bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stromal tumor-infiltrating lymphocytes and pathologic response to neoadjuvant chemotherapy with the addition of platinum and pembrolizumab in TNBC: A single-center real-world study
Authors: Bae Soong June , Kim Jee Hung , Kim Min Ji , Kook Yoonwon , Baek Seung Ho , Kim Jung Hyun , Moon Sohyun , Lee Seung Eun , Jeong Joon , Cha Yoon Jin , Ahn Sung Gwe 
Published: May 2024

Abstract

Background
Immunochemotherapy with pembrolizumab has been integrated into clinical practice as part of the standard-of-care for non-metastatic triple-negative breast cancer (TNBC) with high risk. We conducted a real-world study in TNBC patients treated with neoadjuvant chemotherapy to compare pathologic complete response (pCR) rates relative to stromal tumor-infiltrating lymphocytes (sTIL) across different regimens: non-carboplatin, carboplatin-, and pembrolizumab-chemotherapy.
Methods
We analyzed a cohort of 450 patients with TNBC who underwent surgery following neoadjuvant chemotherapy between March 2007 and February 2024. Treatment groups included 247 non-carboplatin, 120 carboplatin, and 83 pembrolizumab-chemotherapy recipients. sTIL was evaluated in biopsied samples. Lymphocyte-predominant breast cancer (LPBC) was defined as tumors with high sTIL (≥ 50%).
Results
The pCR rates were 32% in the non-carboplatin-, 57% in the carboplatin-, and 64% in the pembrolizumab-chemotherapy group. Ninety-two patients (20.4%) had LPBC. In LPBC, the pCR rates did not increase with the addition of carboplatin (50.0% in the non-carboplatin and 41.7% in carboplatin) but reached 83.3% with the addition of pembrolizumab and carboplatin. Among the non-LPBC, the pCR rate increased from 26.7% to 61.1% with the addition of carboplatin, but there was no difference in the pCR rate between the carboplatin and pembrolizumab groups (61.1% and 61.2%, respectively).
Conclusions
In LPBC patients, the addition of carboplatin did not result in an elevated pCR rate; however, the addition of pembrolizumab tended to raise the pCR rate. In non-LPBC, the addition of carboplatin significantly increased the pCR rate, while the addition of pembrolizumab did not have the same effect.
Mathematical studies on generalized Burgers Huxley equation and its singularly perturbed form: Existence of traveling wave solutions .
Authors: Zhang Xiaohan , Tian Yu , Zhang Mingji , Qi Yao 
Published: May 2024

Abstract

We focus on a class of generalized Burgers Huxley equation and its singularly perturbed form, which has significant impacts on practical applications. The existence of different traveling wave solutions is established. Numerical examples are provided to provide more intuitive illustrations of our analytical results. Our analysis is based on the combination of geometric singular perturbation theory,invariant manifold theory and bifurcation theory.
Signature-Based Portfolio Allocation: A Network Approach
Authors: Gregnanin Marco , Zhang Yanyi , De Smedt Johannes , Gnecco Giorgio , Parton Maurizio 
Published: May 2024

Abstract

Portfolio allocation represents a significant challenge within financial markets, traditionally relying on correlation or covariance matrices to delineate relationships among stocks. However, these methodologies assume time stationarity and only capture linear relationships among stocks.
In this study, we propose to substitute the conventional Pearson's correlation or covariance matrix in portfolio optimization with a similarity matrix derived from the signature. The signature, a concept from path theory, provides a unique representation of time series data, encoding their geometric patterns and inherent properties. Furthermore, we undertake a comparative analysis of network structures derived from the correlation matrix versus those obtained from the signature-based similarity matrix.
Through numerical evaluation on the Standard & Poor's 500, we assess that portfolio allocation utilizing the signature-based similarity matrix yielded superior results in terms of cumulative log-returns and Sharpe ratio compared to the baseline network approach based on Pearson's correlation. This assessment was conducted across various portfolio optimization strategies.
This research contributes to portfolio allocation and financial network representation by proposing the use of signature-based similarity matrices over traditional correlation or covariance matrices.
A 5D Digital Circular Workflow: Digital Transformation Towards Matchmaking of Environmentally Sustainable Building Materials through Reuse from Disassembly
Authors: De Wolf Catherine , Byers Brandon S. , Raghu Deepika , Gordon Matthew , Schwarzkopf Vanessa , Triantafyllidis Eleftherios 
Published: May 2024

Abstract

The intersection of digital transformation, circular economy, and innovative construction practices presents a nascent field with significant potential to mitigate environmental impacts through optimised material reuse. This research aims to understand how digital technologies can augment the reuse of construction materials. We evaluated a plethora of digital tools, including digital product passports, artificial intelligence (AI)-assisted material classification, reality capture, computational and generative AI-aided design, digital fabrication techniques, and blockchain technology, for their efficacy in facilitating building material reuse. The practical component of the study involved disassembling buildings and then designing and executing a construction project reusing the salvaged materials. Findings demonstrate that the successful application of circular economy principles is facilitated by digital forms of cataloguing, inventory management, design, and construction. The research proposes a workflow for incorporating digital innovations into circular construction, suggesting a pathway for future implementation and scalability.
Is Obesity Awareness Related to Food Craving and Metabolic Parameters in Adults with Overweight and Obesity?
Authors: Selcuk Kevser Tarı , Atan Ramazan Mert , DAL Nursel , Arslan Sedat 
Published: May 2024

Abstract

Objective: This study was aimed at investigating the relationship between obesity awareness and metabolic parameters in adults with overweight and obesity.
Materials-Methods: This cross-sectional study was conducted with 508 adults with overweight or obesity. Tools used to collect the data were the Descriptive Information Form, Obesity Awareness Scale and Insight Scale, and Food Craving Inventory. In data analysis, descriptive statistics, and simple and multiple linear regression analysis were used. The significance level of statistical tests was accepted as p<0.05.
Results: Percentages of adults with overweight and obesity were 49.1% and 50.9% respectively. According to the linear regression analysis, in Model 1, Model 2, and Model 3, obesity awareness was statistically significantly negatively associated with food craving (β:−0.114, p<0.05), waist circumference (β:−0.266, p<0.05), triglyceride level (β:−0.161 , p<0.05), fasting glucose level (β:−0.099, p<0.05) and diastolic blood pressure level (β:−0.105, p<0.05).
Conclusion: As obesity awareness increases, craving for sweets and fast-food fats decreases, and waist circumference, fasting glucose, triglyceride and diastolic blood pressure levels decrease.
Risk factors for pulmonary complications after colorectal surgery: a Japanese multicenter study
Authors: Tominaga Tetsuro , Nonaka Takashi , Takamura Yuma , Oishi Kaido , Hashimoto Shintaro , Shiraishi Toshio , Noda Keisuke , Ono Rika , Ishii Mitsutoshi , Hisanaga Makoto , Takeshita Hiroaki , Fukuoka Hidetoshi , Oyama Shosaburo , Ishimaru Kazuhide , Kunizaki Masaki , Sawai Terumitsu , Matsumoto Keitaro 
Published: May 2024

Abstract

Purpose: Pulmonary complications (PC) are a serious condition with a 20% mortality rate. However, few reports have examined risk factors for PC after colorectal surgery. This study investigated the frequency, characteristics, and risk factors for PC after colorectal cancer surgery.
Methods: Between January 2016 and December 2022, we retrospectively reviewed 3979 consecutive patients who underwent colorectal surgery in seven participating hospitals. Patients were divided into patients who experienced PC (PC group, n=54) and patients who did not (Non-PC group, n=3925). Clinical and pathological features were compared between groups.
Results: Fifty-four patients (1.5%) developed PC, of whom 2 patients (3.7%) died due to PC. Age was greater (80 years vs 71 years; p<0.001), frequency of chronic obstructive pulmonary distress was greater (9.3% vs 3.2%; p=0.029), performance status was poorer (p<0.001), body mass index was lower (18 kg/m2 vs 22 kg/m2; p<0.001), frequency of open surgery was greater (24.1% vs 9.3%; p<0.001), and blood loss was greater (40 mL vs 22 mL; p<0.001) in the PC group. Multivariate analysis of PC revealed male sex (odds ratio [OR] 1.845, 95% confidence interval [CI] 1.023–3.329; p=0.041), high age (OR 3.434, 95%CI 1.941–6.075; p<0.001) and poor performance status (OR 4.144, 95%CI 2.318–7.408; p<0.001) as independent predictors of PC.
Conclusion: Our study revealed male sex, greater age, and poorer ASA-PS as factors associated with development of PC, and suggested that pre- and postoperative rehabilitation and pneumonia control measures should be implemented for patients at high risk of PC.
A Geometric Algebra-based machine learning method for Typhoon intensity forecasting
Authors: haiyan chen , Huang Yadi , Li Dongshuang , Luo Wen , Yu Zhaoyuan , Yuan Linwang 
Published: May 2024

Abstract

Typhoons, as highly destructive natural disasters, significantly impact society and economy, making accurate prediction of their intensity crucial. Artificial intelligence technologies, such as machine learning, have been extensively applied in the meteorological domain due to their advantages in processing large-scale datasets and learning complex nonlinear relationships. However, existing machine learning models for typhoon intensity prediction face issues like high complexity and parameter count, increasing computational demands and risking overfitting. This study leverages the advantages of geometric algebra for the unified expression and computation of multi-dimensional variables, introduces a geometric algebra-based convolutional neural network with spatial attention to enhance typhoon intensity predictions. This study employed typhoon data from the Northwest Pacific from 2000 to 2018. The results indicate that, compared to the traditional forecasting methods of the China Meteorological Administration (CMA), the proposed geometric algebra neural network model improved prediction accuracy by approximately 9% and reduced the root mean square error by about 5% compared to real-valued neural network models. Most importantly, while maintaining predictive performance, the geometric algebra neural network significantly reduced the number of model parameters, achieving up to a fourfold decrease compared to real-valued neural networks. This study offers new insights and technical advancements for using Geometric Algebra in analyzing to handle long-term, large-scale meteorological data.
Systematic Mapping Study of Tools to Identify Emotions and Personality Traits
Authors: Islam Amanul , Noor Nurul Fazmidar Binti Mod , Rahman Siti Soraya Abdul 
Published: May 2024

Abstract

Emotions and personality traits profoundly influence human behavior and well-being. Recent advancements in computer-based tools utilizing machine learning techniques have opened new avenues for identifying and understanding these psychological aspects in individuals. This systematic mapping study comprehensively reviews research articles from reputable journals, focusing on tools that leverage various data sources, such as text analysis, face recognition, gestures, and heart rate monitoring. The selected papers underwent rigorous analysis, leading to the categorization of identified tools based on their methodologies, objectives, and application domains. Natural language processing techniques were found to excel in capturing emotions from textual data, while deep learning models demonstrated accuracy in face recognition. Machine learning algorithms showed promise in analyzing gestures and heart rate to understand personality traits and emotional responses. However, the study also highlights the importance of validation standardization and large-scale studies across diverse populations to enhance the reliability and effectiveness of these tools.
Pandemic Profiteering at a time of Crisis: Using Python to Detect Fraud in COVID-19 Testing and Treatment Payments
Authors: Amponsah Isaac Asamoah 
Published: May 2024

Abstract

Background
During the pandemic, the Centre for Medicare and Medicaid Services (CMS) introduced waivers and rule flexibilities to address rising COVID-19 cases. This included expanding telehealth services to urban areas, along with various testing options like surveillance testing, school and workplace testing, self-tests, and more in-patient settings like nursing homes. The federal and State Government also covered for Covid-19 testing, vaccination and treatment for the uninsured population, creating opportunities for fraud and unnecessary testing, double billing, kickbacks, and deceased billing, mainly for monetary gain, by unscrupulous healthcare providers.
Aim
The aim of the study is to employ a mixed-method approach and data mining techniques to detect instances of healthcare fraud, waste, and abuse in COVID-19 claims reimbursement data for services provided to the uninsured population. This aims to safeguard the integrity of public health resources as well as government preparedness in the wakeup future unforeseen crisis.
Methodology:
The study adopted an unsupervised learning approach. Using Python programming, the study employed the use Isolation Forest to detect healthcare providers who were anomalies in the payment of covid-19 testing, treatment and vaccination by HRSA. The study also adopted an official search enquiry into Official U.S Government websites such as the FBI, USDOJ, and the HHS-OIG, for schemes relating to covid testing and treatment.
Results
The Isolation Forest algorithm, set at a 5% contamination level, identified 1,890 healthcare providers (7.64% of total claims) as anomalies. These providers had an average total claims amount of approximately 7.5 million. The highest anomaly claim amounted to 646 million, primarily attributed to “Claims Paid for Testing”. These results support the recommendations given to HRSA by the Office of Inspector General of the Department of Health and Human Services (HHS-OIG), emphasizing the need for identifying and addressing improper payments.
Conclusion/Recommendation:
Protecting public health resources requires preventing fraud in the healthcare industry. Strong education programs for healthcare workers are crucial, as are vigilant oversight and collaboration between federal and state agencies. Additionally, this study emphasizes how crucial it is to use official government resources—like the FBI, HHS-OIG, USDOJ, and the CDC—in order to efficiently detect and prevent fraudulent activities. By putting these suggestions into practice, healthcare programs such as COVID-19 testing can be made more reliable and public health resources won't be misused.
WITHDRAWN: The Surface Area of Red Blood Cell Via PDE Method
Authors: Ahmed Jiyar Jafar 
Published: May 2024

Abstract

Using the PDE method to calculate the surface area for shape of red blood cell is the aim of this paper, and this technique depends on the use of an elliptic PDE and four boundary conditions. For finding the surface area first all creating the vectors Xu and Xv from the given radius and height which are obtained from boundary conditions of PDE method, then finding the norm of cross product between Xu and Xv and finally finding double integration of the result of that norm to get the surface area.
Leading from a distance: Experiences of specialist critical care physicians providing telehealth support to rural clinicians
Authors: Santomauro Chiara , McLanders Mia , Gibbs Clinton , Rae Andrew 
Published: May 2024

Abstract

Introduction: The well-known ‘golden hour’ target for trauma treatment is an unachievable goal in many parts of Australia. In response to the vast population spread in Queensland, Australia, telehealth infrastructure facilitates 24/7 links between rural facilities and specialist critical care physicians who provide advice and coordinate aeromedical retrievals. The purpose of this study was to understand work-as-done for specialist critical care physicians using Queensland Health’s emergency telehealth system to support rural clinicians during acute care.
Methods: Semi-structured interviews were conducted with 10 specialist critical care physicians operating under the governance of Retrieval Services Queensland to provide advice to rural clinicians through Queensland Health’s emergency telehealth system. Using an inductive approach, qualitative data were analysed in three phases: immersion; a combination of process coding and in vivo coding; and focused coding.
Results: The data revealed that supporting rural teams from a distance can be simultaneously rewarding and challenging. Two categories emerged, each with the same three themes representing key challenges to providing emergency telehealth support. The first category presents these challenges, and the second category describes how telehealth technology can help to overcome, but sometimes contributes to, these challenges.
Conclusions: The findings highlight that, although challenging at times, emergency telehealth support goes far beyond clinical advice; it provides an avenue to support isolated clinicians through critical, high-stress situations. The findings have several implications for telehealth technology that could reduce specialists’ mental workload and provide better access to information, thereby improving the quality of support delivered to rural teams during critical care.
Multifunctional nanocomposite assessment using carbon nanotube fiber sensors
Authors: Butt Hassaan A. , Krasnikov Dmitry V. , Kondrashov Vladislav A. , Voloskov Boris V. , Konev Stepan D. , Vershinina Anna I. , Shandakov Sergey D. , Wang Zeyu , Korsunsky Alexander M. , Sergeichev Ivan V. , Nasibulin Albert G. 
Published: May 2024

Abstract

Here, we propose a novel application of carbon nanotube fibers (CNTFs) for the one-step, dual-stage, non-destructive monitoring of multifunctional conductive nanocomposites. Hierarchical nanocomposites were created by embedding CNTFs into carbon nanotube (CNT) - modified matrices during their manufacturing to assess production variables. CNTFs are then left embedded in the structure for monitoring during nanocomposite application. We investigated the dependence of detection sensitivity and reliability on the CNTF diameter (~ 40–700 µm), electrical conductivity (~ 102-104 S/m), and the choice of measurement technique (2- and 4-point) for single-walled and multiwalled CNT fillers at different concentrations. The sensors showed promising sensitivity to CNT type and concentration, the results were independent of CNTF diameter and contact resistance, and showed low noise. For application monitoring, nanocomposites electrical and mechanical (tensile and cyclic) properties were tested to determine sensitivity to static and dynamic conditions. CNTFs did not cause any reduction in mechanical properties, unlike the losses observed for metallic electrodes (up to 60% reduction in ultimate tensile strength). CNTF-based evaluation of the electrical resistivity (between 102 — 106 Ohm∙cm) and dynamic electrical response (gauge factor between ~ 2 — 12) matched values from a standard electrode material. Microstructural analysis proved that this unique performance was due to the surface and internal volume infiltration of the nanocomposite matrices into the CNTFs, causing interconnection of the CNTs of the matrix and CNTFs. These findings show that CNTFs may be used to accurately monitor nanocomposite multifunctional properties both during manufacturing and application using one-step integration, regardless of the sample size and manufacturing technology.
Polymorphism rs117585797 of the ANO2 gene as a genetic marker of a high risk of developing combined proliferative diseases of the uterus in women with endometriosis
Authors: Golovchenko I. O. , Ponomarenko I. V. , Churnosov M. I. 
Published: May 2024

Introduction. Endometriosis is a common gynecological disease that has important socio-economic significance for both sick women, their families, and society as a whole. It is often combined with other hyperplastic diseases of the uterus.Aim. To analyze the involvement of GWAS-significant polymorphic loci of sex hormone genes in the development of combined proliferative diseases of the uterus in women with endometriosis. Materials and methods. The sample for the study included 395 women, including 103 patients with isolated endometriosis and 292 patients with endometriosis in combination with uterine fibroids and/or endometrial hyperplasia. All women underwent molecular genetic testing of seven GWAS-significant single-nucleotide polymorphic variants of sex hormone genes (rs34670419, rs11031005, rs11031002, rs148982377, rs112295236, rs1641549, rs117585797). The analysis of associations of polymorphic variants with the risk of developing combined proliferative diseases of the uterus in patients with endometriosis was carried out. To assess the functional effects of SNP associated with the formation of combined proliferative diseases of the uterus in women with endometriosis, the online programs HaploReg and Gtex Portal were used.Results and discussion. Genetic variant A rs117585797 of the ANO2 gene is a risk factor for the development of combined proliferative diseases of the uterus in women with endometriosis (p = 0.05, рperm = 0.05, OR = 4.29). According to the data obtained in silico, the rs117585797 locus localized in the intron of the ANO2 gene affects the affinity of the regulatory DNA motif to the transcription factors Crx and Gsc.Conclusions. Polymorphic locus rs117585797 of the ANO2 gene is associated with a high risk of the formation of combined proliferative diseases of the uterus in patients with endometriosis.
Iron deficiency in pregnant women: effect on offspring neuropsychic development
Authors: Solovyeva A. V. , Aleynikova E. Yu. , Spitsyna M. A. , Gulenkova K. A. , Jafarova A. M. 
Published: May 2024

Increasing evidence indicates that adversities in utero and early childhood can have long-lasting effects on physiological and mental health not only in childhood but also in adulthood. Micronutrient deficiency, and in particular iron deficiency, is one of the most significant factors. Based on literature data, to determine the effect of iron deficiency in pregnant women on the neuropsychological development of infants, as well as to determine a cohort of pregnant women who need iron supplements. The search was conducted in MEDLINE (from PubMed), EMBASE (from OVID), Cochrane Library and ClinicalTrials.gov from 2010 to December 2023 without language restrictions. Data were extracted. Primary outcomes included iron deficiency, serum ferritin less than 15 μg/L in pregnant women, perinatal outcomes, and development of children in infancy. The data of serum ferritin in umbilical cord blood in newborns were compared with the serum ferritin of mothers. Data on iron supplementation and its effect on perinatal outcomes were assessed. Daily oral iron supplementation during pregnancy in women without IDA or ID reduces the incidence of iron deficiency anemia at the woman’s due date, reduces the incidence of iron deficiency in fullterm neonates, and reduces the incidence of morbidity in low-birth-weight infants. Iron supplementation during pregnancy and the postpartum period is a strategy for timely replenishment of ID and prevention of IDA to reduce cerebral iron deficiency in the fetus and newborn. The absence of side effects and high bioavailability, enhanced by ascorbic acid, allow us to recommend SunActive®Fe Bonoferlat as a daily supplement for pregnant and lactating women, according to WHO recommendations.