Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study

Background Unlike research project–based health data collection (questionnaires and interviews), social media platforms allow patients to freely discuss their health status and obtain peer support. Previous literature has pointed out that both public and private social platforms can serve as data sources for analysis. Objective This study aimed to use natural language processing (NLP) techniques to identify concerns regarding the postoperative quality of life and symptom burdens in patients with uterine fibroids after focused ultrasound ablation surgery. Methods Screenshots taken from clinician-patient WeChat groups were converted into free texts using image text recognition technology and used as the research object of this study. From 408 patients diagnosed with uterine fibroids in Chongqing Haifu Hospital between 2010 and 2020, we searched for symptom burdens in over 900,000 words of WeChat group chats. We first built a corpus of symptoms by manually coding 30% of the WeChat texts and then used regular expressions in Python to crawl symptom information from the remaining texts based on this corpus. We compared the results with a manual review (gold standard) of the same records. Finally, we analyzed the relationship between the population baseline data and conceptual symptoms; quantitative and qualitative results were examined. Results A total of 408 patients with uterine fibroids were included in the study; 190,000 words of free text were obtained after data cleaning. The mean age of the patients was 39.94 (SD 6.81) years, and their mean BMI was 22.18 (SD 2.78) kg/m2. The median reporting times of the 7 major symptoms were 21, 26, 57, 2, 18, 30, and 49 days. Logistic regression models identified preoperative menstrual duration (odds ratio [OR] 1.14, 95% CI 5.86-6.37; P=.009), age of menophania (OR –1.02 , 95% CI 11.96-13.47; P=.03), and the number (OR 2.34, 95% CI 1.45-1.83; P=.04) and size of fibroids (OR 0.12, 95% CI 2.43-3.51; P=.04) as significant risk factors for postoperative symptoms. Conclusions Unstructured free texts from social media platforms extracted by NLP technology can be used for analysis. By extracting the conceptual information about patients’ health-related quality of life, we can adopt personalized treatment for patients at different stages of recovery to improve their quality of life. Python-based text mining of free-text data can accurately extract symptom burden and save considerable time compared to manual review, maximizing the utility of the extant information in population-based electronic health records for comparative effectiveness research.
Fractalkine Signalling (CX3CL1/CX3CR1 Axis) as an Emerging Target in Coronary Artery Disease

Acute myocardial infarction (MI) is the most common and dramatic complication of atherosclerosis, which, despite successful reperfusion therapy, can lead to incident heart failure (HF). HF occurs when the healing process is impaired due to adverse left ventricular remodelling, and can be the result of so-called ischaemia/reperfusion injury (IRI), visualised by the development of intramyocardial haemorrhage (IMH) or microvascular obstruction (MVO) in cardiac MRI. Thus far, translation of novel pharmacological strategies from preclinical studies to target either IRI or HF post MI have been largely unsuccessful. Anti-inflammatory therapies also carry the risk of affecting the immune system. Fractalkine (FKN, CX3CL1) is a unique chemokine, present as a transmembrane protein on the endothelium, or following cleavage as a soluble ligand, attracting leukocyte subsets expressing the corresponding receptor CX3CR1. We have shown previously that the fractalkine receptor CX3CR1 is associated with MVO in patients undergoing primary PCI. Moreover, inhibition of CX3CR1 with an allosteric small molecule antagonist (KAND567) in the rat MI model reduces acute infarct size, inflammation, and IMH. Here we review the cellular biology of fractalkine and its receptor, along with ongoing studies that introduce CX3CR1 as a future target in coronary artery disease, specifically in patients with myocardial infarction.
The Impact of Covid-19 Pandemic on Audit Quality: The Perception of Indonesian Auditors

The thick atmosphere of uncertainty at the macro and micro economic levels due to the Covid-19 pandemic can affect audit quality. The purpose of this study is to provide empirical evidence about the impact of the Covid-19 pandemic on audit quality from the perceptions of Indonesian auditors. The population of this research is all auditors who work in Public Accounting Firms in Indonesia. Samples were selected randomly from those who were willing to fill out a questionnaire. Dissemination of the questionnaire using google forms. The data was processed using Excel and SPSS software. Hypothesis testing using a non-parametric One Sample Wilcoxon Signed Ranks Test. The results of this study indicate that the Covid-19 pandemic has a significant effect on audit quality on all dimensions of audit quality (going concern, audit fees, auditor HR quality, audit procedures, and auditor salaries). Learning from the case of the Covid-19 pandemic, it is necessary to change the audit paradigm through increasing technological competence and digital transformation in training and audit practices going forward.
Production of Ceramics/Metal Oxide Nanofibers via Electrospinning: New Insights into the Photocatalytic and Bactericidal Mechanisms

Environmental pollution is steadily rising and is having a negative influence on all living things, especially human beings. The advancement of nanoscience in recent decades has provided potential to address this issue. Functional metal oxide nanoparticles/nanofibers have been having a pull-on effect in the biological and environmental domains of nanobiotechnology. Current work, for the first time, is focusing on the electrospinning production of Zr0.5Sn0. 5TiO3/SnO2 ceramic nanofibers that may be utilized to battle lethal infections swiftly and inexpensively. By using characterizations like XRD, FT–IR, FESEM, TEM, PL, and UV–Vis–DRS, the composition, structure, morphology, and optical absorption of samples were determined. The minimum inhibitory concentration (MIC) approach was used to investigate the antibacterial activity. Notably, this research indicated that nanofibers exert antibacterial action against both Gram-positive and Gram-negative bacteria with a MIC of 25 µg/mL. Furthermore, negatively charged E. coli was drawn to positively charged metal ions of Zr0.5Sn0. 5TiO3/SnO2, which showed a robust inhibitory effect against E. coli. It was interesting to discover that, compared to pure TiO2, Zr0.5Sn0. 5TiO3/SnO2 nanofibers revealed increased photocatalytic activity and exceptional cyclability to the photodegradation of Rhodamine B. The composite completely degrades dye in 30 min with 100% efficacy and excellent (97%) reusability. The synergetic effects of Zr0.5Sn0. 5TiO3 and SnO2 may be responsible for increased photocatalytic and bactericidal activity.
Shotgun Threshold for Sparse Erdős–Rényi Graphs

In the shotgun assembly problem for a graph, we are given the empirical profile for rooted neighborhoods of depth <inline-formula> <tex-math notation="LaTeX">$r$ </tex-math></inline-formula> (up to isomorphism) for some <inline-formula> <tex-math notation="LaTeX">$r\geq 1$ </tex-math></inline-formula> and we wish to recover the underlying graph up to isomorphism. When the underlying graph is an Erdős–Rényi <inline-formula> <tex-math notation="LaTeX">$\mathcal G\left({n, \frac {\lambda }{n}}\right)$ </tex-math></inline-formula>, we show that the shotgun assembly threshold <inline-formula> <tex-math notation="LaTeX">$r_{*}$ </tex-math></inline-formula> satisfies that <inline-formula> <tex-math notation="LaTeX">$r_{*} \approx \frac { \log n}{\log (\lambda ^{2} \gamma _{\lambda})^{-1}}$ </tex-math></inline-formula> where <inline-formula> <tex-math notation="LaTeX">$\gamma _{\lambda} $ </tex-math></inline-formula> is the probability for two independent Poisson–Galton–Watson trees with parameter <inline-formula> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> to be rooted isomorphic with each other. Our result sharpens a constant factor in a previous work by Mossel and Ross (2019) and thus solves a question therein.
Vertical Momentum Transport by Internal Gravity Waves Above the Equatorial Undercurrent at 140°W

Strong vertical shears occur in the upper Equatorial Ocean as the trade winds drive the South Equatorial Current westward above the eastward flowing Equatorial Undercurrent. An extremely large “effective viscosity” or vertical momentum transport is required to maintain the speed‐differential between the currents as observed. In the 2012 EquatorMix Experiment data from a 1.8 km optical fiber temperature array and a surface scattering radar were combined with high‐resolution shipboard profiling CTD and Doppler sonar measurements to determine the directionality of energetic ∼600 m wavelength internal waves existing above the Undercurrent. A large vertical momentum flux is found (∼10−4 m2 s−2), with waves excited by nocturnal sea surface convection and maintained by near‐surface critical layer over‐reflection. The net downward‐westward momentum flux is an index of the energy lost during reflection below the Undercurrent. Together with near‐surface‐turbulence, these waves provide the momentum transport needed to balance the large‐scale forcing of the equatorial current system.
Peri-Procedural Management of Direct-Acting Oral Anticoagulants (DOACs) in Transcatheter Miniaturized Leadless Pacemaker Implantation

Introduction: Data on peri-operative management of direct-acting oral anticoagulants (DOACs) during transcatheter pacing leadless system (TPS) implantations remain limited. This study aimed to evaluate a standardized DOAC management regime consisting of interruption of a single dose prior to implantation and reinitiation within 6–24 h; also, patient clinical characteristics associated with this approach were identified. Method: Consecutive patients undergoing standard TPS implantation procedures from two Swiss tertiary centers were included. DOAC peri-operative management included the standardized approach (Group 1A) or other approaches (Group 1B). Results: Three hundred and ninety-two pts (mean age 81.4 ± 7.3 years, 66.3% male, left ventricular ejection fraction 55.5 ± 9.6%) underwent TPS implantation. Two hundred and eighty-two pts (71.9%) were under anticoagulation therapy; 192 pts were treated with DOAC; 90 pts were under vitamin-K antagonist. Patients treated with DOAC less often had structural heart disease, diabetes mellitus, and advanced renal failure. The rate of major peri-procedural complications did not differ between groups 1A (n = 115) and 1B (n = 77) (2.6% and 3.8%, p = 0.685). Compared to 1B, 1A patients were implanted with TPS for slow ventricular rate atrial fibrillation (AF) (p = 0.002), in a better overall clinical status, and implanted electively (<0.001). Conclusions: Standardized peri-procedural DOAC management was more often implemented for elective TPS procedures and did not seem to increase bleeding or thromboembolic adverse events.
Energetic Radio Emissions and Possible Terrestrial Gamma‐Ray Flashes Associated With Downward Propagating Negative Leaders

A study of negative polarity energetic in‐cloud pulses (−EIPs) during thunderstorms reveals that −EIPs occur during two distinct stages of downward negative leader development. The first group is observed at the beginning of downward negative leaders, with estimated average source altitude of ~5.9 km between main negative and lower positive charge regions. The second and new group are observed at the very end of downward negative leaders and appear to be produced at low altitudes close to the ground, where image charges amplify the electric field between the leader tip and conducting ground. Negative EIPs appear to occur only in these two scenarios, suggesting that their production require a high background electric field. Their radio emission signatures and occurrence contexts are similar with +EIPs, which are known to be connected to terrestrial gamma‐ray flashes. We thus suggest that these two types of −EIPs may be associated with downward terrestrial gamma ray flashes.
THE EFFECT OF BOARD DIVERSITY ON CORPORATE SOCIAL RESPONSIBILITY DISCLOSURE

This study aims to provide evidence of the effect of board diversity on the disclosure of Corporate Social Responsibility (CSR) in mining sector companies listed on the Indonesia Stock Exchange from 2017 to 2020. The sample used was 156 observations taken by purposive sampling method. This study proves that there is a positive and significant effect of the diversity of the board education level on CSR disclosure. However, the results of this study do not provide evidence of a significant effect of gender diversity, nationality, and board educational background on CSR disclosure.
Deep vs. Shallow: A Comparative Study of Machine Learning and Deep Learning Approaches for Fake Health News Detection

Internet explosion and penetration have amplified the fake news problem that existed even before Internet penetration. This becomes more of a concern, if the news is health-related. To address this issue, this research proposes Content Based Models (CBM) and Feature Based Models (FBM). The difference between the two models lies in the input provided. The CBM only takes news content as the input, whereas the FBM along with the content also takes two readability features as the input. Under each category, the performance of five traditional machine learning techniques: - Decision Tree, Random Forest, Support Vector Machine, AdaBoost-Decision Tree and AdaBoost-Random Forest is compared with two hybrid Deep Learning approaches, namely CNN-LSTM and CNN-BiLSTM. The Fake News Healthcare dataset comprising 9581 articles was utilized for the study. Easy Data Augmentation technique is used to balance this highly imbalanced dataset. The experimental results demonstrate that Feature Based Models perform better than Content Based Models. Among the proposed FBM, the Hybrid CNN - LSTM model had a F1 score of 97.09% and AdaBoost-Random Forest had a F1 Score of 98.9%. Thus, Adaboost-Random Forest under FBM is the best-performing model for the classification of fake news.
Functional Analytic Perspectives on Nonparametric Density Estimation.

Functional Analytic Perspectives on Nonparametric Density Estimation by Robert A. Vandermeulen Chair: Clayton Scott Nonparametric density estimation is a classic problem in statistics. In the standard estimation setting, when one has access to iid samples from an unknown distribution, there exist several established and well-studied nonparametric density estimators. Yet there remains interesting alternative settings which are less well-studied. This work considers two such settings. First we consider the case where the data contains some contamination, i.e. a portion of the data is not distributed according to the density we would like to estimate. In this setting one would like an estimator which is robust to the contaminating data. An approach to this was suggested in Kim and Scott (2012). The estimator in that paper was analytically and experimentally shown to be robust, but no consistency result was presented. In Chapter II it is demonstrated that this estimator is indeed consistent for a class of convex losses. Chapter III introduces a new robust kernel density estimator based on scaling and projection in Hilbert space. This estimator is proven to be consistent and will converge to the true density provided certain assumptions on the contaminating distribution. Its efficacy is demonstrated experimentally by applying it to several datasets. Chapter IV considers a different setting which can be thought of as nonparametric mixture modelling. Here one would x like to estimate multiple densities with access to groups of samples where each sample in a group is known to be distributed according the same unknown density. Tight identifiability bounds and a highly general algorithm for recovery of the densities are presented for this setting. Functional analysis is a unifying theme of these problems. Hilbert spaces in particular are used extensively for the construction of estimators and mathematical analysis.
Temperature correction factor simulation over the heating period

New Regulations on energy efficiency in buildings in the Republic of Serbia legislate values for the temperature correction factor used to calculate the heat flux to the outdoor environment through construction elements of a certain type. The temperature correction factor is used to correct errors caused by calculation of heat losses based on the design outdoor and indoor temperature difference of building elements adjacent to unheated spaces which are in direct contact with the ground or external environment. Since the temperature correction factor directly influences the calculation of total heat losses and total annual energy demand, it is recommended that the temperature correction factor be determined on the basis of measured values of outdoor and indoor air temperature, or, on the basis of the values obtained by the simulation. This paper presents the results of measurements in the High School of Design, Textile and Management in Belgrade in order to assess energy efficiency and the energy performance of buildings. Data obtained on the basis of measurements, such as indoor and outdoor temperatures, are used for the calculation of the heat transfer coefficient for the building envelope elements as well as to calculate a temperature correction factor for the unheated attic space of the building. This paper also offers a dynamic simulation of the multi-zones building in the TRNSYS environment. The aim was to compare temperature correction factor values obtained from measured temperature values, with those calculated from standard, taken from the Regulation table and produced by simulation.
Exploring the Diagnostic Potential of IHC and Next-Generation Sequencing Approaches for Endometrial Carcinoma Detection and Classification

Background: Globally, in 2018 endometrial cancer (EC) regarded as the sixth most commonly diagnosed cancer and the fourteenth leading cause of cancer with more deaths in females. Nowadays classical and modern classifications can be performing for diagnosing various types of cancers including endometrial cancer. Based on this our Objective of study is to determine the effectiveness of immunohistochemical observation, a quick and easy method for determining MSI and/or other types of EC which based on modern classification. As molecular study takes more cost we want to compare the both methods and showing the possibility of doing IHC instead of molecular study. Methods: This study designed to establish a small gene panel of Next Generation Sequencing NGS of endometrial cancer patients targeting 12 genes; MSH6, MLH3, MSH3, CDH1, PTEN, MSH2, MLH1, PMS2, FGFR2, BRCA1, MUTYH and AREL1. By using DNBSEQ-G400 Platform, Human Core Exome kit and Python software for analysis is used. At the same time, Dako kit used to performing IHC for six primary antibodies used to detect each of MSH6, MSH2, MLH, PMS2, PTEN and CDH1. The primary antibodies were applied on 5 µm formalin- fixed paraffin- embedded (FFPE). Results; histopathological examinations showed that all patients were at stage II endometrioid endometrial carcinoma. The FIGO classification were Ia and Ib. Microsatellite instability (MSI), nuclear staining immunoreaction for PTEN antibodies and membranous E- cadherin immunoreactions observed through IHC study. The molecular studies detected several polymorphisms which have clinical significant and some of them have conflict interpretations. Conclusions; we considered that, simply immunoreaction staining procedure can be use as alternative method for MSI phenotype detection rather than any type of more expensive and complex method of NGS. Concerning PTEN and ECAD1 antibodies reactions, also could be dependable in diagnosis and treatment and performing them if molecular diagnosis not available.
A Systematic Review of the Emerging Treatment for Hepatorenal Syndrome With a Principal Focus on Terlipressin: A Recent FDA-Approved Drug

Background: Hepatorenal syndrome (HRS), a consequence of liver cirrhosis, is the development of renal failure, which carries a grave prognosis. Reversing acute renal failure with various vasoconstrictor therapies at an appropriate time favors a good prognosis, especially when a liver transplant is not feasible. Objective: This study aims to compare various treatment modalities to deduce an effective way to manage HRS. Methods: The authors conducted a literature search in PubMed, Google Scholar, the Cochrane Library, and Science Direct in October 2022, using regular and MeSH keywords. A total of 1072 articles were identified. The PRISMA guidelines were used, the PICO framework was addressed, and the inclusion criteria were set based on studies from the past 10 years. After quality assessment, 14 studies were included for in-depth analysis in this review. Results: A total of 14 studies were included after quality assessment, including randomized controlled trials, systematic reviews, meta-analyses, and observational cohort studies. Nine hundred and forty-one patients represented this review's experimental and observational studies, apart from the other systematic reviews analyzed. Nine studies discovered that Terlipressin, especially when administered with albumin, was more effective than other conventional treatment modalities, including norepinephrine and midodrine, in terms of improving mortality and reversing the HRS. Four studies suggested that terlipressin exhibited similar effectiveness but found no significant difference. In contrast, one study found that norepinephrine was superior to terlipressin when particularly considering the adverse effects. Conclusion: Terlipressin, one of the most widely used vasoconstrictor agents across the world, seems to be effective in reversing renal failure in HRS. Although adverse effects are seen with this agent, it is still beneficial when compared to other medications. Further studies with larger sample sizes may be warranted.
Exploring the Potential of Aptamers in Targeting Neuroinflammation and Neurodegenerative Disorders: Opportunities and Challenges

Neuroinflammation is the precursor for several neurodegenerative diseases (NDDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS). Targeting neuroinflammation has emerged as a promising strategy to address a wide range of CNS pathologies. These NDDs still present significant challenges in terms of limited and ineffective diagnosis and treatment options, driving the need to explore innovative and novel therapeutic alternatives. Aptamers are single-stranded nucleic acids that offer the potential for addressing these challenges through diagnostic and therapeutic applications. In this review, we summarize diagnostic and therapeutic aptamers for inflammatory biomolecules, as well as the inflammatory cells in NDDs. We also discussed the potential of short nucleotides for Aptamer-Based Targeted Brain Delivery through their unique features and modifications, as well as their ability to penetrate the blood-brain barrier. Moreover, the unprecedented opportunities and substantial challenges of using aptamers as therapeutic agents, such as drug efficacy, safety considerations, and pharmacokinetics, are also discussed. Taken together, this review assesses the potential of aptamers as a pioneering approach for target delivery to the CNS and the treatment of neuroinflammation and NDDs.