Imaging analysis of pneumonic plague infection in Xizang, China: a case report and literature review.

BACKGROUND: Plague is an acute infectious disease caused by the Yersinia pestis. Historically, it has been a major pandemic with high mortality rates, known as the "Black Death" in the 14th century, which resulted in millions of deaths in Europe. With increasing economic prosperity, more and more people are traveling to Xizang. However, this trend also hides significant safety hazards. Currently, there are few recent reports on plague, especially those with imaging manifestations available. In this study, we report the detailed clinical and radiological data of the patient with pneumonic plague in Xizang, China, in 2023. CASE PRESENTATION: We report a case of pneumonic plague in Xizang, which occurred in a herdsman living in an area where dead marmots were found. The patient presented with symptoms such as fever, hemoptysis, dyspnea and coma. Chest computed tomography (CT) scans showed multiple nodules distributed in the central regions of lung lobes, consolidation distributed in secondary pulmonary lobules, and had a gravity-dependent distribution pattern. These imaging findings were consistent with pulmonary hemorrhage and diffuse alveolar damage. Despite emergency treatment, the patient died within 48 h of admission. Through retrospective medical history investigation, laboratory examination and autopsy, the final diagnosis was confirmed as pneumonic plague. CONCLUSION: Pneumonic plague is the most deadly infectious disease, and its pathological features mainly include damage to the alveoli, pulmonary hemorrhage, and pulmonary edema. Corresponding to CT, it manifests as acute and rapidly progressing pneumonia, alveolar damage, and pulmonary hemorrhage. The value of this article lies in the completeness and typicality of the imaging data, vivid hand-drawn illustrations of transmission pathways, and comprehensive literature review, all of which serve to enhance public understanding of plague and play an important warning role.

Efficient and scalable prediction of stochastic reaction-diffusion processes using graph neural networks.

The dynamics of locally interacting particles that are distributed in space give rise to a multitude of complex behaviours. However the simulation of reaction-diffusion processes which model such systems is highly computationally expensive, the cost increasing rapidly with the size of space. Here, we devise a graph neural network based approach that uses cheap Monte Carlo simulations of reaction-diffusion processes in a small space to cast predictions of the dynamics of the same processes in a much larger and complex space, including spaces modelled by networks with heterogeneous topology. By applying the method to two biological examples, we show that it leads to accurate results in a small fraction of the computation time of standard stochastic simulation methods. The scalability and accuracy of the method suggest it is a promising approach for studying reaction-diffusion processes in complex spatial domains such as those modelling biochemical reactions, population evolution and epidemic spreading.

A sono-responsive antibacterial nanosystem co-loaded with metformin and bone morphogenetic protein-2 for mitigation of inflammation and bone loss in experimental peri-implantitis.

Background: Dental implants have become an increasingly popular option for replacing missing teeth, and the prevalence of peri-implantitis has also increased, which is expected to become a public health problem worldwide and cause high economic and health burdens. This scenario highlights the need for new therapeutic options to treat peri-implantitis. Methods: In this study, we proposed a novel sono-responsive antibacterial nanosystem co-loaded with metformin (Met) and bone morphogenetic protein-2 (BMP-2) to promote efficacy in treating peri-implantitis. We introduced the zeolitic imidazolate framework-8 (ZIF-8) as a carrier for hematoporphyrin monomethyl ether (HMME) to enhance the antibacterial effect of sonodynamic antibacterial therapy and tested its reactive oxygen species (ROS) production efficiency and bactericidal effect in vitro. Afterward, HMME-loaded ZIF-8, BMP-2-loaded polylactic acid-glycolic acid (PLGA), and Met were incorporated into gelatin methacryloyl (GelMA) hydrogels to form HMME@ZIF-8/Met/BMP-2@PLGA/GelMA composite hydrogels, and the biocompatibility of which was determined in vitro and in vivo. A bacterial-induced peri-implantitis model in the maxilla of rats was established to detect the effects of the composite hydrogels with adjunctive use of ultrasound on regulating inflammation and promoting bone tissue repair in vivo. Results: The results indicated that HMME@ZIF-8 with ultrasound stimulation demonstrated more better ROS production efficiency and antimicrobial efficacy. The composite hydrogels had good biocompatibility. Ultrasound-assisted application of the composite hydrogels reduced the release of the inflammatory factors IL-6 and TNF-α and reduced bone loss around the implant in rats with bacterial-induced peri-implantitis. Conclusion: Our observations suggest that HMME@ZIF-8 may be a new good sonosensitizer material for sonodynamic antibacterial therapy. The use of HMME@ZIF-8/Met/BMP-2@PLGA/GelMA composite hydrogels in combination with ultrasound can provide a novel option for treating peri-implantitis in the future.

Temporal variations of bacterial and eukaryotic community in coastal waters-implications for aquaculture.

Despite increased attention to the aquaculture environment, there is still a lack of understanding regarding the significance of water quality. To address this knowledge gap, this study utilized high-throughput sequencing of 16S rRNA and 18S rRNA to examine microbial communities (bacteria and eukaryotes) in coastal water over different months through long-term observations. The goal was to explore interaction patterns in the microbial community and identify potential pathogenic bacteria and red tide organisms. The results revealed significant differences in composition, diversity, and richness of bacterial and eukaryotic operational taxonomic units (OTUs) across various months. Principal coordinate analysis (PCoA) demonstrated distinct temporal variations in bacterial and eukaryotic communities, with significant differences (P = 0.001) among four groups: F (January-April), M (May), S (June-September), and T (October-December). Moreover, a strong association was observed between microbial communities and months, with most OTUs showing a distinct temporal preference. The Kruskal-Wallis test (P < 0.05) indicated significant differences in dominant bacterial and eukaryotic taxa among months, with each group exhibiting unique dominant taxa, including potential pathogenic bacteria and red tide organisms. These findings emphasize the importance of monitoring changes in potentially harmful microorganisms in aquaculture. Network analysis highlighted positive correlations between bacteria and eukaryotes, with bacteria playing a key role in network interactions. The key bacterial genera associated with other microorganisms varied significantly (P < 0.05) across different groups. In summary, this study deepens the understanding of aquaculture water quality and offers valuable insights for maintaining healthy aquaculture practices. KEY POINTS: • Bacterial and eukaryotic communities displayed distinct temporal variations. • Different months exhibited unique potential pathogenic bacteria and red tide organisms. • Bacteria are key taxonomic taxa involved in microbial network interactions.

Virtual sketching-based dental anatomy module improves learners' abilities to use computer-aided design to create dental restorations and prostheses.

Dental anatomy education for dental technology students should be developed in alignment with digital dental laboratory practices. We hypothesized that a virtually assisted sketching-based dental anatomy teaching module could improve students' acquisition of skills essential for digital restoration design. The second-year dental technology curriculum included a novel virtual technology-assisted sketching-based module for dental anatomy education. Pre- and post-course assessments evaluated students' skill sets and knowledge bases. Computer-aided design (CAD) scores were analyzed after one year to assess how the skills students developed through this module impacted their subsequent CAD performance. Participants who undertook the dental sketching-based teaching module demonstrated significantly improved theoretical knowledge of dental anatomy, dental aesthetic perception, and spatial reasoning skills. A partial least squares structural equation model indicated that the positive effects of this module on subsequent CAD performance were indirectly mediated by dental aesthetic perception, spatial reasoning, and practice time. A virtually assisted sketching-based dental anatomy teaching module significantly improved students' acquisition of skills and knowledge and positively mediated dental technology students' CAD performance.

Autonomous and policy-induced behavior change during the COVID-19 pandemic: Towards understanding and modeling the interplay of behavioral adaptation.

Changes in human behaviors, such as reductions of physical contacts and the adoption of preventive measures, impact the transmission of infectious diseases considerably. Behavioral adaptations may be the result of individuals aiming to protect themselves or mere responses to public containment measures, or a combination of both. What drives autonomous and policy-induced adaptation, how they are related and change over time is insufficiently understood. Here, we develop a framework for more precise analysis of behavioral adaptation, focusing on confluence, interactions and time variance of autonomous and policy-induced adaptation. We carry out an empirical analysis of Germany during the fall of 2020 and beyond. Subsequently, we discuss how behavioral adaptation processes can be better represented in behavioral-epidemiological models. We find that our framework is useful to understand the interplay of autonomous and policy-induced adaptation as a "moving target". Our empirical analysis suggests that mobility patterns in Germany changed significantly due to both autonomous and policy-induced adaption, with potentially weaker effects over time due to decreasing risk signals, diminishing risk perceptions and an erosion of trust in the government. We find that while a number of simulation and prediction models have made great efforts to represent behavioral adaptation, the interplay of autonomous and policy-induced adaption needs to be better understood to construct convincing counterfactual scenarios for policy analysis. The insights presented here are of interest to modelers and policy makers aiming to understand and account for behaviors during a pandemic response more accurately.

Efficacy of a Virtual 3D Simulation-Based Digital Training Module for Building Dental Technology Students' Long-Term Competency in Removable Partial Denture Design: Prospective Cohort Study.

Background: Removable partial denture (RPD) design is crucial to long-term success in dental treatment, but shortcomings in RPD design training and competency acquisition among dental students have persisted for decades. Digital production is increasing in prevalence in stomatology, and a digital RPD (D-RPD) module, under the framework of the certified Objective Manipulative Skill Examination of Dental Technicians (OMEDT) system reported in our previous work, may improve on existing RPD training models for students. Objective: We aimed to determine the efficacy of a virtual 3D simulation-based progressive digital training module for RPD design compared to traditional training. Methods: We developed a prospective cohort study including dental technology students at the Stomatology College of Chongqing Medical University. Cohort 1 received traditional RPD design training (7 wk). Cohort 2 received D-RPD module training based on text and 2D sketches (7 wk). Cohort 3 received D-RPD module pilot training based on text and 2D sketches (4 wk) and continued to receive training based on 3D virtual casts of real patients (3 wk). RPD design tests based on virtual casts were conducted at 1 month and 1 year after training. We collected RPD design scores and the time spent to perform each assessment. Results: We collected the RPD design scores and the time spent to perform each assessment at 1 month and 1 year after training. The study recruited 109 students, including 58 (53.2%) female and 51 male (56.8%) students. Cohort 1 scored the lowest and cohort 3 scored the highest in both tests (cohorts 1-3 at 1 mo: mean score 65.8, SD 21.5; mean score 81.9, SD 6.88; and mean score 85.3, SD 8.55, respectively; P<.001; cohorts 1-3 at 1 y: mean score 60.3, SD 16.7; mean score 75.5, SD 3.90; and mean score 90.9, SD 4.3, respectively; P<.001). The difference between cohorts in the time spent was not statistically significant at 1 month (cohorts 1-3: mean 2407.8, SD 1370.3 s; mean 1835.0, SD 1329.2 s; and mean 1790.3, SD 1195.5 s, respectively; P=.06) but was statistically significant at 1 year (cohorts 1-3: mean 2049.16, SD 1099.0 s; mean 1857.33, SD 587.39 s; and mean 2524.3, SD 566.37 s, respectively; P<.001). Intracohort comparisons indicated that the differences in scores at 1 month and 1 year were not statistically significant for cohort 1 (95% CI -2.1 to 13.0; P=.16), while cohort 3 obtained significantly higher scores 1 year later (95% CI 2.5-8.7; P=.001), and cohort 2 obtained significantly lower scores 1 year later (95% CI -8.8 to -3.9; P<.001). Conclusions: Cohort 3 obtained the highest score at both time points with retention of competency at 1 year, indicating that progressive D-RPD training including virtual 3D simulation facilitated improved competency in RPD design. The adoption of D-RPD training may benefit learning outcomes.

Dynamic Growth of Macroscopically Structured Supramolecular Hydrogels through Orchestrated Reaction-Diffusion.

Living organisms are capable of dynamically changing their structures for adaptive functions through sophisticated reaction-diffusion processes. Here we show how active supramolecular hydrogels with programmable lifetimes and macroscopic structures can be created by relying on a simple reaction-diffusion strategy. Two hydrogel precursors (poly(acrylic acid) PAA/CaCl2 and Na2 CO3 ) diffuse from different locations and generate amorphous calcium carbonate (ACC) nanoparticles at the diffusional fronts, leading to the formation of hydrogel structures driven by electrostatic interactions between PAA and ACC nanoparticles. Interestingly, the formed hydrogels are capable of autonomously disintegrating over time because of a delayed influx of electrostatic-interaction inhibitors (NaCl). The hydrogel growth process is well explained by a reaction-diffusion model which offers a theoretical means to program the dynamic growth of structured hydrogels. Furthermore, we demonstrate a conceptual access to dynamic information storage in soft materials using the developed reaction-diffusion strategy. This work may serve as a starting point for the development of life-like materials with adaptive structures and functionalities.

Piecewise Developable Modeling Via Implicit Neural Deformation and Feature-Guided Cutting.

We propose a novel and automatic method to model shapes using a small set of discrete developable patches. Central to our approach is using implicit neural shape representation that makes our algorithm independent of tessellation and allows us to obtain the Gaussian curvature of each point analytically. With this powerful representation, we first deform the input shape to be an almost developable shape with clear and sparse salient feature curves. Then, we convert the deformed implicit field to a triangle mesh, which is further cut to disk topology along parts of the sparse feature curves. Finally, we achieve the resulting piecewise developable mesh by alternatingly optimizing discrete developability, enforcing manufacturability constraints, and merging patches. The feasibility and practicability of our method are demonstrated over various shapes. Compared to the state-of-the-art methods, our method achieves a better tradeoff between the number of developable patches and the approximation error.

Assembling the anaerobic gamma-butyrobetaine to TMA metabolic pathway in Escherichia fergusonii and confirming its role in TMA production from dietary L-carnitine in murine models.

IMPORTANCE: The key atherosclerotic TMAO originates from the initial gut microbial conversion of L-carnitine and other dietary compounds into TMA. Developing therapeutic strategies to block gut microbial TMA production needs a detailed understanding of the different production mechanisms and their relative contributions. Recently, we identified a two-step anaerobic pathway for TMA production from L-carnitine through initial conversion by some microbes into the intermediate γBB which is then metabolized by other microbes into TMA. Investigational studies of this pathway, however, are limited by the lack of single microbes harboring the whole pathway. Here, we engineered E. fergusonii strain to harbor the whole two-step pathway and optimized the expression through cloning a specific chaperone from the original host. Inoculating germ-free mice with this recombinant E. fergusonii is enough to raise serum TMAO to pathophysiological levels upon L-carnitine feeding. This engineered microbe will facilitate future studies investigating the contribution of this pathway to cardiovascular disease.

Practical Integer-Constrained Cone Construction for Conformal Parameterizations.

We propose a practical method to construct sparse integer-constrained cone singularities with low distortion constraints for conformal parameterizations. Our solution for this combinatorial problem is a two-stage procedure that first enhances sparsity for generating an initialization and then optimizes to reduce the number of cones and the parameterization distortion. Central to the first stage is a progressive process to determine the combinatorial variables, i.e., numbers, locations, and angles of cones. The second stage iteratively conducts adaptive cone relocations and merges close cones for optimization. We extensively test our method on a data set containing 3885 models, demonstrating practical robustness and performance. Our method achieves fewer cone singularities and lower parameterization distortion than state-of-the-art methods.

Hereditary spastic paraplegia (SPG 48) with deafness and azoospermia: A case report.

Hereditary spastic paraplegias (HSP) are inherited neurodegenerative disorders characterized by progressive paraplegia and spasticity in the lower limbs. SPG48 represents a rare genotype characterized by mutations in AP5Z1, a gene playing a role in intracellular membrane trafficking. This study describes a case of a 53-year-old male patient with SPG48 presenting spastic paraplegia, infertility, hearing impairment, cognitive abnormalities and peripheral neuropathy. The Sanger sequencing revealed a homozygous deletion in the chr 7:4785904-4786677 region causing a premature stop codon in exon 10. The patient's brother was heterozygous for the mutation. The brain magnetic resonance imaging found a mild brain atrophy and white matter lesions. In the analysis of the auditory thresholds, we found a significant hearing decrease in both ears.

Robust Coarse Cage Construction with Small Approximation Errors.

We propose a robust and automatic method to construct manifold cages for 3D triangular meshes. The cage contains hundreds of triangles to tightly enclose the input mesh without self-intersections. To generate such cages, our algorithm consists of two phases: (1) construct manifold cages satisfying the tightness, enclosing, and intersection-free requirements and (2) reduce mesh complexities and approximation errors without violating the enclosing and intersection-free requirements. To theoretically make the first stage have those properties, we combine the conformal tetrahedral meshing and tetrahedral mesh subdivision. The second step is a constrained remeshing process using explicit checks to ensure that the enclosing and intersection-free constraints are always satisfied. Both phases use a hybrid coordinate representation, i.e., rational numbers and floating point numbers, combined with exact arithmetic and floating point filtering techniques to guarantee the robustness of geometric predicates with a favorable speed. We extensively test our method on a data set of over 8500 models, demonstrating robustness and performance. Compared to other state-of-the-art methods, our method possesses much stronger robustness.

A human mobility dataset collected via LBSLab.

Location-Based Services (LBS) have been prosperous owing to technological advancements of smart devices. Analyzing location-based user-generated data is a helpful way to understand human mobility patterns, further fueling applications such as recommender systems and urban computing. This dataset documents user activities of location-based services through LBSLab, a smartphone-based system implemented as a mini-program in the WeChat app. The dataset contains activity data of multiple types including logins, profile viewing, weather checking, and check-ins with location information (latitude and longitude), POI and mood indicated, collected from 467 users over a period of 11 days. We also present some temporal and spatial data analysis and believe the reuse of the data will allow researchers to better understand user behaviors of LBS, human mobility, and also temporal and spatial characteristics of people's moods.

Two distinct gut microbial pathways contribute to meta-organismal production of phenylacetylglutamine with links to cardiovascular disease.

Recent studies show gut microbiota-dependent metabolism of dietary phenylalanine into phenylacetic acid (PAA) is critical in phenylacetylglutamine (PAGln) production, a metabolite linked to atherosclerotic cardiovascular disease (ASCVD). Accordingly, microbial enzymes involved in this transformation are of interest. Using genetic manipulation in selected microbes and monocolonization experiments in gnotobiotic mice, we identify two distinct gut microbial pathways for PAA formation; one is catalyzed by phenylpyruvate:ferredoxin oxidoreductase (PPFOR) and the other by phenylpyruvate decarboxylase (PPDC). PPFOR and PPDC play key roles in gut bacterial PAA production via oxidative and non-oxidative phenylpyruvate decarboxylation, respectively. Metagenomic analyses revealed a significantly higher abundance of both pathways in gut microbiomes of ASCVD patients compared with controls. The present studies show a role for these two divergent microbial catalytic strategies in the meta-organismal production of PAGln. Given the numerous links between PAGln and ASCVD, these findings will assist future efforts to therapeutically target PAGln formation in vivo.

Rational Design, Synthesis, and Anti-Proliferative Evaluation of Novel 4-Aryl-3,4-Dihydro-2H-1,4-Benzoxazines.

A synthetic pathway to a novel 4-aryl-3,4-dihydro-2H-1,4-benzoxazine scaffold was developed and a series of compounds based on the scaffold were synthesised as potential anticancer agents. The 4-aryl-substituted compounds were prepared via Buchwald-Hartwig cross-coupling between substituted bromobenzenes and various 1,4-benzoxazines, which in turn were generated from a cascade hydrogenation and reductive amination one-pot reaction. These analogues exhibited moderate to good potency against various cancer cell lines. Structure-activity relationship analysis indicated that the inclusion of hydroxyl groups on ring A and ring B was beneficial to biological activity, while having a para-amino group on ring C significantly enhanced potency. Molecule 14f displayed the most potent anticancer activity (IC50 = 7.84-16.2 µM against PC-3, NHDF, MDA-MB-231, MIA PaCa-2, and U-87 MG cancer cell lines), indicating its potential as a lead compound for further structural optimisation. All the synthesised compounds were fully characterised with NMR, HMRS, and IR. The novel benzoxazine scaffold described in this study holds promise and deserves further in-depth studies.

Therapists and psychotherapy side effects in China: A machine learning-based study.

Objective: Side effects in the psychotherapy are sometimes unavoidable. Therapists play a significant role in the side effects of psychotherapy, but there have been few quantitative studies on the mechanisms by which therapists contribute to them. Methods: We designed the psychotherapy Side Effects Questionnaire-Therapist Version (PSEQ-T) and released it online through an official WeChat account, where 530 therapists participated in the cross-sectional analysis. The therapists were classified into groups with and without perceptions of clients' side effects. A number of features were selected to distinguish the therapists by category. Six machine learning-based algorithms were selected and trained by our dataset to build classification models. We leveraged the Shapley Additive exPlanations (SHAP) method to quantify the importance of each feature to the therapist categories. Results: Our study demonstrated the following: (1) Of the therapists, 316 perceived clients' side effects in psychotherapy, with a 59.6% incidence of side effects; the most common type was "make the clients or patients feel bad" (49.8%). (2) A Random Forest-based machine-learning classifier offered the best predictive performance to distinguish the therapists with and without perceptions of clients' side effects, with an F1 score of 0.722 and an AUC value of 0.717. (3) "Therapists' psychological activity" was the most relevant feature for distinguishing the therapist category. Conclusions: Our study revealed that the therapist's mastery of the limitations of psychotherapy technology and theory, especially the awareness and construction of their psychological states, was the most critical factor in predicting the therapist's perception of the side effects of psychotherapy.

Survey on computational 3D visual optical art design.

Visual arts refer to art experienced primarily through vision. 3D visual optical art is one of them. Artists use their rich imagination and experience to combine light and objects to give viewers an unforgettable visual experience. However, the design process involves much trial and error; therefore, it is often very time-consuming. This has prompted many researchers to focus on proposing various algorithms to simplify the complicated design processes and help artists quickly realize the arts in their minds. To help computer graphics researchers interested in creating 3D visual optical art, we first classify and review relevant studies, then extract a general framework for solving 3D visual optical art design problems, and finally propose possible directions for future research.