Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

The influence of sleep on human taste function and perception: A systematic review.

Sleep problems are associated with increased risk of obesity. Multiple mechanisms have been identified to support this relationship, including changes in sensory processing and food choice. Taste researchers have recently begun to explore whether changes in taste occur as a result of short-term or long-term sleep habits. A systematic review was conducted to investigate these relationships. A total of 13 studies were included in the review. Heterogeneity in both the sleep and taste measurements used was noted, and most studies failed to assess sour, bitter and umami tastes. Still, the available evidence suggests that sweet taste hedonic perception appears to be undesirably influenced by short sleep when viewed through the lens of health. That is, preferred sweetness concentration increases as sleep duration decreases. Habitual sleep and interventions curtailing sleep had minimal associations or effects on sweet taste sensitivity. Salt taste sensitivity and hedonic responses appear to be relatively unaffected by insufficient sleep, but more work is needed. Solid evidence on other taste qualities is not available at the present time.

Classical BSE dismissed as the cause of CWD in Norwegian red deer despite strain similarities between both prion agents.

The first case of CWD in a Norwegian red deer was detected by a routine ELISA test and confirmed by western blotting and immunohistochemistry in the brain stem of the animal. Two different western blotting tests were conducted independently in two different laboratories, showing that the red deer glycoprofile was different from the Norwegian CWD reindeer and CWD moose and from North American CWD. The isolate showed nevertheless features similar to the classical BSE (BSE-C) strain. Furthermore, BSE-C could not be excluded based on the PrPSc immunohistochemistry staining in the brainstem and the absence of detectable PrPSc in the lymphoid tissues. Because of the known ability of BSE-C to cross species barriers as well as its zoonotic potential, the CWD red deer isolate was submitted to the EURL Strain Typing Expert Group (STEG) as a BSE-C suspect for further investigation. In addition, different strain typing in vivo and in vitro strategies aiming at identifying the BSE-C strain in the red deer isolate were performed independently in three research groups and BSE-C was not found in it. These results suggest that the Norwegian CWD red deer case was infected with a previously unknown CWD type and further investigation is needed to determine the characteristics of this potential new CWD strain.

Transmission of Norwegian reindeer CWD to sheep by intracerebral inoculation results in an unusual phenotype and prion distribution.

Chronic wasting disease (CWD), a prion disease affecting cervids, has been known in North America (NA) since the 1960s and emerged in Norway in 2016. Surveillance and studies have revealed that there are different forms of CWD in Fennoscandia: contagious CWD in Norwegian reindeer and sporadic CWD in moose and red deer. Experimental studies have demonstrated that NA CWD prions can infect various species, but thus far, there have been no reports of natural transmission to non-cervid species. In vitro and laboratory animal studies of the Norwegian CWD strains suggest that these strains are different from the NA strains. In this work, we describe the intracerebral transmission of reindeer CWD to six scrapie-susceptible sheep. Detection methods included immunohistochemistry (IHC), western blot (WB), enzyme-linked immunosorbent assay (ELISA), real-time quaking-induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA). In the brain, grey matter vacuolation was limited, while all sheep exhibited vacuolation of the white matter. IHC and WB conventional detection techniques failed to detect prions; however, positive seeding activity with the RT-QuIC and PMCA amplification techniques was observed in the central nervous system of all but one sheep. Prions were robustly amplified in the lymph nodes of all animals, mainly by RT-QuIC. Additionally, two lymph nodes were positive by WB, and one was positive by ELISA. These findings suggest that sheep can propagate reindeer CWD prions after intracerebral inoculation, resulting in an unusual disease phenotype and prion distribution with a low amount of detectable prions.

Experience of Autologous Immunotherapy for Non-Small Cell Lung Cancer Using Zoledronate-Actived Gammadelta T Cells.

BACKGROUND: Several nations around the world have utilized autologous immune enhancement therapy in the treatment of cancer, with initial positive outcomes. This study describes our experience with autologous gamma delta T cell immunotherapy for the treatment of non-small cell lung cancer patients in Vietnam, a developing nation. METHODS: Five patients with non-small cell lung cancer at stages III - IV were enrolled in the study. Each patient received six infusions of autologous γδT cells, separated by two weeks. Before, during, at the end of treatment, and three and six months after treatment, a comprehensive evaluation of clinical, laboratory, quality of life, and adverse events related to the method was conducted. RESULTS: At the time of culture seeding, the total number of cells ranged from 2.9 to 18.2 x 106, with γδT cells ranging in number from 10.7 to 19.6 x 104. On day 14 of the culture, the number of γδT cells ranged from 3.1 to 8.3 x 108. Regarding the safety of therapy in a total of 30 infusions, two (fever), one (myalgia), and one (joint pain) were graded as 1 by CTCAE criteria. After the course, no toxicity was observed in the hematopoietic system, kidney function, or liver function. Evaluation of the patient's response in accordance with the RECIST 1.1 criteria: 20% of patients (one patient) had partial response disease, and 80% of patients (four patients) had stable disease at the end of treatment. During the follow-up period of the study, three patients were still alive, and the disease remained stable. The patient's quality of life improved after treatment in most functional measures (activity, cognitive, and social), but physical and emotional scores decreased slightly. Two patients' fatigue symptoms increased, but after six months of treatment, the average value dropped from 25.3 to 8.3. Dyspnea symptoms decreased gradually from 33.3 at the start of treatment to 8.3 six months later. CONCLUSIONS: The initial results we obtained regarding the efficacy and safety of autologous γδT cell immunotherapy for patients with non-small cell lung cancer are extremely encouraging and comparable to those of previous studies.

The prevalence of Pfk13 polymorphism in malaria patients treated with artemisinin-based therapy: a systematic review and meta-analysis.

Artemisinin (ART) combination therapy is the main treatment for malaria. Pfk13 mutations (or K13 mutations, Kelch 13) are associated with ART resistance. This study aims to conduct a systematic review and meta-analysis of the prevalence of K13 mutations with ART resistance in malaria-endemic countries. An electronic search of studies in 2018 and a manual search in 2020 were performed to identify relevant studies. The risk of bias was assessed using the National Institutes of Health (NIH) quality assessment tool for observational cohort and cross-sectional studies. Data analysis was performed using R 4.1.0. Heterogeneity was estimated using the statistic I2 and Cochran Q test. A total of 170 studies were included in our review. Of these, 55 studies investigated the prevalence of K13 mutations in Southeast Asia. The meta-analysis showed that Southeast Asia had the highest prevalence of K13 mutations, whereas Africa, South America, Oceania, and other Asian countries outside Southeast Asia had a low prevalence of K13 mutations. The C580Y mutation was the most common in Southeast Asia with 35.5% (95%CI: 25.4-46.4%), whereas the dominant mutation in Africa was K189T (22.8%, 95%CI: 7.6-43.2%). This study revealed the emergence of ART resistance associated with K13 mutations in Southeast Asia. The diversity of each type of K13 mutation in other regions was also reported.

Overview of Dermatological Manifestations Associated with the COVID-19 Infection.

COVID-19 is an infectious disease caused by SARS-CoV-2 that is characterized by respiratory symptoms, fever, and chills.[1] While these systemic symptoms are widely known and well understood, there have also been reports of dermatological manifestations in patients with COVID-19. These manifestations include chilblain-like lesions, maculopapular lesions, urticarial lesions, necrosis, and other varicella-like exanthems.[2] The pathogenesis of these lesions are not well understood, but the procoagulant and pro-inflammatory state induced by COVID-19 infections may be contributing to varied cutaneous manifestations.[3] Drug interactions and concurrent hypersensitivity reactions have also been postulated.[4] This review aims to compile and analyze various retrospective studies and case reports to summarize the clinical presentation of dermatological lesions associated with COVID-19 infections and suggest further areas of research.

The eukaryotic-like characteristics of small GTPase, roadblock and TRAPPC3 proteins from Asgard archaea.

Membrane-enclosed organelles are defining features of eukaryotes in distinguishing these organisms from prokaryotes. Specification of distinct membranes is critical to assemble and maintain discrete compartments. Small GTPases and their regulators are the signaling molecules that drive membrane-modifying machineries to the desired location. These signaling molecules include Rab and Rag GTPases, roadblock and longin domain proteins, and TRAPPC3-like proteins. Here, we take a structural approach to assess the relatedness of these eukaryotic-like proteins in Asgard archaea, the closest known prokaryotic relatives to eukaryotes. We find that the Asgard archaea GTPase core domains closely resemble eukaryotic Rabs and Rags. Asgard archaea roadblock, longin and TRAPPC3 domain-containing proteins form dimers similar to those found in the eukaryotic TRAPP and Ragulator complexes. We conclude that the emergence of these protein architectures predated eukaryogenesis, however further adaptations occurred in proto-eukaryotes to allow these proteins to regulate distinct internal membranes.

Impact of Different Red Blood Cell Storage Solutions and Conditions on Cell Function and Viability: A Systematic Review.

Red blood cell (RBC) storage solutions have evolved significantly over the past decades to optimize the preservation of cell viability and functionality during hypothermic storage. This comprehensive review provides an in-depth analysis of the effects of various storage solutions and conditions on critical RBC parameters during refrigerated preservation. A wide range of solutions, from basic formulations such as phosphate-buffered saline (PBS), to advanced additive solutions (ASs), like AS-7 and phosphate, adenine, glucose, guanosine, saline, and mannitol (PAGGSM), are systematically compared in terms of their ability to maintain key indicators of RBC integrity, including adenosine triphosphate (ATP) levels, morphology, and hemolysis. Optimal RBC storage requires a delicate balance of pH buffering, metabolic support, oxidative damage prevention, and osmotic regulation. While the latest alkaline solutions enable up to 8 weeks of storage, some degree of metabolic and morphological deterioration remains inevitable. The impacts of critical storage conditions, such as the holding temperature, oxygenation, anticoagulants, irradiation, and processing methods, on the accumulation of storage lesions are also thoroughly investigated. Personalized RBC storage solutions, tailored to individual donor characteristics, represent a promising avenue for minimizing storage lesions and enhancing transfusion outcomes. Further research integrating omics profiling with customized preservation media is necessary to maximize post-transfusion RBC survival and functions. The continued optimization of RBC storage practices will not only enhance transfusion efficacy but also enable blood banking to better meet evolving clinical needs.

RNA therapeutics for respiratory diseases.

It has become increasingly common to utilize RNA treatment to treat respiratory illnesses. Experimental research on both people and animals has advanced quickly since the turn of the twenty-first century in an effort to discover a treatment for respiratory ailments that could not be accomplished with earlier techniques, specifically in treating prevalent respiratory diseases such as lung cancer, chronic obstructive pulmonary disease (COPD), respiratory infections caused by viruses, and asthma. This chapter has provided a comprehensive overview of the scientific evidence in applying RNA therapy to treat respiratory diseases. The chapter describes the development of this therapy for respiratory diseases. At the same time, the types of RNA therapy for respiratory diseases have been highlighted. In addition, the mechanism of this therapy for respiratory diseases has also been covered. These insights are indispensable if this therapy is to be developed widely.

Modeling biases from low-pass genome sequencing to enable accurate population genetic inferences.

Low-pass genome sequencing is cost-effective and enables analysis of large cohorts. However, it introduces biases by reducing heterozygous genotypes and low-frequency alleles, impacting subsequent analyses such as demographic history inference. We developed a probabilistic model of low-pass biases from the Genome Analysis Toolkit (GATK) multi-sample calling pipeline, and we implemented it in the population genomic inference software dadi. We evaluated the model using simulated low-pass datasets and found that it alleviated low-pass biases in inferred demographic parameters. We further validated the model by downsampling 1000 Genomes Project data, demonstrating its effectiveness on real data. Our model is widely applicable and substantially improves model-based inferences from low-pass population genomic data.

Computationally efficient demographic history inference from allele frequencies with supervised machine learning.

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we developed donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future input data AFS. We demonstrated that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

Retention of prions in the polychaete Hediste diversicolor and black soldier fly, Hermetia illucens, larvae after short-term experimental immersion and feeding with brain homogenate from scrapie infected sheep.

Finding alternative protein and lipid sources for aquafeeds is crucial for the sustainable growth of fed aquaculture. Upcycling industrial side streams and byproducts using extractive species can reduce waste and help reduce the sector's dependence on fish meal and fish oils. Polychaete worms (Hediste diversicolor) and black soldier fly (Hermetia illucens) larvae (BSFL) are promising candidates for converting waste materials into valuable protein and lipid sources. However, further research and evaluations are needed to ensure the safety and regulatory compliance of these alternative feed sources, especially regarding prions spreading potential in the unlikely case that prions would be introduced in the value chain via feedstocks. In the present investigation, BSFL and juvenile polychaetes that had received a massive dose of scrapie prions through immersion and oral inoculation were found to harbour detectable prions using an ultrasensitive amplification method known as PMCA. This observation suggests that both H. diversicolor and BSFL have the potential to serve as mechanical vectors for prions diseases. However, it is important to note that insects, lacking the prion protein gene, are incapable of propagating prions. Therefore, the quantity of prions present in the larvae will inevitably be lower than the amount of prions they encountered. This is the first study to report on the fate of prions through ingestion by these marine and terrestrial invertebrate species.

Disaster after disaster: the outbreak of infectious diseases in Pakistan in the wake of 2022 floods.

In June 2022, Pakistan witnessed catastrophic floods, affecting millions of people. The ensuing epidemics of cholera, cryptosporidiosis, rotavirus infections, generalized diarrhoea, typhoid and paratyphoid fevers, as well as the frequency of vector-borne diseases including malaria and dengue fever, are studied in this investigation. It also explores the latest outbreak of poliomyelitis and the frequency of respiratory diseases such COVID-19, diphtheria, and tuberculosis, as well as how floods have contributed to skin and eye problems. The report also describes the obstacles governments must overcome in order to manage these health emergencies and offers possible solutions for reducing the effects of ongoing and anticipated epidemics. This flood emphasizes the pressing need for international action and acts as an alarming indicator of the significant impact of climate change. It emphasizes how crucial it is to have effective flood response and preparation strategies in developing nations that are vulnerable to natural disasters.

Navigating critical barriers for green bond markets using A fuzzy multi-criteria decision-making model: Case study in Vietnam.

As potent financial instruments channeling capital into climate and environmental projects, green bonds' issuance is frustrated by myriad obstacles. Therefore, this study aims to explore the critical barriers to green bond issuance in Vietnam. This study elucidates the salient barriers by utilizing an integrated multi-criteria decision-making methodology synthesizing the Fuzzy set theory-based Delphi, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and DEMATEL-based Analytic Network Process (DANP). An extensive literature review of green bonds delineates five key dimensions related to impediments: Policy, Market, Financial, Capacity, and Awareness, with 38 discrete indicators representing potential obstacles. Firstly, employing a Fuzzy Delphi survey of 16 experts, 32 indicators are distilled for further analysis. Then, Fuzzy DEMATEL modeling evaluates the interrelationships and interactions among barriers. Finally, DANP is applied to obtain the relative importance of key barriers. Results unveil the five most formidable barriers as a weak regulatory framework and infrastructure (PO1), Limited availability of green bond issuance guidelines and templates (PO2), Insufficient incentives or tax benefits for green bond issuers (PO3), Limited coordination and alignment with international green bond standards (PO5), Lack of investor confidence in the quality and credibility of green projects (AW4), and Capacity constraints among issuers, particularly in smaller organisations or government agencies (CA1). The research contributes to the broader literature on green finance and offers valuable insights for promoting sustainable finance practices in Vietnam and other developing economies.

Visual narratives in nursing education: A generative artificial intelligence approach.

AIM: The aim of this paper is to investigate the incorporation of visual narratives, such as comics and graphics, into nursing education using Generative Artificial Intelligence (GAI) models like DALL-E. BACKGROUND: Visual narratives serve as a powerful method for communicating intricate concepts in nursing education. Despite their advantages, challenges in creating effective educational comics persist due to the need for expertise in graphic design and the associated time and resource constraints. DESIGN: This study examines existing literature that highlights the efficacy of visual narratives in education and demonstrates the potential of GAI models, specifically DALL-E, in creating visual narratives for nursing education. METHODS: We analyze the potential of GAI models, specifically DALL-E, to create visual narratives for educational purposes. This was demonstrated through illustrative examples addressing sensitive topics, illustrating research methodology and designing recruitment posters for clinical trials. Additionally, we discussed the necessity of reviewing and editing the text generated by DALL-E to ensure its accuracy and relevance in educational contexts. The method also considered legal concerns related to copyright and ownership of the generated content, highlighting the evolving legal landscape in this domain. RESULTS: The study found that GAI, specifically DALL-E, has significant potential to bridge the gap in creating visual narratives for nursing education. While offering cost-effectiveness and accessibility, GAI tools require careful consideration of challenges such as text-related errors, misinterpretation of user prompts and legal concerns. CONCLUSIONS: GAI models like DALL-E offer promising solutions for enhancing visual storytelling in nursing education. However, their effective integration requires a collaborative approach, where educators engage with these tools as co-pilots, leveraging their capabilities while mitigating potential drawbacks. By doing so, educators can harness the full potential of GAI to enrich the educational experience for learners through compelling visual narratives.

Quantifying the Dosimetric Impact of Proton Range Uncertainties on RBE-Weighted Dose Distributions in Intensity-Modulated Proton Therapy for Bilateral Head and Neck Cancer.

BACKGROUND: In current clinical practice, intensity-modulated proton therapy (IMPT) head and neck cancer (HNC) plans are generated using a constant relative biological effectiveness (cRBE) of 1.1. The primary goal of this study was to explore the dosimetric impact of proton range uncertainties on RBE-weighted dose (RWD) distributions using a variable RBE (vRBE) model in the context of bilateral HNC IMPT plans. METHODS: The current study included the computed tomography (CT) datasets of ten bilateral HNC patients who had undergone photon therapy. Each patient's plan was generated using three IMPT beams to deliver doses to the CTV_High and CTV_Low for doses of 70 Gy(RBE) and 54 Gy(RBE), respectively, in 35 fractions through a simultaneous integrated boost (SIB) technique. Each nominal plan calculated with a cRBE of 1.1 was subjected to the range uncertainties of ±3%. The McNamara vRBE model was used for RWD calculations. For each patient, the differences in dosimetric metrices between the RWD and nominal dose distributions were compared. RESULTS: The constrictor muscles, oral cavity, parotids, larynx, thyroid, and esophagus showed average differences in mean dose (Dmean) values up to 6.91 Gy(RBE), indicating the impact of proton range uncertainties on RWD distributions. Similarly, the brachial plexus, brain, brainstem, spinal cord, and mandible showed varying degrees of the average differences in maximum dose (Dmax) values (2.78-10.75 Gy(RBE)). The Dmean and Dmax to the CTV from RWD distributions were within ±2% of the dosimetric results in nominal plans. CONCLUSION: The consistent trend of higher mean and maximum doses to the OARs with the McNamara vRBE model compared to cRBE model highlighted the need for consideration of proton range uncertainties while evaluating OAR doses in bilateral HNC IMPT plans.

Anti-Staphylococcus aureus potential of compounds from Ganoderma sp.: A comprehensive molecular docking and simulation approaches.

In this study, a series of secondary metabolites from Ganoderma sp. were screened against Staphylococcus aureus protein targets, including as phosphotransacetylase, clumping factor A, and dihydrofolate reductase, using molecular docking simulations. The chemicals that showed the strongest binding energy with the targeted proteins were ganodermanontriol, lucidumol B, ganoderic acid J, ergosterol, ergosterol peroxide, 7-oxoganoderic acid Z, ganoderic acid AM1, ganosinoside A, ganoderic acid D, and 24R-ergosta-7,2E-diene-3ß,5α,6ß-triol. Interestingly, ganosinoside A showed the greatest affinity for the protein clumping factor A, a result validated by molecular dynamic simulation. Additionally, three natural Ganoderma sp. Strains as Ganoderma lingzhi VNKKK1903, Ganoderma lingzhi VNKK1905A2, and Amauroderma subresinosum VNKKK1904 were collected from Kon Ka Kinh National Park in central land of Vietnam and evaluated for their antibacterial activity against Staphylococcus aureus using an agar well diffusion technique. These results suggest that the fungal extracts and secondary metabolites may serve as valuable sources of antibiotics against Staphylococcus aureus. These findings provided an important scientific groundwork for further exploration of the antibacterial mechanisms of compounds derived from Ganoderma sp. in future research.

Evaluating the benefit of virtual surgical planning on bony union rates in head and neck reconstructive surgery.

OBJECTIVES: Virtual surgical planning (VSP) has gained acceptance because of its benefits in obtaining adequate resection, achieving cephalometric accuracy, and reducing operative time. The aim of this study is to compare the rate of union between VSP and free-hand surgery (FHS), identify predictors of non-union and evaluate the difference in operative time. METHODS: Post-operative CT were retrospectively reviewed for 123 patients who underwent maxillary or mandibular reconstruction between 2014 and 2021 using either VSP or FHS. Each apposition was graded as complete, partial or non-union. The rate of union, risk difference and inter-rater reliability were calculated. The difference in operative time was assessed. Predictors of non-union were identified using logistic regression. RESULTS: A total of 326 appositions were graded (VSP n = 150; FHS n = 176). The rates of complete and partial union were higher with VSP than FHS (74.7% vs. 65.3%; 18% vs. 15.9%, respectively, p = 0.01). Non-union was found at a higher rate with FHS than with VSP (18.7% vs. 7.3%). The non-union risk difference was 11.4. FHS, major complications and apposition at the native bone were predictors of non-union (OR 2.9, p = 0.02; OR 3.4, p = 0.01; OR 2.5, p = 0.05, respectively). The mean surgical time was shorter with VSP than with FHS (265.3 vs. 381.5 min, p < 0.001). The inter-rater agreement was high (k = 0.85; ICC = 0.86). CONCLUSION: VSP demonstrated significantly higher bony union rates and shorter operative time. FHS, development of major complications and apposition with native bone correlated with non-union.