Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute respiratory distress syndrome: A study of autopsy findings

Context: In this autopsy study, the various morphological patterns of acute respiratory distress syndrome (ARDS) have been analyzed and compared along with their etiopathogenesis. Aims: We aimed to study the prevalence and clinicopathological correlation of ARDS based on age, gender, hospital stay, symptoms, clinical diagnosis, gross, and microscopy findings. Subjects and Methods: Total 130 cases of ARDS were studied over a period of 5 years. Age, gender, hospital stay duration, symptoms, clinical diagnosis, gross and microscopic lung finding, clinicopathological correlation, and cause of death were documented and analyzed. Special stains were done whenever required. Statistical Analysis: This is an observational study, and simple statistics such as mean, median, and standard deviation have been used for continuous variables. Results: The prevalence of ARDS among the adult autopsy was 6.05%. Majority of the cases were in the age group of 18–30 years (36.9%), with a male: female ratio of 1.7:1. Chief complaints were fever (71%), breathlessness (54.6%), and chills (43.8%). The main clinical diagnoses were ARDS (41.6%), sepsis (28.3%), acute febrile illness (17%), and lower respiratory tract infection (12.5%). Most of the patients had a hospital stay of <1 day. Associated conditions mostly included chronic alcoholism (16.1%), pregnancy (16.1%), and chronic smoking (10.7%). Major findings on gross examination were intrapulmonary hemorrhage (38.5%), ARDS (33%), pulmonary edema (13%), and pneumonia (15.3%). On microscopy, major findings were hyaline membrane (84.6%), intrapulmonary hemorrhage (76.1%), pulmonary edema (75.3%), organizing fibrin (55.3%), and bronchopneumonia (36.2%). Conclusion: Infections were one of the major predisposing causes of ARDS. Due to the short interval, the underlying cause for ARDS often goes undiagnosed.
Associations between use of specific social media sites and electronic cigarette use among college students

Abstract Objective To examine dose-response associations between use of specific social media sites and the use of electronic cigarettes (e-cigarettes) and traditional cigarettes. Methods This was a cross-sectional study of 298 first-year college students enrolled in the fall 2019 semester at a large state university. Heckman selection and Probit model were used to estimate associations between use of specific social media sites and e-cigarette/traditional cigarette use. Results Each additional hour per day spent on Snapchat was associated with a 4.61% increase in the probability of lifetime e-cigarette use. In addition, among current e-cigarette users, more time spent on Snapchat was associated with more frequent e-cigarette use (marginal effects: 0.13, p = 0.001). Facebook, Twitter, Snapchat and Instagram were not associated with traditional cigarette smoking. Conclusion Snapchat was the only major social media platform associated with both lifetime and current e-cigarette use.
Factors that Influence Intent to Share Genetic Information Related to Cancer Risk with Family Members

Abstract We describe factors influencing patient decisions to share positive cancer genetic test results with family members. We focused on patients who were diagnosed with several different cancer types but did not have a family history that was suggestive of an inherited risk. Participants were recruited from Mayo Clinic and had been recently diagnosed with cancer. An 80+ gene panel was performed. Before receiving genetic test results, patients completed a 49-item survey on their intent to share their results with relatives. 1,721 (57.7%) of 2,984 individuals who elected to pursue genetic testing completed the survey. Most patients planned to share cancer-related genetic results with a spouse or partner (97.0%), at least one adult child (92.2%), at least one sibling (86.2%), and with at least one parent (70.3%). Familial support scores and familial communication scores were predictive of intent to share cancer-related genetic test results. Our data highlight differences in family communication capacity and support that are important for clinicians to consider when supporting patients who wish to share cancer-related genetic test results with family members. Our data point to several potential interventional strategies that might increase the likelihood of cancer-related genetic test results being shared with family members at risk.
Loneliness, love attitudes and satisfaction with love life among university students in palestine: Gender differences

Abstract The association between loneliness, love styles and satisfaction with love life among Palestinian university students was examined in this study. Gender differences in loneliness, love styles and satisfaction with love life was examined as well as the relationship among these variables. The study included 500 students (63.6% females and 36.4% males), aged between 18 and 26 years. Data were collected using the Loneliness Scale (UCLA), Love Attitudes Scale (LAS), Satisfaction with Love Life Scale (SWLLS) and a Demographic Information Form. Analyses showed that there were no gender differences in loneliness. Significant gender differences were found on ludic, males were more in their love styles than were females. Loneliness was negatively associated with Eros, Storge and Pragma love styles, and with satisfaction with love life. Multiple regression analysis indicated that all the variables accounted for 18% of the variance in loneliness scores. Satisfaction with love life appears to be the best predictor of university student’s loneliness.
Differential Roles of Three Different Upper Pathway meta Ring Cleavage Product Hydrolases in the Degradation of Dibenzo-p-Dioxin and Dibenzofuran by Sphingomonas wittichii Strain RW1

S. wittichii RW1 is one of only a few strains known to grow on DXN as the sole course of carbon. Much of the work deciphering the related RW1 DXN and DBF catabolic pathways has involved genome gazing, transcriptomics, proteomics, heterologous expression, and enzyme purification and characterization. ABSTRACT Sphingomonas wittichii RW1 grows on the two related compounds dibenzofuran (DBF) and dibenzo-p-dioxin (DXN) as the sole source of carbon. Previous work by others (P. V. Bunz, R. Falchetto, and A. M. Cook, Biodegradation 4:171–178, 1993, https://doi/org/10.1007/BF00695119) identified two upper pathway meta cleavage product hydrolases (DxnB1 and DxnB2) active on the DBF upper pathway metabolite 2-hydroxy-6-oxo-6-(2-hydroxyphenyl)-hexa-2,4-dienoate. We took a physiological approach to determine the role of these two enzymes in the degradation of DBF and DXN by RW1. Single knockouts of either plasmid-located dxnB1 or chromosome-located dxnB2 had no effect on RW1 growth on either DBF or DXN. However, a double-knockout strain lost the ability to grow on DBF but still grew normally on DXN, demonstrating that DxnB1 and DxnB2 are the only hydrolases involved in the DBF upper pathway. Using a transcriptomics-guided approach, we identified a constitutively expressed third hydrolase encoded by the chromosomally located SWIT0910 gene. Knockout of SWIT0910 resulted in a strain that no longer grows on DXN but still grows normally on DBF. Thus, the DxnB1 and DxnB2 hydrolases function in the DBF but not the DXN catabolic pathway, and the SWIT0190 hydrolase functions in the DXN but not the DBF catabolic pathway. IMPORTANCE S. wittichii RW1 is one of only a few strains known to grow on DXN as the sole source of carbon. Much of the work deciphering the related RW1 DXN and DBF catabolic pathways has involved genome gazing, transcriptomics, proteomics, heterologous expression, and enzyme purification and characterization. Very little research has utilized physiological techniques to precisely dissect the genes and enzymes involved in DBF and DXN degradation. Previous work by others identified and extensively characterized two RW1 upper pathway hydrolases. Our present work demonstrates that these two enzymes are involved in DBF but not DXN degradation. In addition, our work identified a third constitutively expressed hydrolase that is involved in DXN but not DBF degradation. Combined with our previous work (T. Y. Mutter and G. J. Zylstra, Appl Environ Microbiol 87:e02464-20, 2021, https://doi.org/10.1128/AEM.02464-20), this means that the RW1 DXN upper pathway involves genes from three very different locations in the genome, including an initial plasmid-encoded dioxygenase and a ring cleavage enzyme and hydrolase encoded on opposite sides of the chromosome.
TESSERAE3D: A BENCHMARK FOR TESSERAE SEMANTIC SEGMENTATION IN 3D POINT CLOUDS

3D point cloud of mosaic tesserae is used by heritage researchers, restorers and archaeologists for digital investigations. Information extraction, pattern analysis and semantic assignment are necessary to complement the geometric information. Automated processes that can speed up the task are highly sought after, especially new supervised approaches. However, the availability of labelled data necessary for training supervised learning models is a significant constraint. This paper introduces Tesserae3D, a 3D point cloud benchmark dataset for training and evaluating machine learning models, applied to mosaic tesserae segmentation. It is a publicly available, very high density and coloured dataset, accompanied by a standard multi-class semantic segmentation baseline. It consists of about 502 million points and contains 11 semantic classes covering a wide range of tesserae types. We propose a semantic segmentation baseline building on radiometric and covariance features fed to ensemble learning methods. The results delineate an achievable 89% F1-score and are made available under https://github.com/akharroubi/Tesserae3D, providing a simple interface to improve the score based on feedback from the research community.
Tuberculosis health profile in indigenous children and adolescents: an integrative review

Objective: To analyze how the literature has addressed the tuberculosis health among indigenous children and adolescents. Method: An integrative review conducted with descriptors indexed in the Medline, Scopus, Embase, Web of Science, and Lilacs databases from primary studies, with complete texts, published in the period 2000 to 2020. Results: 726 publications were identified, and after the application of the previously established criteria, 10 articles were considered eligible, carried out exclusively in Brazil, between 2004 and 2019, with a higher incidence in children under 5 years old, a predominance of the male gender, clinical pulmonary form, being the chest radiography the clinical variable most explored in the studies, the low performance of the tuberculin skin test and nutritional assessment, in addition to a high cure rate. Conclusion: Diagnostic difficulties are evident, including the low exam performance, failure to systematize the search for clinical findings, absence of nutritional assessment, and investigation of contacts. DESCRIPTORS: Health Profile; Tuberculosis; Indigenous Peoples; Child; Adolescent.
Traumatic Brain Injury in Children

Objectives Glial fibrillary acidic protein (GFAP) is a neuronal protein released after traumatic brain injury (TBI) and detectable in serum samples. GFAP correlates with symptom severity in adults and may be a marker of brain injury in children with milder symptoms or preverbal children. Methods GFAP was examined in children with severe TBI (initial Glasgow Coma Scale score <8), with mild TBI (Glasgow Coma Scale score 14/15), and at 0 to 4 and at 10 to 14 days after TBI and was compared with healthy age-matched controls. Mechanism, time points from injury, and symptoms were recorded. Results The study enrolled 208 children including 110 with TBI (n = 104 mild, 6 severe) and controls (n = 98). GFAP was higher in mild TBI than in controls and highest in the severe TBI cohort, with a maximum value at 6 hours from injury. Vomiting was significantly associated with higher GFAP levels, but no association was found with amnesia, loss of consciousness, and the Sports Concussion Assessment Tool. Children reporting >1-point changes from their preinjury functioning on the Post-Concussive Symptom Inventory had higher initial GFAP but not total Post-Concussive Symptom Inventory score changes. Conclusions GFAP identifies children with TBI, even at the milder end of the spectrum, and is strongly associated with postinjury vomiting. It may be a useful marker of pediatric TBI; however, sampling is time critical.
Freeze-drying of ovalbumin-loaded carboxymethyl chitosan nanocapsules: Impact of freezing and annealing procedures on physicochemical properties of the formulation during dried storage

ABSTRACT Nanocapsules were prepared from 6-O-carboxymethyl chitosan, ovalbumin, and calcium chloride as a cross-linker agent. Cryoprotectants were added to the formulation to protect these carriers during freezing and desiccation steps. The nanocapsules showed acceptable stability with regard to the residual humidity and cake appearance. Regarding the stability, particle diameter, and polydispersity index increased over the storage time, especially for formulations stored at room temperature. For all studied lyophilization protocols exclusively δ-mannitol was formed, and the addition of an annealing step maximized mannitol crystallization. The decrease of the pore surface area for annealed formulations resulted of structural changes other than Ostwald ripening.
Genome Sequence of Acinetobacter towneri Strain DSM 16313, Previously Known as the Proposed Type Strain of Acinetobacter seohaensis

Acinetobacter species are widely distributed in the environment and clinical settings worldwide. We report the 2.99-Mbp draft genome sequence of Acinetobacter towneri strain DSM 16313, which was isolated from seawater in South Korea and proposed as a type strain of Acinetobacter seohaensis. Genome comparisons demonstrated that A. seohaensis should be reclassified as A. towneri. ABSTRACT Acinetobacter species are widely distributed in the environment and clinical settings worldwide. We report the 2.99-Mbp draft genome sequence of Acinetobacter towneri strain DSM 16313, which was isolated from seawater in South Korea and proposed as a type strain of Acinetobacter seohaensis. Genome comparisons demonstrated that A. seohaensis should be reclassified as A. towneri.
Synthesis of ZnO Nanoparticles/Nanofibers and Their Luminescence via Electrospinning

This article reports on the synthesis procedure of ZnO nanoparticles/nanofibers structure by electrospinning method using Zinc acetate and polyvinylpyrrolidone (PVP) surfactant as reagents and evaluates their luminescent properties. The microstructure of ZnO nanoparticles/nanofibers was observed by FE-SEM. The phase formation of ZnO nanoparticles/nanofibers was studied by XRD. ZnO nanoparticles/nanofibers structure shows strong luminescence centering at 660 nm, which has potential applications in solid-state lighting.
Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service

A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real‐world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real‐world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.
Anticancer activity evaluation of green synthesised gold–silver alloy nanoparticles on colourectal HT‐29 and prostate DU‐145 carcinoma cell lines

Nanotechnology is an interesting way to produce and deal with fascinating features of materials. Metal nanoparticles offer a vast spectrum of potential applications in medicine and pharmaceutical sciences due to advances in nanotechnology research. This study was aimed to evaluate the anticancer properties of gold–silver (Au–Ag) alloy nanoparticles, which were synthesised using a green method by Galega officinalis ethanolic extract. The antitumour activity of biosynthesised nanoparticles has been assessed in vitro using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay on DU-145 and HT-29, prostate and colon cell lines, respectively. The Au–Ag alloy nanoparticles activity to inhibit the growth of cancer cell lines was recorded in terms of decrease in viable cell count in comparison with the control value. The spherical Au–Ag alloy nanoparticles with average size of 100 nm diameter were synthesised. The growth inhibition of the human prostate (DU-145) and colon carcinoma cell line (HT-29) has been found to be dose dependent. The calculated IC50 for the DU-145 cell line was 95.78 μg/ml after 24 h and for the HT-29 cell line was 70.66 µg/ml after 48 h. The results of this study suggest that Au–Ag alloy nanoparticles could be as an active substance against tumour cell lines and may be a beneficial treatment for cancer.
Ascorbic acid supports ex vivo generation of plasmacytoid dendritic cells from circulating hematopoietic stem cells

Plasmacytoid dendritic cells (pDCs) constitute a rare type of immune cell with multifaceted functions, but their potential use as a cell-based immunotherapy is challenged by the scarce cell numbers that can be extracted from blood. Here, we systematically investigate culture parameters for generating pDCs from hematopoietic stem and progenitor cells (HSPCs). Using optimized conditions combined with implementation of HSPC pre-expansion, we generate an average of 465 million HSPC-derived pDCs (HSPC-pDCs) starting from 100,000 cord blood-derived HSPCs. Furthermore, we demonstrate that such protocol allows HSPC-pDC generation from whole-blood HSPCs, and these cells display a pDC phenotype and function. Using GMP-compliant medium, we observe a remarkable loss of TLR7/9 responses, which is rescued by ascorbic acid supplementation. Ascorbic acid induces transcriptional signatures associated with pDC-specific innate immune pathways, suggesting an undescribed role of ascorbic acid for pDC functionality. This constitutes the first protocol for generating pDCs from whole blood and lays the foundation for investigating HSPC-pDCs for cell-based immunotherapy.
Perspective: Ab initio force field methods derived from quantum mechanics

It is often desirable to accurately and efficiently model the behavior of large molecular systems in the condensed phase (thousands to tens of thousands of atoms) over long time scales (from nanoseconds to milliseconds). In these cases, ab initio methods are difficult due to the increasing computational cost with the number of electrons. A more computationally attractive alternative is to perform the simulations at the atomic level using a parameterized function to model the electronic energy. Many empirical force fields have been developed for this purpose. However, the functions that are used to model interatomic and intermolecular interactions contain many fitted parameters obtained from selected model systems, and such classical force fields cannot properly simulate important electronic effects. Furthermore, while such force fields are computationally affordable, they are not reliable when applied to systems that differ significantly from those used in their parameterization. They also cannot provide .
Distribution, excretion and metabolic pathways of a single parenteral administration of kappa-opioid receptor agonist RU-1205

Introduction: The purpose was to study the pharmacokinetic properties of RU-1205 with the previously identified kappa-agonistic and analgesic effects after parenteral administration. Materials and methods: Pharmacokinetic parameters of RU-1205 after intravenous and subcutaneous administration at doses of 10 mg/kg and 50 mg/kg, respectively, were investigated, using the method of high-performance liquid chromatography with measurement of the compound according to a pre-established calibration curve. The indices of the area under the pharmacokinetic curve, clearance, half-life, residence time of the drug molecule in the body, total (apparent) volume of distribution, as well as the indicator of absolute bioavailability for subcutaneous administration were calculated. Tissue distribution and excretion of RU-1205 were also studied. Evaluation of metabolism of RU-1205 was conducted in silico, using the PALLAS 3.00 software, with the use of specific tests with CYP 450 substrates and by studying the ability of RU-1205 to form conjugates with endogenous acids. Results and discussion: It was found that after a single intravenous administration, the investigated substance was determined in the blood for 12 h; the half-life was 8.49 hours. The absolute bioavailability after subcutaneous administration is 57.35%. RU-1205 is eliminated within 3–4 days. The main route of excretion is extrarenal. The biotransformation of the substance probably proceeds mainly with the formation of oxidized forms of the initial molecule according to the reactions of the first phase of metabolic transformation, so the chance to observe phase 2 of the metabolism could be very low. Conclusion: The test substance undergoes a long process of elimination, has the highest tropism to the elimination organs and undergoes active biotransformation processes in the body of animals.
A hybrid deep learning model for air quality time series prediction

Air quality (mainly PM2.5) forecasting plays an important role in the early detection and control of air pollution. In recent times, numerous deep learning-based models have been proposed to forecast air quality more accurately. The success of these deep learning models heavily depends on the two key factors viz. proper representation of the input data and preservation of temporal order of the input data during the feature’s extraction phase. Here we propose a hybrid deep neural network (HDNN) framework to forecast the PM2.5 by integrating two popular deep learning architectures, viz. Convolutional neural network (CNN) and bidirectional long short-term memory (BDLSTM) network. Here we build a 3D input tensor so that CNN can extract the trends and spatial features more accurately within the input window. Here we also introduce a linking layer between CNN and BDLSTM to maintain the temporal ordering of feature vectors. In the end, our proposed HDNN framework is compared with the state-of-the-art models, and we show that HDNN outruns other models in terms of prediction accuracy.