Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge, attitude, and practice of menstrual blood-derived mesenchymal stem cells among female healthcare workers in India

Introduction Mesenchymal stem cells (MSCs) are pluripotent progenitor cells that can be differentiated into a variety of specialized cell types. Menstrual blood, such as umbilical cord blood and bone marrow stem cells, is a rich source of MSCs with proliferative properties. This research was conducted to understand the knowledge, attitude, and practice of menstrual blood donation regarding menstrual blood-derived mesenchymal stem cells (MenSCs) among female healthcare workers in India. Methodology A cross-sectional online and offline survey was conducted at the national level between 20 November 2021 and 10 March 2022. A self-constructed semi-structured questionnaire was distributed through Google Forms on various social media platforms. The questionnaire was self-administered, and data were collected using the purposive sampling technique. Results A total of 499 respondents completed the questionnaire. Nearly 49% of the respondents had adequate knowledge, 54% showed a positive attitude, and 45% reported adequate practices regarding menstrual blood donation and the usage of related products. The educational background, occupational status, and monthly income of the participants were found to be significantly associated with their attitude toward MenSCs. Conclusion There is a need to promote interactive sessions on MenSCs among healthcare professionals in order to bridge the gap between general populations and the healthcare setting. Enhancing knowledge and awareness regarding the potential benefits of MenSCs would help in dissipating the age-old myths associated with menstruation and will benefit society as a whole.
Unraveling complex relationships between COVID-19 risk factors using machine learning based models for predicting mortality of hospitalized patients and identification of high-risk group: a large retrospective study

Background At the end of 2019, the coronavirus disease 2019 (COVID-19) pandemic increased the hospital burden of COVID-19 caused by the SARS-Cov-2 and became the most significant health challenge for nations worldwide. The severity and high mortality of COVID-19 have been correlated with various demographic characteristics and clinical manifestations. Prediction of mortality rate, identification of risk factors, and classification of patients played a crucial role in managing COVID-19 patients. Our purpose was to develop machine learning (ML)-based models for the prediction of mortality and severity among patients with COVID-19. Identifying the most important predictors and unraveling their relationships by classification of patients to the low-, moderate- and high-risk groups might guide prioritizing treatment decisions and a better understanding of interactions between factors. A detailed evaluation of patient data is believed to be important since COVID-19 resurgence is underway in many countries. Results The findings of this study revealed that the ML-based statistically inspired modification of the partial least square (SIMPLS) method could predict the in-hospital mortality among COVID-19 patients. The prediction model was developed using 19 predictors including clinical variables, comorbidities, and blood markers with moderate predictability (Q2 = 0.24) to separate survivors and non-survivors. Oxygen saturation level, loss of consciousness, and chronic kidney disease (CKD) were the top mortality predictors. Correlation analysis showed different correlation patterns among predictors for each non-survivor and survivor cohort separately. The main prediction model was verified using other ML-based analyses with a high area under the curve (AUC) (0.81−0.93) and specificity (0.94−0.99). The obtained data revealed that the mortality prediction model can be different for males and females with diverse predictors. Patients were classified into four clusters of mortality risk and identified the patients at the highest risk of mortality, which accentuated the most significant predictors correlating with mortality. Conclusion An ML model for predicting mortality among hospitalized COVID-19 patients was developed considering the interactions between factors that may reduce the complexity of clinical decision-making processes. The most predictive factors related to patient mortality were identified by assessing and classifying patients into different groups based on their sex and mortality risk (low-, moderate-, and high-risk groups).
The Statistical Content of Elementary School Mathematics Textbooks

This study reports on the statistical content of five U.S. textbook series written for elementary students in grades 1-5. The researchers examined 17,688 pages and coded 7445 statistical tasks to determine (1) the distribution of statistical topics within textbooks, and (2) the relative emphasis on the phases of the statistical problem solving process (Formulate Questions, Collect Data, Analyze Data, and Interpret Results). Different series contained markedly different distributions of statistical content: two series located most statistical content near the end of the text, whereas two other series located statistical tasks more uniformly throughout the textbook. A large majority of statistical tasks required students to Analyze Data, with a heavy emphasis on the activities of reading displays and performing mathematical calculations.
Insights into nanoparticles-induced neurotoxicity and cope up strategies

Nanoparticle applications are becoming increasingly popular in fields such as photonics, catalysis, magnetics, biotechnology, manufacturing of cosmetics, pharmaceuticals, and medicines. There is still a huge pile of undermining information about the potential toxicity of these products to humans, which can be encountered by neuroprotective antioxidants and anti-inflammatory compounds. Nanoparticles can be administered using a variety of methods, including oronasal, topical applications, and enteral and parenteral routes of administration. There are different properties of these nanomaterials that characterize different pathways. Crossing of the blood-brain barrier, a direct sensory nerve-to-brain pathway whose barriers are bypassed, these checks otherwise prevent the nanoparticles from entering the brain. This inflicts damage to sensory neurons and receptors by nanoparticles that lead to neurotoxicity of the central nervous system. A number of routes make nanoparticles able to penetrate through the skin. Exposure by various routes to these nanoparticles can result in oxidative stress, and immune suppression triggers inflammatory cascades and genome-level mutations after they are introduced into the body. To out-power, these complications, plant-based antioxidants, essential oils, and dietary supplements can be put into use. Direct nanoparticle transport pathways from sensory nerves to the brain via blood have been studied grossly. Recent findings regarding the direct pathways through which nanoparticles cross the blood-brain barriers, how nanoparticles elicit different responses on sensory receptors and nerves, how they cause central neurotoxicity and neurodegeneration through sensory nerve routes, and the possible mechanisms that outcast these effects are discussed.
Comparison between transrectal palpation, B-mode and Doppler ultrasonography to assess luteal function in Holstein cattle

Introduction Over the years, the most common methods for monitoring reproductive health in cattle have varied from transrectal palpation to B-mode ultrasonography. Nowadays, some portable ultrasound equipment includes the Doppler mode. Therefore, the aim of this study was to compare the accuracy of the different methods to assess corpus luteum (CL) functionality. Methods In Experiment 1, 53 Holstein lactating cows undergoing a synchronization protocol were examined via transrectal palpation and B-mode scanning. Measurements for the largest diameter (LAD) and subjective size of CL (SCLS) were collected. Data were analyzed using correlation analysis and ROC Curves. In Experiment 2, 30 Holstein non-lactating cows with a CL were administered PGF2α and examined several times after injection, first in B-mode and then with Power Doppler. Measurements for LAD, CL area (CLA) and subjective and objective CL blood flow were collected. Blood samples were taken in both experiments to determine P4 concentration. Data were analyzed using correlation analysis and the GLM repeated measures test. Results Results for Experiment 1 showed that LAD was more accurate than SCLS. In Experiment 2, CLA was the best measurement to assess CL function, although both subjective and objective CL blood flow offer accurate information 24 h after PGF2α administration. Discussion Consequently, ultrasonography provides more accurate information about CL function than transrectal palpation. Although CLA seems to be an earlier indicator of luteal function than blood flow, 24 h after the onset of luteolysis, both parameters are valid.
Estimating hidden fishing activity hotspots from vessel transmitted data

Monitoring fishery activity is essential for resource planning and guaranteeing fisheries sustainability. Large fishing vessels constantly and continuously communicate their positions via Automatic Identification System (AIS) or Vessel Monitoring Systems (VMSs). These systems can use radio or Global Positioning System (GPS) devices to transmit data. Processing and integrating these big data with other fisheries data allows for exploring the relations between socio-economic and ecosystem assets in marine areas, which is fundamental in fishery monitoring. In this context, estimating actual fishing activity from time series of AIS and VMS data would enhance the correct identification of fishing activity patterns and help assess regulations' effectiveness. However, these data might contain gaps because of technical issues such as limited coverage of the terrestrial receivers or saturated transmission bands. Other sources of data gaps are adverse meteorological conditions and voluntary switch-offs. Gaps may also include hidden (unreported) fishing activity whose quantification would improve actual fishing activity estimation. This paper presents a workflow for AIS/VMS big-data analysis that estimates potential unreported fishing activity hotspots in a marine area. The workflow uses a statistical spatial analysis over vessel speeds and coordinates and a multi-source data integration approach that can work on multiple areas and multiple analysis scales. Specifically, it (i) estimates fishing activity locations and rebuilds data gaps, (ii) estimates the potential unreported fishing hour distribution and the unreported-over-total ratio of fishing hours at a 0.01° spatial resolution, (iii) identifies potential unreported fishing activity hotspots, (iv) extracts the stocks involved in these hotspots (using global-scale repositories of stock and species observation data) and raises an alert about their possible endangered, threatened, and protected (ETP) status. The workflow is also a free-to-use Web Service running on an open science-compliant cloud computing platform with a Web Processing Service (WPS) standard interface, allowing efficient big data processing. As a study case, we focussed on the Adriatic Sea. We reconstructed the monthly reported and potential unreported trawling activity in 2019, using terrestrial AIS data with a 5-min sampling period, containing ~50 million records transmitted by ~1,600 vessels. The results highlight that the unreported fishing activity hotspots especially impacted Italian coasts and some forbidden and protected areas. The potential unreported activity involved 33 stocks, four of which were ETP species in the basin. The extracted information agreed with expert studies, and the estimated trawling patterns agreed with those produced by the Global Fishing Watch.
A systematic review of handover actions in human dyads

Introduction Handover actions are joint actions in which an object is passed from one actor to another. In order to carry out a smooth handover action, precise coordination of both actors’ movements is of critical importance. This requires the synchronization of both the kinematics of the reaching movement and the grip forces of the two actors during the interaction. Psychologists, for example, may be interested in studying handover actions in order to identify the cognitive mechanisms underlying the interaction of two partners. In addition, robotic engineers may utilize insights from sensorimotor information processing in human handover as models for the design controllers in robots in hybrid (human-robot) interaction scenarios. To date, there is little knowledge transfer between researchers in different disciplines and no common framework or language for the study of handover actions. Methods For this reason, we systematically reviewed the literature on human-human handover actions in which at least one of the two types of behavioral data, kinematics or grip force, was measured. Results Nine relevant studies were identified. The different methodologies and results of the individual studies are here described and contextualized. Discussion Based on these results, a common framework is suggested that, provides a distinct and straightforward language and systematics for use in future studies. We suggest to term the actors as giver and receiver, as well as to subdivide the whole action into four phases: (1) Reach and grasp, (2) object transport, (3) object transfer, and (4) end of handover to comprehensively and clearly describe the handover action. The framework aims to foster the necessary exchange between different scientific disciplines to promote research on handover actions. Overall, the results support the assumption that givers adapt their executions according to the receiver’s intentions, that the start of the release of the object is processed feedforward and that the release process is feedback-controlled in the transfer phase. We identified the action planning of the receiver as a research gap.
Post-traumatic stress disorder and depressive symptoms among firefighters: a network analysis

Background Firefighters, as first responders with a high risk of occupational exposure to traumatic events and heavy working stress, have a high prevalence of PTSD symptoms and depressive symptoms. But no previous studies analyzed the relationships and hierarchies of PTSD and depressive symptoms among firefighters. Network analysis is a novel and effective method for investigating the complex interactions of mental disorders at the symptom level and providing a new understanding of psychopathology. The current study was designed to characterize the PTSD and depressive symptoms network structure in the Chinese firefighters. Method The Primary Care PTSD Screen for DSM-5 (PC-PTSD-5) and the Self-Rating Depression Scale (SDS) were applied to assess PTSD and depressive symptoms, respectively. The network structure of PTSD and depressive symptoms was characterized using “expected influence (EI)” and “bridge EI” as centrality indices. The Walktrap algorithm was conducted to identify communities in the PTSD and depressive symptoms network. Finally, Network accuracy and stability were examined using the Bootstrapped test and the case-dropping procedure. Results A total of 1,768 firefighters were enrolled in our research. Network analysis revealed that the relationship between PTSD symptoms, “Flashback” and “Avoidance,” was the strongest. “Life emptiness” was the most central symptom with the highest EI in the PTSD and depression network model. Followed by “Fatigue” and “Interest loss. ” Bridge symptoms connecting PTSD and depressive symptoms in our study were “Numb,” “High alertness,” “Sad mood,” and “Compunction and blame,” successively. The data-driven community detection suggested the differences in PTSD symptoms in the clustering process. The reliability of the network was approved by both stability and accuracy tests. Conclusion To the best of our knowledge, the current study first demonstrated the network structure of PTSD and depressive symptoms among Chinese firefighters, identifying the central and bridge symptoms. Targeting interventions to the symptoms mentioned above may effectively treat firefighters suffering from PTSD and depressive symptoms.
Application of microalgae Scenedesmus acuminatus enhances water quality in rice-crayfish culture

Improper management of aquatic environments substantially restricts the development of the aquaculture industry. The industrialisation of the crayfish Procambarus clarkii, for example, is currently being limited by poor water quality. Research suggests that microalgal biotechnology has a great potential for water quality regulation. However, the ecological effects of microalgal applications on aquatic communities in aquaculture systems remain largely unknown. In the present study, 5 L Scenedesmus acuminatus GT-2 culture (biomass 120 g L-1) was added to an approximately 1,000 m2 rice-crayfish culture to examine the response of aquatic ecosystems to microalgal application. The total nitrogen content decreased significantly as a result of microalgal addition. Moreover, the microalgal addition changed the bacterial community structure directionally and produced more nitrate reducing and aerobic bacteria. The effect of microalgal addition on plankton community structure was not obvious, except for a significant difference in Spirogyra growth which was inhibited by 81.0% under microalgal addition. Furthermore, the network of microorganisms in culture systems with the added microalga had higher interconnectivity and was more complex, which indicating microalgal application enhance the stability of aquaculture systems. The application of microalgae was found to have the greatest effect on the 6th day of the experiment, as supported by both environmental and biological evidence. These findings can provide valuable guidance for the practical application of microalgae in aquaculture systems.
Whole genome sequencing of Ethiopian Brucella abortus isolates expands the known diversity of an early branching sub-Saharan African lineage

Brucellosis remains one of the most significant zoonotic diseases globally, responsible for both considerable human morbidity and economic losses due to its impacts on livestock productivity. Despite this, there remain significant evidence gaps in many low- and middle-income countries, including those of sub-Saharan Africa. Here we report the first molecular characterisation of Brucella sp. from Ethiopia. Fifteen Brucella sp. isolates from an outbreak in cattle from a herd in central Ethiopia were identified as Brucella abortus, using bacterial culture and molecular methods. Sequencing of the Ethiopian B. abortus isolates allowed their phylogenetic comparison with 411 B. abortus strains of diverse geographical origins, using whole genome single nucleotide polymorphisms (wgSNP). The Ethiopian isolates belonged to an early-branching lineage (Lineage A) previously only represented by data from two strains, both of sub-Saharan African origin (Kenya and Mozambique). A second B. abortus lineage (Lineage B), also comprised solely of strains originating from sub-Saharan Africa, was identified. The majority of strains belonged to one of two lineages of strains originating from a much broader geographical range. Further analyses based on multi-locus sequence typing (MLST) and multi-locus variable-number tandem repeat analysis (MLVA) expanded the number of B. abortus strains available for comparison with the Ethiopian isolates and were consistent with the findings from wgSNP analysis. MLST profiles of the Ethiopian isolates expanded the sequence type (ST) diversity of the early branching lineage of B. abortus, equivalent to wgSNP Lineage A. A more diverse cluster of STs, equivalent to wgSNP Lineage B, was comprised solely of strains originating from sub-Saharan Africa. Similarly, analysis of B. abortus MLVA profiles (n = 1891) confirmed that the Ethiopian isolates formed a unique cluster, similar to only two existing strains, and distinct from the majority of other strains of sub-Saharan African origin. These findings expand the known diversity of an under-represented lineage of B. abortus and suggest a potential evolutionary origin for the species in East Africa. In addition to providing information concerning Brucella species extant within Ethiopia this work serves as the basis for further studies on the global population structure and evolutionary history of a major zoonotic pathogen.
HIV-associated photodermatitis in African populations

Photosensitive dermatoses are seen in 5% of HIV-infected persons. These include drug- and chemical-induced photoallergic and phototoxic reactions, chronic actinic dermatitis of HIV, photo lichenoid drug eruptions, and porphyria. Data on photodermatitis in HIV are limited to case reports and series. The pathogenesis is not completely understood and includes a th2 phenotype in HIV which results in impaired barrier function and resultant allergen sensitisation as well as immune dysregulation. The objective of this manuscript is to review the literature on the clinical phenotype, pathogenesis, role of photo and patch testing, outcomes, and treatment of photodermatitis in HIV in an African population.
Matrilin-2 within a three-dimensional lysine-modified chitosan porous scaffold enhances Schwann cell migration and axonal outgrowth for peripheral nerve regeneration

Background: Matrilin-2 is a key extracellular matrix protein involved in peripheral nerve regeneration. We sought to develop a biomimetic scaffold to enhance peripheral nerve regeneration by incorporating matrilin-2 within a chitosan-derived porous scaffold. We hypothesized that the use of such a novel biomaterial delivers microenvironmental cues to facilitate Schwann cell (SC) migration and enhance axonal outgrowth during peripheral nerve regeneration. Materials and Methods: The effect of matrilin-2 on SC migration was evaluated with agarose drop migration assay on matrilin-2 coated dishes. SC adhesion was determined with SCs cultured atop tissue culture dishes coated with matrilin-2. Various formulations of chitosan vs matrilin-2 in scaffold constructs were examined with scanning electron microscopy. The effect of the matrilin-2/chitosan scaffold on SC migration in the collagen conduits was determined by capillary migration assays. Neuronal adhesion and axonal outgrowth were evaluated with three-dimensional (3D) organotypic assay of dorsal root ganglions (DRG). DRG axonal outgrowth within the scaffolds was determined by immunofluorescence staining of neurofilaments. Results: Matrilin-2 induced SC migration and enhanced its adhesion. A formulation of 2% chitosan with matrilin-2 demonstrated an optimal 3D porous architecture for SC interaction. Matrilin-2/chitosan scaffold enabled SCs to migrate against gravity within conduits. Chemical modification of chitosan with lysine (K-chitosan) further improved DRG adhesion and axonal outgrowth than the matrilin-2/chitosan scaffold without lysine modification. Conclusion: We developed a matrilin-2/K-chitosan scaffold to mimic extracellular matrix cues and provide a porous matrix to enhance peripheral nerve regeneration. Taking advantage of matrilin-2’s capability to stimulate SC migration and adhesion, we formulated a porous matrilin-2/chitosan scaffold to support axongal outgrowth. Chemical modification of chitosan with lysine further improved matrilin-2 bioactivity in the 3D scaffold. The 3D porous matrilin-2/K-chitosan scaffolds have high potential for enhancing nerve repair by stimulating SC migration, neuronal adhesion, and axonal outgrowth.