miR-758-3p/ILK signaling modulated angiogenesis by regulating VEGFA in wound healing.

Chronic wounds cause physical, psychological and economic damage to patients, while therapeutic choices are limited. ILK was reported to play key roles in both fibrosis and angiogenesis, which are two important factors during wound healing. However, the function of ILK during vascularization in wounds remains unclear. In our study, we found increased ILK expression in chronic wound tissues compared to adjacent tissue, as well as a positive relationship between ILK expression and microvessel density. Moreover, fibroblasts overexpressing ILK showed an enhanced ability to promote HUVEC migration and tube formation, during which PI3K/Akt, downstream of ILK, played key roles and VEGFA was the key cytokine. Considering the important function of ILK in wound healing and the lack of an ILK activator, we investigated microRNAs targeting ILK and found that miR-758-3p could target ILK to regulate its transcription. The inhibition of miR-758-3p increased ILK expression and sequentially upregulated VEGFA and activated angiogenesis in vivo and in vitro. Taken together, these results revealed that ILK played a key role in wound healing by regulating angiogenesis and that activating ILK by inhibiting miR-758-3p was an effective way to promote wound healing. Whether miR-758-3p/ILK signaling can be utilized as a therapeutic target for wound healing requires further investigation.

Engineering improved strategies for spinel cathodes in high-performing zinc-ion batteries.

The development of high-performing cathode materials for aqueous zinc-ion batteries (ZIBs) is highly important for the future large-scale energy storage. Owing to the distinctive framework structure, diversity of valences, and high electrochemical activity, spinel materials have been widely investigated and used for aqueous ZIBs. However, the stubborn issues of low electrical conductivity and sluggish kinetics plague their smooth applications in aqueous ZIBs, which stimulates the development of effective strategies to address these issues. This review highlights the recent advances of spinel-based cathode materials that include the configuration of aqueous ZIBs and corresponding reaction mechanisms. Subsequently, the classifications of spinel materials and their properties are also discussed. Then, the review mainly summarizes the effective strategies for elevating their electrochemical performance, including their morphology and structure design, defect engineering, heteroatom doping, and coupling with a conductive support. In the final section, several sound prospects in this fervent field are also proposed for future research and applications.

Characterization and genomic analysis of a novel Pseudoalteromonas phage PS_L5.

Pseudoalteromonas is a widely distributed bacterial genus that is associated with marine algae. However, there is still limited knowledge about their bacteriophage. In this study, we reported the isolation of a novel lytic bacteriophage that infects Pseudoalteromonas marina. Transmission electron microscopy revealed that PS_L5 had an icosahedral head of 52.6 ± 2 nm and a non-contractile tail with length of 96.5 ± 2 nm. The genome sequence of this phage was 34, 257 bp and had a GC content of 40.75%. Furthermore, this genome contained 61 predicted open reading frames (ORFs), which involved in various functions such as phage structure, packaging, DNA metabolism, host lysis and other additional functions. Additionally, the phylogenetic analysis based on major capsid protein showed that the phage PS_L5 was closely related to five other Pseudoalteromonas phages, namely PHS3, PHS21, AL, SL25 and Pq0 which also possessed the non-contractile long tail. This study provided the fundamental insights into the evolutionary dynamics of Pseudoalteromonas phages and the interaction between phage and host.

Recent progress in structural modification of polymer gel electrolytes for use in solid-state zinc-ion batteries.

Zinc-ion batteries are one of the promising energy storage devices, which have the advantages of environmental friendliness, high safety and low price and are expected to be used in large-scale battery application fields. However, four prominent water-induced adverse reactions, including zinc dendrite formation, zinc corrosion, passivation and the hydrogen evolution reaction in aqueous systems, seriously shorten the cycling life of zinc-ion batteries and greatly hinder their development. Based on this, polymer gel electrolytes have been developed to alleviate these issues due to their unique network structure, which can reduce water activity and suppress water-induced side reactions. Based on the challenges of polymer gel electrolytes, this review systematically summarizes the latest research progress in the use of additives in them and explores new perspectives in response to the existing problems with polymer electrolytes. In order to expand the performance of polymer gel electrolytes in zinc-ion batteries, a range of different types of additives are added via physical/chemical crosslinking, such as organic or inorganic substances, natural plants, etc. In addition, different types of additives and polymerization crosslinking from different angles essentially improve the ionic conductivity of the gel electrolyte, inhibit the growth of zinc dendrites, and reduce hydrogen evolution and oxygen-absorbed corrosion. After these modifications of polymer gel electrolytes, a more stable and superior electrochemical performance of zinc-ion batteries can be obtained, which provides some strategies for solid-state zinc-ion batteries.

Altered Membrane Expression and Function of CD11b Play a Role in the Immunosuppressive Effects of Morphine on Macrophages at the Nanomolar Level.

Morphine, one of the most efficacious analgesics, is effective in severe pain, especially in patients with concomitant painful cancers. The clinical use of morphine may be accompanied by increased immunosuppression, susceptibility to infection and postoperative tumor metastatic recurrence, and the specific mechanisms and clinical strategies to alleviate this suppression remain to be investigated. Expression of CD11b is closely associated with the macrophage phagocytosis of xenobiotic particles, bacteria or tumor cells. Here, we find that morphine at 0.1-10 nM levels inhibited CD11b expression and function on macrophages via a µ-opioid receptor (MOR)-dependent mechanism, thereby reducing macrophage phagocytosis of tumor cells, a process that can be reversed by thymopentin (TP5), a commonly used immune-enhancing adjuvant in clinical practice. By knocking down or overexpressing MOR on macrophages and using naloxone, an antagonist of the MOR receptor, and LA1, a molecule that promotes macrophage CD11b activation, we suggest that morphine may regulate macrophage phagocytosis by inhibiting the surface expression and function of macrophage CD11b through the membrane expression and activation of MOR. The CD47/SIRPα axis, which is engaged in macrophage-tumor immune escape, was not significantly affected by morphine. Notably, TP5, when combined with morphine, reversed the inhibition of macrophage phagocytosis by morphine through mechanisms that promote membrane expression of CD11b and modulate its downstream signaling (e.g., NOS2, IFNG, IL1B and TNFA, as well as AGR1, PDGFB, IL6, STAT3, and MYC). Thus, altered membrane expression and function of CD11b may mediate the inhibition of macrophage phagocytosis by therapeutic doses of morphine, and the reversal of this process by TP5 may provide an effective palliative option for clinical immunosuppression by morphine.

A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity.

BACKGROUND: Acute promyelocytic leukemia (APL) is a subtype of acute myeloid leukemia (AML) characterized by its rapidly progressive and fatal clinical course if untreated, although it is curable if treated in a timely manner. Promptly screening patients who have results that are suspicious for APL is vital to overcome early death. METHODS: The authors developed an innovative framework consisting of ResNet-18, a convolutional neural network architecture, with the objective of quantitatively mapping a complete blood count (CBC) scattergram to quickly and robustly indicate a probable susceptibility to APL. Three hundred and twenty scattergrams of the white blood cell differential channel from 51 patients with APL, 510 scattergrams from 105 patients who had non-APL AML, and 320 scattergrams from 320 healthy controls were randomly stratified at a ratio of 4:1 and split into training and testing data sets to accomplish five-fold cross-validation. RESULTS: Both the area under the curve and the average precision of >0.99 were achieved in each fold. Three hundred four of the 320 APL scattergrams (95%) were correctly flagged by the model, which outcompeted the CBC review rules recommended by the International Society of Laboratory Hematology (all p < .001). External validation based on an independent testing data set that included 56 scattergrams from 31 patients with APL, 56 scattergrams from 55 patients with non-APL AML, and 64 scattergrams from 64 healthy controls also confirmed the sensitivity and specificity of the framework. CONCLUSIONS: To the authors' knowledge, their convolutional neural network-based framework is the first to use scattergram output from routine CBC analysis to map suspicious APL early with outstanding sensitivity, specificity, and precision. The authors also describe a new CBC workflow incorporating this framework upstream of the morphologic review, which would provide the earliest flag for APL. PLAIN LANGUAGE SUMMARY: The authors propose an innovative way to visualize complete blood counts (CBCs) by mapping the difference in white blood cell counts using automated CBC analysis to identify potential acute promyelocytic leukemia (APL) using a convolutional neural network (CNN), which can eliminate the potential pitfalls of manual observation. Analyses of an unprecedented, realistic data set validated that the quantitative relationship between the CBC scattergram and an APL abnormality is highly consistent. This is the first study to date focusing on screening for APL using scattergrams of the difference in white blood cell counts from routine CBC tests and has significant clinical relevance. The authors recommend using this method even before analyzing cell images, which could provide the earliest way to screen for APL in a sensitive and accurate way.

The relationship of publication language, study population, risk of bias, and treatment effects in acupuncture related systematic reviews: a meta-epidemiologic study.

BACKGROUND: There are debates in acupuncture related systematic reviews and meta-analyses on whether searching Chinese databases to get more Chinese-language studies may increase the risk of bias and overestimate the effect size, and whether the treatment effects of acupuncture differ between Chinese and non-Chinese populations. METHODS: In this meta-epidemiological study, we searched the Cochrane library from its inception until December 2021, and identified systematic reviews and meta-analyses with acupuncture as one of the interventions. Paired reviewers independently screened the reviews and extracted the information. We repeated the meta-analysis of the selected outcomes to separately pool the results of Chinese- and non-Chinese-language acupuncture studies and presented the pooled estimates as odds ratios (OR) with 95% confidence interval (CI). We calculated the Ratio of ORs (ROR) by dividing the OR of the Chinese-language trials by the OR of the non-Chinese-language trials, and the ROR by dividing the OR of trials addressing Chinese population by the OR of trials addressing non-Chinese population. We explored whether the impact of a high risk of bias on the effect size differed between studies published in Chinese- and in non-Chinese-language, and whether the treatment effects of acupuncture differed between Chinese and non-Chinese population. RESULTS: We identified 84 Cochrane acupuncture reviews involving 33 Cochrane groups, of which 31 reviews (37%) searched Chinese databases. Searching versus not searching Chinese databases significantly increased the contribution of Chinese-language literature both to the total number of included trials (54% vs. 15%) and the sample size (40% vs. 15%). When compared with non-Chinese-language trials, Chinese-language trials were associated with a larger effect size (pooled ROR 0.51, 95% CI 0.29 to 0.91). We also observed a higher risk of bias in Chinese-language trials in blinding of participants and personnel (97% vs. 51%) and blinding of outcome assessment (93% vs. 47%). The higher risk of bias was associated with a larger effect estimate in both Chinese-language (allocation concealment: high/unclear risk vs. low risk, ROR 0.43, 95% CI 0.21 to 0.87) and non-Chinese-language studies (blinding of participants and personnel: high/unclear risk vs. low risk, ROR 0.41, 95% CI 0.23 to 0.74). However, we found no evidence that the higher risk of bias would increase the effect size of acupuncture in Chinese-language studies more often than in non-Chinese-language studies (the confidence intervals of all ROR in the high-risk group included 1, Table 3). We further found acupuncture appeared to be more effective in Chinese than in non-Chinese population (Table 4). CONCLUSIONS: The findings of this study suggest the higher risk of bias may lead to an overestimation of the treatment effects of acupuncture but would not increase the treatment effects in Chinese-language studies more often than in other language studies. The difference in treatment effects of acupuncture was probably associated with differences in population characteristics. TRIAL REGISTRATION: We registered our protocol on the Open Science Framework (OSF) ( https://doi.org/10.17605/OSF.IO/PZ6XR ).

Co-doped amorphous NiMoS4 modified with rGO for high-rate performance and long-cycling stability of hybrid supercapacitors.

The electrochemical performance of hybrid capacitors is seriously affected by the slow charging and discharging of the bulk phase. Here, Co-doped amorphous NiMoS4 modified with reduced graphene oxide (rGO) was prepared by a simple one-step hydrothermal method, and the obtained Co-doped NiMoS4/rGO nanocomposite (Ni1-xCoxMoS4/rGO) exhibits a high specific surface area, realizing the redox reaction from the bulk to the surface. Owing to the doping of Co with abundant redox active sites and the support of rGO sheets with high conductivity and a stable structure, the Ni1-xCoxMoS4/rGO anode assembled with an oxidized needle coke (NCO) cathode shows an excellent energy density of 28.9 W h kg-1 at a power density of 968.3 W kg-1. In addition, the hybrid supercapacitor displays a superior cycling performance with a capacity retention of 92.4% after 10 000 cycles. The construction of the Co-doped NiMoS4/rGO nanocomposite provides an effective strategy to boost the activity and stability of amorphous NiMoS4 for high-performance hybrid supercapacitors.

An End-to-End Pipeline for Early Diagnosis of Acute Promyelocytic Leukemia Based on a Compact CNN Model.

Timely microscopy screening of peripheral blood smears is essential for the diagnosis of acute promyelocytic leukemia (APL) due to the occurrence of early death (ED) before or during the initial therapy. Screening manually is time-consuming and tedious, and may lead to missed diagnosis or misdiagnosis because of subjective bias. To address these problems, we develop a three-step pipeline to help in the early diagnosis of APL from peripheral blood smears. The entire pipeline consists of leukocytes focusing, cell classification and diagnostic opinions. As the key component of the pipeline, a compact classification model based on attention embedded convolutional neural network blocks is proposed to distinguish promyelocytes from normal leukocytes. The compact classification model is validated on both the combination of two public datasets, APL-Cytomorphology_LMU and APL-Cytomorphology_JHH, as well as the clinical dataset, to yield a precision of 96.53% and 99.20%, respectively. The results indicate that our model outperforms the other evaluated popular classification models owing to its better accuracy and smaller size. Furthermore, the entire pipeline is validated on realistic patient data. The proposed method promises to act as an assistant tool for APL diagnosis.

Application of Natural Flavonoids to Impart Antioxidant and Antibacterial Activities to Polyamide Fiber for Health Care Applications.

Polyamide fiber has the requirements for antioxidant and antibacterial properties when applied to produce functional textiles for heath care purposes. In this work, three natural flavonoids (baicalin, quercetin, and rutin) were used to simultaneously impart antioxidant and antibacterial functions to polyamide fiber using an adsorption technology. The relations of the chemical structures of flavonoids with their adsorption capability, adsorption mechanisms, and antioxidant and antibacterial activities were discussed. The Langmuir-Nernst adsorption model fitted the adsorption isotherms of the three flavonoids well. The adsorption kinetics of the three flavonoids conformed to the pseudo second-order kinetic model. Quercetin exhibited the highest affinity and adsorption capability, and imparted the highest antioxidant and antibacterial activities to polyamide fiber; and moreover, its antioxidant and antibacterial functions had good washing durability. This study demonstrates that the treatment using natural flavonoids is an effective way to exhance the health care functions of polyamide fiber.

Adsorption, Antibacterial and Antioxidant Properties of Tannic Acid on Silk Fiber.

Natural bioactive compounds have received increasing attention in the functional modification of textiles. In this work, tannic acid was used to impart antibacterial and antioxidant functions to silk using an adsorption technique, and the adsorption properties of tannic acid on silk were studied. The adsorption quantity of tannic acid on silk increased with decreasing pH in the range of 3-7. The rates of the uptake of tannic acid by silk were well correlated to the pseudo-second-order kinetic model, and the calculated activation energy of adsorption was 93.49 kJ/mol. The equilibrium adsorption isotherms followed the Langmuir model. The adsorption rate and isotherm studies demonstrated that the chemical adsorption of tannic acid on silk occurred through the ion-ion interaction between tannic acid and silk. Tannic acid displayed good building-up properties on silk. The silk fabric treated with 0.5% tannic acid (relative to fabric weight) exhibited excellent and durable antibacterial properties. Moreover, the silk fabrics treated with 2% and 5% tannic acid had good and durable antioxidant properties. The treatment by tannic acid had less impact on the whiteness of the silk fabric. In summary, tannic acid can be used as a functional agent for preparing healthy and hygienic silk materials.

Fourier ptychographic microscopy reconstruction with multiscale deep residual network.

Fourier ptychographic microscopy (FPM) is a newly developed microscopic technique for large field of view, high-resolution and quantitative phase imaging by combining the techniques from ptychographic imaging, aperture synthesizing and phase retrieval. In FPM, an LED array is utilized to illuminate the specimen from different angles and the corresponding intensity images are synthesized to reconstruct a high-resolution complex field. As a flexible and low-cost approach to achieve high-resolution, wide-field and quantitative phase imaging, FPM is of enormous potential in biomedical applications such as hematology and pathology. Conventionally, the FPM reconstruction problem is solved by using a phase retrieval method, termed Alternate Projection. By iteratively updating the Fourier spectrum with low-resolution-intensity images, the result converges to a high-resolution complex field. Here we propose a new FPM reconstruction framework with deep learning methods and design a multiscale, deep residual neural network for FPM reconstruction. We employ the widely used open-source deep learning library PyTorch to train and test our model and carefully choose the hyperparameters of our model. To train and analyze our model, we build a large-scale simulation dataset with an FPM imaging model and an actual dataset captured with an FPM system. The simulation dataset and actual dataset are separated as training datasets and test datasets, respectively. Our model is trained with the simulation training dataset and fine tuned with the fine-tune dataset, which contains actual training data. Both our model and the conventional method are tested on the simulation test dataset and the actual test dataset to evaluate the performances. We also show the reconstruction result of another neural network-based method for comparison. The experiments demonstrate that our model achieves better reconstruction results and consumes much less time than conventional methods. The results also point out that our model is more robust under system aberrations such as noise and blurring (fewer intensity images) compared with conventional methods.