Amorphous calcium phosphate nanoparticles using adenosine triphosphate as an organic phosphorus source for promoting tendon-bone healing.

BACKGROUND: Rotator cuff tear (RCT) is a common problem of the musculoskeletal system. With the advantage of promoting bone formation, calcium phosphate materials have been widely used to augment tendon-bone healing. However, only enhancing bone regeneration may be not enough for improving tendon-bone healing. Angiogenesis is another fundamental factor required for tendon-bone healing. Therefore, it's necessary to develop a convenient and reliable method to promote osteogenesis and angiogenesis simultaneously, thereby effectively promoting tendon-bone healing. METHODS: The amorphous calcium phosphate (ACP) nanoparticles with dual biological activities of osteogenesis and angiogenesis were prepared by a simple low-temperature aqueous solution method using adenosine triphosphate (ATP) as an organic phosphorus source. The activities of osteogenesis and angiogenesis and the effect on the tendon-bone healing of ACP nanoparticles were tested in vitro and in a rat model of acute RCT. RESULTS: The ACP nanoparticles with a diameter of tens of nanometers were rich in bioactive adenosine. In vitro, we confirmed that ACP nanoparticles could enhance osteogenesis and angiogenesis. In vivo, radiological and histological evaluations demonstrated that ACP nanoparticles could enhance bone and blood vessels formation at the tendon-bone junction. Biomechanical testing showed that ACP nanoparticles improved the biomechanical strength of the tendon-bone junction and ultimately promoted tendon-bone healing of rotator cuff. CONCLUSIONS: We successfully confirmed that ACP nanoparticles could promote tendon-bone healing. ACP nanoparticles are a promising biological nanomaterial in augmenting tendon-bone healing.

Hyperparameter optimization: Classics, acceleration, online, multi-objective, and tools.

Hyperparameter optimization (HPO) has been well-developed and evolved into a well-established research topic over the decades. With the success and wide application of deep learning, HPO has garnered increased attention, particularly within the realm of machine learning model training and inference. The primary objective is to mitigate the challenges associated with manual hyperparameter tuning, which can be ad-hoc, reliant on human expertise, and consequently hinders reproducibility while inflating deployment costs. Recognizing the growing significance of HPO, this paper surveyed classical HPO methods, approaches for accelerating the optimization process, HPO in an online setting (dynamic algorithm configuration, DAC), and when there is more than one objective to optimize (multi-objective HPO). Acceleration strategies were categorized into multi-fidelity, bandit-based, and early stopping; DAC algorithms encompassed gradient-based, population-based, and reinforcement learning-based methods; multi-objective HPO can be approached via scalarization, metaheuristics, and model-based algorithms tailored for multi-objective situation. A tabulated overview of popular frameworks and tools for HPO was provided, catering to the interests of practitioners.

Allele-specific DNA methylation and gene expression during shoot organogenesis in tissue culture of hybrid poplar.

Plant tissue regeneration is critical for genetic transformation and genome editing techniques. During the regeneration process, changes in epigenetic modifications accompany the cell fate transition. However, how allele-specific DNA methylation in two haplotypes contributes to the transcriptional dynamics during regeneration remains elusive. Here we applied an inter-species hybrid poplar (Populus alba × P. glandulosa cv. 84 K) as a system to characterize the DNA methylation landscape during de novo shoot organogenesis at allele level. Both direct and indirect shoot organogenesis showed a reduction in genome-wide DNA methylation. At gene level, non-expressed genes were hypermethylated in comparison with expressed genes. Among the genes exhibiting significant correlations between levels of DNA methylation and gene expression, the expression patterns of 75% of genes were negatively correlated with DNA methylation in the CG context, whereas the correlation patterns in the CHH context were the reverse. The allele-biased DNA methylation was consistent during shoot organogenesis, with fewer than one-thousandth of allele-specific methylation regions shifted. Analysis of allele-specific expression revealed that there were only 1909 genes showing phase-dependent allele-biased expression in the regeneration process, among which the allele pairs with greater differences in transcription factor binding sites at promoter regions exhibited greater differences in allele expression. Our results indicated a relatively independent transcriptional regulation in two subgenomes during shoot organogenesis, which was contributed by cis-acting genomic and epigenomic variations.

Case report: Apatinib combined with neoadjuvant therapy for primary squamous cell carcinoma of the breast: a case report.

Primary squamous cell carcinoma of the breast is a rare subtype of carcinoma of chemosis for which there is no effective chemotherapy regimen. Breast squamous cell carcinoma is usually "triple negative", with poor chemotherapy effects and poor prognosis. Here, we report a successful case of primary breast squamous cell carcinoma treated with apatinib. The patient was treated with 2 cycles of apatinib. The efficacy was evaluated as partial remission, and a sublesion of approximately 4 cm fell off.

Mutual Information Scaling for Tensor Network Machine Learning.

Tensor networks have emerged as promising tools for machine learning, inspired by their widespread use as variational ansatze in quantum many-body physics. It is well known that the success of a given tensor network ansatz depends in part on how well it can reproduce the underlying entanglement structure of the target state, with different network designs favoring different scaling patterns. We demonstrate here how a related correlation analysis can be applied to tensor network machine learning, and explore whether classical data possess correlation scaling patterns similar to those found in quantum states which might indicate the best network to use for a given dataset. We utilize mutual information as measure of correlations in classical data, and show that it can serve as a lower-bound on the entanglement needed for a probabilistic tensor network classifier. We then develop a logistic regression algorithm to estimate the mutual information between bipartitions of data features, and verify its accuracy on a set of Gaussian distributions designed to mimic different correlation patterns. Using this algorithm, we characterize the scaling patterns in the MNIST and Tiny Images datasets, and find clear evidence of boundary-law scaling in the latter. This quantum-inspired classical analysis offers insight into the design of tensor networks which are best suited for specific learning tasks.

Acute thrombocytopenia induced by trastuzumab due to complement reaction: A case report.

Background: The usual treatment option for HER2 breast cancer is targeted therapy with trastuzumab. The common adverse effects of trastuzumab treatment are thrombocytopenia, however, acute thrombocytopenia is rare and its mechanism is still largely unknown. Case presentation: We reported a patient who presented with acute thrombocytopenia on two consecutive occasions, and the predisposing factor was identified on the second occasion because of trastuzumab-only treatment. Routine blood results showed a dramatic increase in white blood cell count and neutrophil count after both trastuzumab treatments. Moreover, the complement reaction results suggested that the dramatic thrombocytopenia was probably due to platelet destruction after complement activation. Conclusion: This case suggests that it would be useful to perform a platelet complement reaction test before trastuzumab treatment in patients with HER2 breast cancer.

High-performance degradable films of poly(lactic acid)/thermochromic microcapsule composites with thermochromic and energy storage functions via blown film process.

In this study, the polylactic acid (PLA)/reversible thermochromic microcapsule (TCM) packaging application film was successfully synthesized by the blown film process. White mineral oil (WMO) was used as a dispersant to prepare PLA/TCM extruded materials with different mass fractions, in which the mass fraction of TCM was up to 20 wt% and the structural, thermal, mechanical, barrier, thermochromic, and heat storage-release properties were evaluated. It was found that WMO had a plasticizing effect, the elongation at break and water vapor transmission rate of the films with the addition of 7 wt% TCM were increased by 533 % and 31.38 %, respectively. For each thermochromic film, significant thermochromic and energy storage release phenomena were observed. For instance, 20 wt% TCM thermochromic film was most effective for prolonging the holding time and suspending the temperature drop rate. In general, thermochromic packaging films with optimized constitutes were successfully synthesized by the blown film process, which provides essential reference significance for the large-scale thermochromic film applications.