The Drought Regime in Southern Africa: A Systematic Review
Authors: Chivangulula Fernando Maliti , Amraoui Malik , Pereira Mário Gonzalez
Published: July 2023

Drought is one natural disaster with the greatest impact worldwide. Southern Africa (SA) is susceptible and vulnerable to drought due to its type of climate. In the last four decades, droughts have occurred more frequently, with increasing intensity and impacts on ecosystems, agriculture, and health. The work consists of a systematic literature review on the drought regime’s characteristics in the SA under current and future climatic conditions, conducted on the Web of Science and Scopus platforms, using the PRISMA2020 methodology, with usual and appropriate inclusion and exclusion criteria to minimize/eliminate the risk of bias, which lead to 53 documents published after the year 1987. The number of publications on the drought regime in SA is still very small. The country with the most drought situations studied is South Africa, and the countries with fewer studies are Angola and Namibia. The analysis revealed that the main driver of drought in SA is the ocean–atmosphere interactions, including the El Niño Southern Oscillation. The documents used drought indices, evaluating drought descriptors for some regions, but it was not possible to identify one publication that reports the complete study of the drought regime, including the spatial and temporal distribution of all drought descriptors in SA.
Emission and Reduction of Air Pollutants from Charcoal-Making Process in the Vietnamese Mekong Delta
Authors: Toan Pham Van , Kim Lavane , Thanh Nguyen Truong , Toan Huynh Long , Tuan Le Anh , Minh Huynh Vuong Thu , Kumar Pankaj
Published: July 2023

Charcoal is a fuelwood commonly used for domestic purposes on the household scale in Africa and Southeast Asia. Earnings from charcoal production contribute to the income of local inhabitants in rural areas. Unfortunately, airborne emissions from the traditional charcoal-making process affect both human health and the ambient environment. A series of studies were performed at a charcoal-making village in the Vietnamese Mekong Delta (VMD) to assess: (i) air pollutant emissions from the traditional charcoal-making process; (ii) the impacts on human well-being and the environment of traditional charcoal production; (iii) the loading of carbon dioxide from a charcoal-making kiln; and (iv) the efficiency in reducing contaminants of an air pollution-controlling method developed at a charcoal-making kiln. Study results revealed that the traditional charcoal-making method causes a substantial loss of carbon from fuelwood materials and emits the products of incomplete combustion. These contaminants negatively impact human well-being and the environment. Carbon dioxide and incomplete combustion substances emitted from the charcoal-making kiln are potential causes of the global warming phenomenon. The installation of an air pollution-controlling system at the charcoal-making kiln is recommended as an urgent solution before alternatives would be found to control the impacts of charcoal production.
Sequestration of Drugs from Biomolecular and Biomimicking Environments: Spectroscopic and Calorimetric Studies
Authors: Yadav Rahul , Paul Bijan Kumar , Mukherjee Saptarshi
Published: July 2023

The binding of drugs to nucleic acids, proteins, lipids, amino acids, and other biological receptors is necessary for the transportation of drugs. However, various side effects may also originate if the bound drug molecules are not dissociated from the carrier, especially with the aid of non-toxic agents. The sequestration of small drug molecules bound to biomolecules is thus central to counter issues related to drug overdose and drug detoxification. In this article, we aim to present several methods used for the dissociation of small drug molecules bound to different biological and biomimicking assemblies under in vitro experimental conditions. To this effect, the application of various molecular assemblies, like micelles, mixed micelles, molecular containers, like β-cyclodextrin, cucurbit[7]uril hydrate, etc., has been discussed. Herein, we also try to shed light on the driving forces underlying such sequestration processes through spectroscopic and calorimetric techniques.
Incorporating Time-Series Forecasting Techniques to Predict Logistics Companies’ Staffing Needs and Order Volume
Authors: Alqatawna Ahmad , Abu-Salih Bilal , Obeid Nadim , Almiani Muder
Published: July 2023

Time-series analysis is a widely used method for studying past data to make future predictions. This paper focuses on utilizing time-series analysis techniques to forecast the resource needs of logistics delivery companies, enabling them to meet their objectives and ensure sustained growth. The study aims to build a model that optimizes the prediction of order volume during specific time periods and determines the staffing requirements for the company. The prediction of order volume in logistics companies involves analyzing trend and seasonality components in the data. Autoregressive (AR), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) are well-established and effective in capturing these patterns, providing interpretable results. Deep-learning algorithms require more data for training, which may be limited in certain logistics scenarios. In such cases, traditional models like SARIMAX, ARIMA, and AR can still deliver reliable predictions with fewer data points. Deep-learning models like LSTM can capture complex patterns but lack interpretability, which is crucial in the logistics industry. Balancing performance and practicality, our study combined SARIMAX, ARIMA, AR, and Long Short-Term Memory (LSTM) models to provide a comprehensive analysis and insights into predicting order volume in logistics companies. A real dataset from an international shipping company, consisting of the number of orders during specific time periods, was used to generate a comprehensive time-series dataset. Additionally, new features such as holidays, off days, and sales seasons were incorporated into the dataset to assess their impact on order forecasting and workforce demands. The paper compares the performance of the four different time-series analysis methods in predicting order trends for three countries: United Arab Emirates (UAE), Kingdom of Saudi Arabia (KSA), and Kuwait (KWT), as well as across all countries. By analyzing the data and applying the SARIMAX, ARIMA, LSTM, and AR models to predict future order volume and trends, it was found that the SARIMAX model outperformed the other methods. The SARIMAX model demonstrated superior accuracy in predicting order volumes and trends in the UAE (MAPE: 0.097, RMSE: 0.134), KSA (MAPE: 0.158, RMSE: 0.199), and KWT (MAPE: 0.137, RMSE: 0.215).
Influence of the γ′ Volume Fraction on the High-Temperature Strength of Single Crystalline Co–Al–W–Ta Superalloys
Authors: Xue Fei , Bezold Andreas , Volz Nicklas , Kirchmayer Andreas , Zenk Christopher H. , Neumeier Steffen , Göken Mathias
Published: July 2023

Understanding the influence of γ′ and secondary-phase fractions on the mechanical properties of superalloys is very important to optimize these high-strength materials. So far, this has not been systematically investigated for the novel class of Co-based superalloys. In this study, a Co–Al–W–Ta model alloy series was designed with compositions of γ/γ′ on the tie-line and an increasing γ′ volume fraction of up to 70% after heat treatment at 900 °C, while a few alloys are unexpectedly out of γ/γ′ two-phase region with an additional secondary phase fraction of up to 15%. The high-temperature strength and creep properties were evaluated by compression tests up to 1050 °C and compressive creep experiments at 950 °C, respectively. At temperatures of up to 1050 °C, an increasing γ′ volume fraction consistently increased the yield strength, which was not dramatically changed by the presence of secondary phases. Significant work hardening was found in alloys with γ′ volume fractions of 65–70% during compression testing, but not in alloys with either a lower γ′ volume fraction (<50%) or a high fraction of secondary phases (~15%). Similar to the yield strength, the creep strength also increased continuously with the γ′ volume fraction, but was greatly reduced with an increasing fraction of secondary phases. The best creep performance at 950 °C and 200 MPa was found in the alloy with the highest γ′ volume fraction and no secondary phases. At higher creep stresses, rafting contributed significantly to the hardening and, again, the alloy with a high γ′ volume fraction and a small amount of secondary phases exhibited the highest strength.
Completion of Crystallographic Data for the Series of 4-Halogenated-1H-pyrazoles: Crystal Structure Determination of 4-Iodo-1H-pyrazole and Spectroscopic Comparison
Authors: Rue Kelly L. , Herrera Susana , Chakraborty Indranil , Mebel Alexander M. , Raptis Raphael G.
Published: July 2023

Prior to 2021, 4-bromo-1H-pyrazole (published in 1999) was the only structurally characterized 4-halogenated-1H-pyrazole in the Cambridge Crystallographic Data Center (CCDC). The structures of 4-chloro-1H-pyrazole and 4-fluoro-1H-pyrazole were published in 2021 and 2023, respectively. Herein, we report the crystal structure for 4-iodo-1H-pyrazole, completing the crystallographic data for the series of 4-halogenated-1H-pyrazoles. The bromo and chloro analogs are isostructural, forming trimeric H-bonding motifs, whereas the fluoro and iodo analogs form non-isostructural catemers. We also compare the experimental and theoretical (by DFT calculations) IR and 1H NMR spectroscopic data of the four halogenated 4-X-pzH compounds and unsubstituted pyrazole (pzH). An explanation is offered for some counterintuitive structural, infrared, and 1H-NMR spectroscopic data.
Imaging Spectrum of the Developing Glioblastoma: A Cross-Sectional Observation Study
Authors: Currie Stuart , Fatania Kavi , Frood Russell , Whitehead Ruth , Start Joanna , Lee Ming-Te , McDonald Benjamin , Rankeillor Kate , Roberts Paul , Chakrabarty Aruna , Mathew Ryan K. , Murray Louise , Short Susan , Scarsbrook Andrew
Published: July 2023

Glioblastoma (GBM) has the typical radiological appearance (TRA) of a centrally necrotic, peripherally enhancing tumor with surrounding edema. The objective of this study was to determine whether the developing GBM displays a spectrum of imaging changes detectable on routine clinical imaging prior to TRA GBM. Patients with pre-operative imaging diagnosed with GBM (1 January 2014–31 March 2022) were identified from a neuroscience center. The imaging was reviewed by an experienced neuroradiologist. Imaging patterns preceding TRA GBM were analyzed. A total of 76 out of 555 (14%) patients had imaging preceding TRA GBM, 57 had solitary lesions, and 19 had multiple lesions (total = 84 lesions). Here, 83% of the lesions had cortical or cortical/subcortical locations. The earliest imaging features for 84 lesions were T2 hyperintensity/CT low density (n = 18), CT hyperdensity (n = 51), and T2 iso-intensity (n = 15). Lesions initially showing T2 hyperintensity/CT low density later showed T2 iso-intensity. When CT and MRI were available, all CT hyperdense lesions showed T2 iso-intensity, reduced diffusivity, and the following enhancement patterns: nodular 35%, solid 29%, none 26%, and patchy peripheral 10%. The mean time to develop TRA GBM from T2 hyperintensity was 140 days and from CT hyperdensity was 69 days. This research suggests that the developing GBM shows a spectrum of imaging features, progressing through T2 hyperintensity to CT hyperdensity, T2 iso-intensity, reduced diffusivity, and variable enhancement to TRA GBM. Red flags for non-TRA GBM lesions are cortical/subcortical CT hyperdense/T2 iso-intense/low ADC. Future research correlating this imaging spectrum with pathophysiology may provide insight into GBM growth patterns.
Desensitization Protocol for Cemiplimab-Related Infusion Reaction in Cutaneous Squamous Cell Carcinoma: A Case Report and Literature Review
Authors: Banini Marco , Salvestrini Viola , Vultaggio Alessandra , Perlato Margherita , Mecheri Valentina , Cerbai Cecilia , Scotti Vieri , Matucci Andrea , Mangoni Monica , Livi Lorenzo , Bonomo Pierluigi
Published: July 2023

Background: The landscape of systemic therapies for advanced non-melanoma skin cancers has been revolutionized by the advent of immunotherapy. Cemiplimab is the only immune checkpoint inhibitor (ICI) approved by the European Medicine Agency for recurrent/metastatic cutaneous squamous cell carcinoma (cSCC). Its excellent efficacy outcomes are achieved due to its good tolerability profile. The drug-related hypersensitivity reaction (HSR) is a well-known issue in oncology, but it is rarely reported in respect to immune checkpoint inhibitors. Cemiplimab is among the agents with the best infusion tolerability profiles. Clinical practice guidelines in this field are lacking. Results: We report on the successful management of a severe infusion reaction induced by Cemiplimab in a patient with cSCC based on a desensitization protocol, which led to adequate treatment delivery and prolonged clinical benefit. A review of the available literature on HSR rates and its management with ICIs, and on drug desensitization (DD) protocols and their efficacy, was conducted to highlight the limited knowledge on this topic and its importance. Conclusion: Our experience highlights the need for a DD protocol in order to improve the treatment of HSRs, particularly when elicited by an immunotherapy agent, preventing treatment discontinuation and preserving its efficacy.
Is Ficopomatus enigmaticus (Annelida: Serpulidae) Becoming a Nuisance in Galveston Bay, Texas?
Authors: Fernández-Rodríguez Vanessa , Jurgens Laura , Schulze Anja
Published: July 2023

Herein, we report the recent proliferation of the non-native serpulid worm Ficopomatus enigmaticus in parts of Galveston Bay, Texas. Reef-like aggregations were first recovered from a settlement plate and a submerged brick at the Kemah Boardwalk Marina on 4 May 2023. By 20 May, similar growths were reported to us by the Kemah Boardwalk Marina on floating docks, boat hulls, propellers, and rudders. On June 8, nearby marinas remained unaffected. We confirmed the worms’ species identities by noting their diagnostic characteristics.
Cutting Edge Developments in Diabetes
Authors: Clifton Peter M.
Published: July 2023

Since 2005, glucagon-like peptide 1 (GLP-1) receptor agonists have been clinically available, which has resulted in a significant transformation in diabetes care, both in terms of weight management and glucose control [...]
Focus on the Most Common Paucisymptomatic Vasculopathic Population, from Diagnosis to Secondary Prevention of Complications
Authors: Martelli Eugenio , Enea Iolanda , Zamboni Matilde , Federici Massimo , Bracale Umberto M. , Sangiorgi Giuseppe , Martelli Allegra R. , Messina Teresa , Settembrini Alberto M.
Published: July 2023

Middle-aged adults can start to be affected by some arterial diseases (ADs), such as abdominal aortic or popliteal artery aneurysms, lower extremity arterial disease, internal carotid, or renal artery or subclavian artery stenosis. These vasculopathies are often asymptomatic or paucisymptomatic before manifesting themselves with dramatic complications. Therefore, early detection of ADs is fundamental to reduce the risk of major adverse cardiovascular and limb events. Furthermore, ADs carry a high correlation with silent coronary artery disease (CAD). This study focuses on the most common ADs, in the attempt to summarize some key points which should selectively drive screening. Since the human and economic possibilities to instrumentally screen wide populations is not evident, deep knowledge of semeiotics and careful anamnesis must play a central role in our daily activity as physicians. The presence of some risk factors for atherosclerosis, or an already known history of CAD, can raise the clinical suspicion of ADs after a careful clinical history and a deep physical examination. The clinical suspicion must then be confirmed by a first-level ultrasound investigation and, if so, adequate treatments can be adopted to prevent dreadful complications.
Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals
Authors: Hasan Md. Nazmul , Koo Insoo
Published: July 2023

Sleep stage classification plays a pivotal role in predicting and diagnosing numerous health issues from human sleep data. Manual sleep staging requires human expertise, which is occasionally prone to error and variation. In recent times, availability of polysomnography data has aided progress in automatic sleep-stage classification. In this paper, a hybrid deep learning model is proposed for classifying sleep and wake states based on a single-channel electroencephalogram (EEG) signal. The model combines an artificial neural network (ANN) and a convolutional neural network (CNN) trained using mixed-input features. The ANN makes use of statistical features calculated from EEG epochs, and the CNN operates on Hilbert spectrum images generated during each epoch. The proposed method is assessed using single-channel Pz-Oz EEG signals from the Sleep-EDF database Expanded. The classification performance on four randomly selected individuals shows that the proposed model can achieve accuracy of around 96% in classifying between sleep and wake states from EEG recordings.
The Reliability of Three-Dimensional Landmark-Based Craniomaxillofacial and Airway Cephalometric Analysis
Authors: Yao Kan , Xie Yilun , Xia Liang , Wei Silong , Yu Wenwen , Shen Guofang
Published: July 2023

Cephalometric analysis is a standard diagnostic tool in orthodontics and craniofacial surgery. Today, as conventional 2D cephalometry is limited and susceptible to analysis bias, a more reliable and user-friendly three-dimensional system that includes hard tissue, soft tissue, and airways is demanded in clinical practice. We launched our study to develop such a system based on CT data and landmarks. This study aims to determine whether the data labeled through our process is highly qualified and whether the soft tissue and airway data derived from CT scans are reliable. We enrolled 15 patients (seven males, eight females, 26.47 ± 3.44 years old) diagnosed with either non-syndromic dento–maxillofacial deformities or OSDB in this study to evaluate the intra- and inter-examiner reliability of our system. A total of 126 landmarks were adopted and divided into five sets by region: 28 cranial points, 25 mandibular points, 20 teeth points, 48 soft tissue points, and 6 airway points. All the landmarks were labeled by two experienced clinical practitioners, either of whom had labeled all the data twice at least one month apart. Furthermore, 78 parameters of three sets were calculated in this study: 42 skeletal parameters (23 angular and 19 linear), 27 soft tissue parameters (9 angular and 18 linear), and 9 upper airway parameters (2 linear, 4 areal, and 3 voluminal). Intraclass correlation coefficient (ICC) was used to evaluate the inter-examiner and intra-examiner reliability of landmark coordinate values and measurement parameters. The overwhelming majority of the landmarks showed excellent intra- and inter-examiner reliability. For skeletal parameters, angular parameters indicated better reliability, while linear parameters performed better for soft tissue parameters. The intra- and inter-examiner ICCs of airway parameters referred to excellent reliability. In summary, the data labeled through our process are qualified, and the soft tissue and airway data derived from CT scans are reliable. Landmarks that are not commonly used in clinical practice may require additional attention while labeling as they are prone to poor reliability. Measurement parameters with values close to 0 tend to have low reliability. We believe this three-dimensional cephalometric system would reach clinical application.
Acute Kidney Injury in Liver Cirrhosis
Authors: Attieh Rose Mary , Wadei Hani M.
Published: July 2023

Acute kidney injury (AKI) is common in cirrhotic patients affecting almost 20% of these patients. While multiple etiologies can lead to AKI, pre-renal azotemia seems to be the most common cause of AKI. Irrespective of the cause, AKI is associated with worse survival with the poorest outcomes observed in those with hepatorenal syndrome (HRS) and acute tubular necrosis (ATN). In recent years, new definitions, and classifications of AKI in cirrhosis have emerged. More knowledge has also become available regarding the benefits and drawbacks of albumin and terlipressin use in these patients. Diagnostic tools such as urinary biomarkers and point-of-care ultrasound (POCUS) became available and they will be used in the near future to differentiate between different causes of AKI and direct management of AKI in these patients. In this update, we will review these new classifications, treatment recommendations, and diagnostic tools for AKI in cirrhotic patients.
Left Ventricle Segmentation in Echocardiography with Transformer
Authors: Liao Minqi , Lian Yifan , Yao Yongzhao , Chen Lihua , Gao Fei , Xu Long , Huang Xin , Feng Xinxing , Guo Suxia
Published: July 2023

Left ventricular ejection fraction (LVEF) plays as an essential role in the assessment of cardiac function, providing quantitative data support for the medical diagnosis of heart disease. Robust evaluation of the ejection fraction relies on accurate left ventricular (LV) segmentation of echocardiograms. Because human bias and expensive labor cost exist in manual echocardiographic analysis, computer algorithms of deep-learning have been developed to help human experts in segmentation tasks. Most of the previous work is based on the convolutional neural networks (CNN) structure and has achieved good results. However, the region occupied by the left ventricle is large for echocardiography. Therefore, the limited receptive field of CNN leaves much room for improvement in the effectiveness of LV segmentation. In recent years, Vision Transformer models have demonstrated their effectiveness and universality in traditional semantic segmentation tasks. Inspired by this, we propose two models that use two different pure Transformers as the basic framework for LV segmentation in echocardiography: one combines Swin Transformer and K-Net, and the other uses Segformer. We evaluate these two models on the EchoNet-Dynamic dataset of LV segmentation and compare the quantitative metrics with other models for LV segmentation. The experimental results show that the mean Dice similarity of the two models scores are 92.92% and 92.79%, respectively, which outperform most of the previous mainstream CNN models. In addition, we found that for some samples that were not easily segmented, whereas both our models successfully recognized the valve region and separated left ventricle and left atrium, the CNN model segmented them together as a single part. Therefore, it becomes possible for us to obtain accurate segmentation results through simple post-processing, by filtering out the parts with the largest circumference or pixel square. These promising results prove the effectiveness of the two models and reveal the potential of Transformer structure in echocardiographic segmentation.
A Cross-Sectional Study of the Dermatological Manifestations of Patients with Fabry Disease and the Assessment of Angiokeratomas with Multimodal Imaging
Authors: Anker Pálma , Fésűs Luca , Kiss Norbert , Lengyel Anna , Pinti Éva , Lihacova Ilze , Lihachev Alexey , Plorina Emilija Vija , Fekete György , Medvecz Márta
Published: July 2023

Fabry disease (FD) is a multisystemic X-linked lysosomal storage disease that presents with angiokeratomas (AKs). Our objective was to investigate the clinical and morphologic features of AKs and to present two experimental techniques, multispectral imaging (MSI) and non-linear microscopy (NLM). A thorough dermatological examination was carried out in our 26 FD patients and dermoscopic images (n = 136) were evaluated for specific structures. MSI was used for the evaluation of AKs in seven patients. NLM was carried out to obtain histology samples of two AKs and two hemangiomas. Although AKs were the most common manifestation, the majority of patients presented an atypical distribution and appearance, which could cause a diagnostic challenge. Dermoscopy revealed lacunae (65%) and dotted vessels (56%) as the most common structures, with a whitish veil present in only 25%. Autofluorescence (405 nm) and diffuse reflectance (526 nm) images showed the underlying vasculature more prominently compared to dermoscopy. Using NLM, AKs and hemangiomas could be distinguished based on morphologic features. The clinical heterogeneity of FD can result in a diagnostic delay. Although AKs are often the first sign of FD, their presentation is diverse. A thorough dermatological examination and the evaluation of other cutaneous signs are essential for the early diagnosis of FD.
Diagnosis of Latent Tuberculosis Infection in Hemodialysis Patients: TST versus T-SPOT.TB
Authors: Binay Umut Devrim , Kara Ali Veysel , Karakeçili Faruk , Barkay Orçun
Published: July 2023

Hemodialysis (HD) patients should be screened for latent tuberculosis (TB) infection. We aimed to determine the frequency of latent TB infection in HD patients and to compare the effectiveness of the tests used. The files of 56 HD patients followed between 1 January 2021 and 1 October 2022 were retrospectively analyzed. Demographic data, the presence of the Bacillus Calmette-Guerin (BCG) vaccine, whether or not the patients had previously received treatment for TB before, the status of encountering a patient with active TB of patients over 18 years of age, without active tuberculosis and who had a T-SPOT.TB test or a Tuberculin Skin Test (TST) were obtained from the patient files. The presence of previous TB in a posterior–anterior (PA) chest X-ray was obtained by evaluating PA chest X-rays taken routinely. Of the patients, 60.7% (n = 34) were male and their mean age was 60.18 ± 14.85 years. The mean duration of dialysis was 6.43 ± 6.03 years, and 76.8% (n = 43) had 2 BCG scars. The T-SPOT.TB test was positive in 32.1% (n = 18). Only 20 patients (35.7%) had a TST and all had negative results. While the mean age of those with positive T-SPOT.TB results was higher (p = 0.003), the time taken to enter HD was shorter (p = 0.029). T-SPOT.TB test positivity was higher in the group that had encountered active TB patients (p = 0.033). However, no significant difference was found between T-SPOT.TB results according to BCG vaccine, albumin, urea and lymphocyte levels. Although T-SPOT.TB test positivity was higher in patients with a previous TB finding in a PA chest X-ray, there was no statistically significant difference (p = 0.093). The applicability of the TST in the diagnosis of latent TB infection in HD patients is difficult and it is likely to give false-negative results. The T-SPOT.TB test is not affected by the BCG vaccine and immunosuppression. Therefore, using the T-SPOT.TB test would be a more appropriate and practical approach in the diagnosis of latent TB in HD patients.
Uterine Cervical Angioleiomyoma Mimicking Squamous Cell Carcinoma
Authors: Lee Jiwon , Shin Seoyeon , Kim Jin-Hwi , Lee Su Lim , Chong Yosep , Seo Kyung Jin , Yim Kwangil
Published: July 2023

Angioleiomyoma, a rare variant of leiomyoma, is a benign tumor of mesenchymal origin. Angioleiomyomas of the female urogenital tract are extremely rare, with only six cases of uterine cervical angioleiomyoma previously reported in the literature. In this case study, we report on a 49-year-old female patient who presented with menorrhagia whose initial magnetic resonance imaging (MRI) findings suggested cervical squamous cell carcinoma (SCC). However, following the hysterectomy, histological examination confirmed the lesion to be angioleiomyoma. To the best of our knowledge, there have been no previously reported cases of angioleiomyomas presenting with MRI findings that are suggestive of uterine SCC. Recognizing that angioleiomyomas can mimic uterine malignancies on MRI may prove beneficial for future diagnostic and treatment strategies.
Surgical Anatomy of the Liver—Significance in Ovarian Cancer Surgery
Authors: Kostov Stoyan , Selçuk Ilker , Watrowski Rafał , Dineva Svetla , Kornovski Yavor , Slavchev Stanislav , Ivanova Yonka , Dzhenkov Deyan , Yordanov Angel
Published: July 2023

Introduction: Ovarian cancer is the leading cause of death among all gynecological malignancies. Most patients present with an advanced stage of the disease. The routes of spread in ovarian cancer include peritoneal dissemination, direct invasion, and lymphatic or hematogenous spread, with peritoneal and lymphatic spread being the most common among them. The flow direction of the peritoneal fluid makes the right subphrenic space a target site for peritoneal metastases, and the most frequently affected anatomical area in advanced cases is the right upper quadrant. Complete cytoreduction with no macroscopically visible disease is the most important prognostic factor. Methods: We reviewed published clinical anatomy reports associated with surgery of the liver in cases of advanced ovarian cancer. Results: The disease could disseminate anatomical areas, where complex surgery is required—Morrison’s pouch, the liver surface, or porta hepatis. The aim of the present article is to emphasize and delineate the gross anatomy of the liver and its surgical application for oncogynecologists. Moreover, the association between the gross and microscopic anatomy of the liver is discussed. Additionally, the vascular supply and variations of the liver are clearly described. Conclusions: Oncogynecologists performing liver mobilization, diaphragmatic stripping, and porta hepatis dissection must have a thorough knowledge of liver anatomy, including morphology, variations, functional status, potential diagnostic imaging mistakes, and anatomical limits of dissection.
A Nomogram Based on Consolidation Tumor Ratio Combined with Solid or Micropapillary Patterns for Postoperative Recurrence in Pathological Stage IA Lung Adenocarcinoma
Authors: Zhang Longfu , Liu Jie , Yang Dawei , Ni Zheng , Lu Xinyuan , Liu Yalan , Liu Zilong , Wang Hao , Feng Mingxiang , Zhang Yong
Published: July 2023

Background: Patients with pathological stage IA lung adenocarcinoma (LUAD) are at risk of relapse. The value of the TNM staging system is limited in predicting recurrence. Our study aimed to develop a precise recurrence prediction model for stage IA LUAD. Materials and methods: Patients with pathological stage IA LUAD who received surgical treatment at Zhongshan Hospital Fudan University were retrospectively analyzed. Multivariate Cox proportional hazards regression models were used to create nomograms for recurrence-free survival (RFS). The predictive performance of the model was assessed using calibration plots and the concordance index (C-index). Results: The multivariate Cox regression analysis revealed that CTR (0.75 < CTR ≤ 1; HR = 9.882, 95% CI: 2.036–47.959, p = 0.004) and solid/micropapillary-predominance (SMPP; >5% and the most dominant) (HR = 4.743, 95% CI: 1.506–14.933, p = 0.008) were independent prognostic factors of RFS. These risk factors were used to construct a nomogram to predict postoperative recurrence in these patients. The C-index of the nomogram for predicting RFS was higher than that of the eighth T-stage system (0.873 for the nomogram and 0.643 for the eighth T stage). The nomogram also achieved good predictive performance for RFS with a well-fitted calibration curve. Conclusions: We developed and validated a nomogram based on CTR and SMP patterns for predicting postoperative recurrence in pathological stage IA LUAD. This model is simple to operate and has better predictive performance than the eighth T stage system, making it suitable for selecting further adjuvant treatment and follow-up.
Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture
Authors: Lim Chee Chin , Ling Apple Ho Wei , Chong Yen Fook , Mashor Mohd Yusoff , Alshantti Khalilalrahman , Aziz Mohd Ezane
Published: July 2023

Osteosarcoma is a common type of bone tumor, particularly prevalent in children and adolescents between the ages of 5 and 25 who are experiencing growth spurts during puberty. Manual delineation of tumor regions in MRI images can be laborious and time-consuming, and results may be subjective and difficult to replicate. Therefore, a convolutional neural network (CNN) was developed to automatically segment osteosarcoma cancerous cells in three types of MRI images. The study consisted of five main stages. First, 3692 DICOM format MRI images were acquired from 46 patients, including T1-weighted, T2-weighted, and T1-weighted with injection of Gadolinium (T1W + Gd) images. Contrast stretching and median filter were applied to enhance image intensity and remove noise, and the pre-processed images were reconstructed into NIfTI format files for deep learning. The MRI images were then transformed to fit the CNN’s requirements. A 3D U-Net architecture was proposed with optimized parameters to build an automatic segmentation model capable of segmenting osteosarcoma from the MRI images. The 3D U-Net segmentation model achieved excellent results, with mean dice similarity coefficients (DSC) of 83.75%, 85.45%, and 87.62% for T1W, T2W, and T1W + Gd images, respectively. However, the study found that the proposed method had some limitations, including poorly defined borders, missing lesion portions, and other confounding factors. In summary, an automatic segmentation method based on a CNN has been developed to address the challenge of manually segmenting osteosarcoma cancerous cells in MRI images. While the proposed method showed promise, the study revealed limitations that need to be addressed to improve its efficacy.
Flow Structure around a Multicopter Drone: A Computational Fluid Dynamics Analysis for Sensor Placement Considerations
Authors: Ghirardelli Mauro , Kral Stephan T. , Müller Nicolas Carlo , Hann Richard , Cheynet Etienne , Reuder Joachim
Published: July 2023

This study presents a computational fluid dynamics (CFD) based approach to determine the optimal positioning for an atmospheric turbulence sensor on a rotary-wing uncrewed aerial vehicle (UAV) with X8 configuration. The vertical (zBF) and horizontal (xBF) distances of the sensor to the UAV center to reduce the effect of the propeller-induced flow are investigated by CFD simulations based on the k−ϵ turbulence model and the actuator disc theory. To ensure a realistic geometric design of the simulations, the tilt angles of a test UAV in flight were measured by flying the drone along a fixed pattern at different constant ground speeds. Based on those measurement results, a corresponding geometry domain was generated for the CFD simulations. Specific emphasis was given to the mesh construction followed by a sensitivity study on the mesh resolution to find a compromise between acceptable simulation accuracy and available computational resources. The final CFD simulations (twelve in total) were performed for four inflow conditions (2.5 m s−1, 5 m s−1, 7.5 m s−1 and 10 m s−1) and three payload configurations (15 kg, 20 kg and 25 kg) of the UAV. The results depend on the inflows and show that the most efficient way to reduce the influence of the propeller-induced flow is mounting the sensor upwind, pointing along the incoming flow direction at xBF varying between 0.46 and 1.66 D, and under the mean plane of the rotors at zBF between 0.01 and 0.7 D. Finally, results are then applied to the possible real-case scenario of a Foxtech D130 carrying a CSAT3B ultrasonic anemometer, that aims to sample wind with mean flows higher than 5 m s−1. The authors propose xBF=1.7 m and zBF=20 cm below the mean rotor plane as a feasible compromise between propeller-induced flow reduction and safety. These results will be used to improve the design of a novel drone-based atmospheric turbulence measurement system, which aims to combine accurate wind and turbulence measurements by a research-grade ultrasonic anemometer with the high mobility and flexibility of UAVs as sensor carriers.
Tracking a Maneuvering Object by Indirect Observations with Random Delays
Authors: Bosov Alexey
Published: July 2023

A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the observation environment but also on the state of the object itself. The source of such a model is the observation process, by stationary means, of an autonomous underwater vehicle, in which the time for obtaining up-to-date data depends on the unknown distance between the object and the observer. As part of the study of the problem, the equations of the optimal Bayesian filter are obtained. But this filter is not possible to implement. For practical purposes, it is proposed to use the conditionally minimax nonlinear filter, which has shown promising results in other complex tracking models. The conditions for the filter’s evaluation and its accuracy characteristics are given. A large-scale numerical experiment illustrating the filter’s operation and the observation system’s features with random delays are described.
Practical Entropy Accumulation for Random Number Generators with Image Sensor-Based Quantum Noise Sources
Authors: Choi Youngrak , Yeom Yongjin , Kang Ju-Sung
Published: July 2023

The efficient generation of high-quality random numbers is essential in the operation of cryptographic modules. The quality of a random number generator is evaluated by the min-entropy of its entropy source. The typical method used to achieve high min-entropy of the output sequence is an entropy accumulation based on a hash function. This is grounded in the famous Leftover Hash Lemma, which guarantees a lower bound on the min-entropy of the output sequence. However, the hash function-based entropy accumulation has slow speed in general. For a practical perspective, we need a new efficient entropy accumulation with the theoretical background for the min-entropy of the output sequence. In this work, we obtain the theoretical bound for the min-entropy of the output random sequence through the very efficient entropy accumulation using only bitwise XOR operations, where the input sequences from the entropy source are independent. Moreover, we examine our theoretical results by applying them to the quantum random number generator that uses dark shot noise arising from image sensor pixels as its entropy source.
The Modal Components of Judgements in a Quantum Model of Psychoanalytic Theory
Authors: Battilotti Giulia , Borozan Miloš , Lauro Grotto Rosapia
Published: July 2023

In the present paper, we develop a theory of thinking based on an attempt to formalize the construction of mental representations as described in psychoanalytic theory. In previous work, we described Freud’s and Matte Blanco’s structural Unconscious in a formal model in which the properties of unconscious representations are captured by particular sets-infinite singletons-that can be derived in first-order logic language. Here, we afford the issue of the finitization of unconscious representations by assuming that the mind can form an all-purpose modality, originating from abstraction from infinite singletons; in this way, a symmetric prelogical setting for mental representations is formally created, and this is interpreted in a quantum spin model by a modal (necessity) projector. Then, by introducing time, one can describe the links that mental representations can establish with reality, and hence finitize the representations. The modality is so split into finite components, here termed positive, negative and irreal; the splitting of the modality is traced back to the decomposition of the spin observables by means of the Pauli matrices, which can offer a quantum semantics to the method applied. Here, we suggest that the development of the modal approach and its quantum logic implementation can be considered as a proper formalization of some aspect of the psychoanalytic theory of thinking proposed by Bion; namely, we will show that the process of abstraction leading from raw data to preconceptions, and therefore to the definition of the content-container relationship, is adequately captured by our model, and further correspondences can be detected with Bion’s theory about links and transformations, implying different ways in which the mind can get in touch with both internal and external reality.
Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains
Authors: Fu Moxuan , Zhang Chuan , Hu Chenfei , Wu Tong , Dong Jinyang , Zhu Liehuang
Published: July 2023

Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data. Among all the machine learning models, decision tree models stand out due to their great interpretability and simplicity, and have been implemented in cloud computing services for various purposes. Despite its great success, the integrity issue of online decision tree prediction is a growing concern. The correctness and consistency of decision tree predictions in cloud computing systems need more security guarantees since verifying the correctness of the model prediction remains challenging. Meanwhile, blockchain has a promising prospect in two-party machine learning services as the immutable and traceable characteristics satisfy the verifiable settings in machine learning services. In this paper, we initiate the study of decision tree prediction services on blockchain systems and propose VDT, a Verifiable Decision Tree prediction scheme for decision tree prediction. Specifically, by leveraging the Merkle tree and hash function, the scheme allows the service provider to generate a verification proof to convince the client that the output of the decision tree prediction is correctly computed on a particular data sample. It is further extended to an update method for a verifiable decision tree to modify the decision tree model efficiently. We prove the security of the proposed VDT schemes and evaluate their performance using real datasets. Experimental evaluations show that our scheme requires less than one second to produce verifiable proof.