Hydraulic conditions control the abundance of antibiotic resistance genes and their potential host microorganisms in a frequently regulated river-lake system.

Antibiotic resistance genes (ARGs) are a growing problem that is widespread in river-lake ecosystems, where they pose a threat to the aquatic environment's health and public safety. These systems serve as critical nodes in water management, as they facilitate the equitable allocation of water resources through long-term and frequent water diversions. However, hydrological disturbances associated with water-regulation practices can influence the dynamics of their potential host microorganisms and associated resistance genes. Consequently, identifying the key ARGs and their resistance mechanisms in heavily regulated waters is vital for safeguarding human health and that of river-lake ecosystems. In this study, we examined the impact of water-regulation factors on ARGs and their hosts within a river-lake continuum using 16S rRNA and metagenomic sequencing. We found that a significant increase in ARG abundance during regulation periods (p < 0.05), especially in the aquatic environment. Key resistance genes were macB, tetA, evgS, novA, and msbA, with increased efflux pinpointed as their principal resistance mechanism. Network analysis identified Flavobacteriales, Acinetobacter, Pseudomonas, Burkholderiaceae, and Erythrobacter as key potential host microorganisms, which showed increased abundance within the water column during regulation periods (p < 0.05). Flow velocity and water depth both drove the host microorganisms and critical ARGs. Our findings underscore the importance of monitoring and mitigating the antibiotic resistance risk during water transfers in river-lake systems, thereby supporting informed management and conservation strategies.

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

Effect of alkali treatment on enzymatic hydrolysis of p-toluenesulfonic acid pretreated bamboo substrates.

The comprehensive separation and utilization of whole components of lignocellulosic materials has received extensive attention in present research. This study focused on the efficacy of alkali treatment for enzymatic saccharification of cellulose based on p-toluenesulfonic acid (p-TsOH) pretreated bamboo substrate. The results showed that the cellulose to glucose conversion yield was 94.69 % under optimized conditions of 0.4 g NaOH/g, 160 °C and 4 h (soaked), which after only 6 h enzymatic hydrolysis time. Alkali lignin recovery was 88.51 %, with potential for conversion to lignin derivatives. The yield of hemicellulose in the pretreated filtrate was 51.85 % after the 4th cycling reuse of p-TsOH. This work has borrowed the advantages of p-TsOH pretreatment of depolymerized hemicellulose from bamboo, combined with a low-priced weak alkali secondary treatment method, which can be effectively applied to the co-production of lignin, xylooligosaccharide, xylose and glucose, and the whole process is green and economically sustainable.

Development of a QM/MM(ABEEM) method for the deprotonation of neutral and cation radicals in the G-tetrad and GGX(8-oxo-G) tetrad.

The rapid deprotonation of G˙+ in the DNA strand impedes positive charge (hole) transfer, whereas the slow deprotonation rate of G˙+ in the G-tetrad makes it a more suitable carrier for hole conduction. The QM/MM(ABEEM) combined method, which involves the integration of QM and the ABEEM polarizable force field (ABEEM PFF), was developed to investigate the deprotonation of neutral and cation free radicals in the G-tetrad and GGX(8-oxo-G) tetrad (xanthine and 8-oxoguanine dual substituted G-tetrad). By incorporating valence-state electronegativity piecewise functions χ*(r) and implementing charge local conservation conditions, QM/MM(ABEEM) possesses the advantage of accurately simulating charge transfer and polarization effect during deprotonation. The activation energy calculated by the QM method of X˙ is the lowest among other bases in the GGX(8-oxo-G) tetrad, which is supported by the computation of the average electronegativity calculated by ABEEM PFF. By utilizing QM/MM(ABEEM) with a two-way free energy perturbation method, the deprotonation activation energy of X˙ in the GGX(8-oxo-G) tetrad is determined to be 33.0 ± 2.1 kJ mol-1, while that of G˙+ in the G-tetrad is 20.7 ± 0.6 kJ mol-1, consistent with the experimental measurement of 20 ± 1.0 kJ mol-1. These results manifest that X˙ in the GGX(8-oxo-G) tetrad exhibits a slower deprotonation rate than G˙+ in the G-tetrad, suggesting that the GGX(8-oxo-G) tetrad may serve as a more favorable hole transport carrier. Furthermore, the unequal average electronegativities of bases in the GGX(8-oxo-G) tetrad impede the deprotonation rate. This study provides a potential foundation for investigating the microscopic mechanism of DNA electronic devices.

Vision-Guided Attention-Enhanced Network for Predicting Microvascular Invasion in Hepatocellular Carcinoma.

As the most common malignant tumor worldwide, hepatocellular carcinoma (HCC) has a high rate of death and recurrence, and microvascular invasion (MVI) is considered to be an independent risk factor affecting its early recurrence and poor survival rate. Accurate preoperative prediction of MVI is of great significance for the formulation of individualized treatment plans and long-term prognosis assessment for HCC patients. However, as the mechanism of MVI is still unclear, existing studies use deep learning methods to directly train CT or MR images, with limited predictive performance and lack of explanation. We map the pathological "7-point" baseline sampling method used to confirm the diagnosis of MVI onto MR images, propose a vision-guided attention-enhanced network to improve the prediction performance of MVI, and validate the prediction on the corresponding pathological images reliability of the results. Specifically, we design a learnable online class activation map (CAM) to guide the network to focus on high-incidence regions of MVI guided by an extended tumor mask. Further, an attention-enhanced module is proposed to force the network to learn image regions that can explain the MVI results. The generated attention maps capture long-distance dependencies and can be used as spatial priors for MVI to promote the learning of vision-guided module. The experimental results on the constructed multi-center dataset show that the proposed algorithm achieves the state-of-the-art compared to other models.

Contrastive Learning for Preoperative Early Recurrence Prediction of Hepatocellular Carcinoma with Liver CT Image and Tumor Mask.

High early recurrence (ER) rate is the main factor leading to the poor outcome of patients with hepatocellular carcinoma (HCC). Accurate preoperative prediction of ER is thus highly desired for HCC treatment. Many radiomics solutions have been proposed for the preoperative prediction of HCC using CT images based on machine learning and deep learning methods. Nevertheless, most current radiomics approaches extract features only from segmented tumor regions that neglect the liver tissue information which is useful for HCC prognosis. In this work, we propose a deep prediction network based on CT images of full liver combined with tumor mask that provides tumor location information for better feature extraction to predict the ER of HCC. While, due to the complex imaging characteristics of HCC, the image-based ER prediction methods suffer from limited capability. Therefore, on the one hand, we propose to employ supervised contrastive loss to jointly train the deep prediction model with cross-entropy loss to alleviate the problem of intra-class variation and inter-class similarity of HCC. On the other hand, we incorporate the clinical data to further improve the prediction ability of the model. Experiments are extensively conducted to verify the effectiveness of our proposed deep prediction model and the contribution of liver tissue for prognosis assessment of HCC.

Pretreatment of moso bamboo with p-toluenesulfonic acid for the recovery and depolymerization of hemicellulose.

Bamboo and its mechanical processing residues have broad prospects for high value-added utilization. In this research, p-toluenesulfonic acid was used for the pretreatment of bamboo to investigate the effects of extraction and depolymerization of hemicellulose. The response and behavior of changes of cell-wall chemical components were investigated after different solvent concentration, time, and temperature pretreatment. Results indicated that the maximum extraction yield of hemicellulose was 95.16 % with 5 % p-toluenesulfonic acid at 140 °C for 30 min. The depolymerized components of hemicellulose in the filtrate were mainly xylose and xylooligosaccharide, with xylobiose accounting for 30.77 %. The extraction of xylose from the filtrate reached a maximum of 90.16 % with 5 % p-toluenesulfonic acid at 150 °C for 30 min pretreatment. This research provided a potential strategy for the industrial production of xylose and xylooligosaccharide from bamboo and for the future conversion and utilization.

Fungal-induced CaCO3 and SrCO3 precipitation: a potential strategy for bioprotection of concrete.

Biomineralization of CaCO3 by microorganisms is a well-documented process considered applicable to concrete self-healing and metal bioremediation. Urea hydrolysis is the most widely explored and efficient pathway regarding concrete bioprotection. However, the potential of fungi has received relatively little attention compared to bacteria. In this work, we show that Fusarium cerealis, Phoma herbarum and Mucor hiemalis, isolated from concrete, could produce 828.6-941.3 mg L-1 ammonium­nitrogen in liquid media through urea hydrolysis indicating significant urease activity, and could grow in moderate (pH 8.3) or even extremely alkaline (pH 10.6) conditions. After culture in media containing 50 mM CaCl2, at least 48.8% Ca2+ was removed from solution by the selected fungi as calcite. The accumulation of Ca by the biomass was around 83.64-114.21 mg g-1. In addition, all fungi could mediate strontium carbonate formation with F. cerealis processing the highest ability for Sr removal, with ~61% added Sr being removed from solution. Scanning electron microscopy showed carbonate biominerals were encrusted on hyphae or aggregated in fungal pellets. When equivalent concentrations of Ca2+ and Sr2+ were supplemented to the media, CaCO3 with incorporated Sr formed with F. cerealis and M. hiemalis, and Sr(Sr, Ca)(CO3)2 with P. herbarum. Our results demonstrate the potential of fungi in providing carbonate coatings for concrete surfaces and simultaneous immobilization of Sr. We anticipate our work will promote further practical field research on porous cementitious materials protection by fungi and immobilization of potentially toxic metals from metal-laden ingredients, such as fly ash and granulated ground blast furnace slag.

Effect of modified atmosphere packaging on shelf life and bacterial community of roast duck meat.

The purpose of this work was to assess the effect of different packaging methods on the shelf life and bacterial communities of roast duck meat. Samples were packaged under the following five conditions: overwrapped packaging (OWP), 100% N2 (100% N2-MAP), 30% CO2/70% N2 (30% CO2-MAP), 50% CO2/50% N2 (50% CO2-MAP), and 0.4% CO/30% CO2/69.6% N2 (CO-MAP). Physicochemical and microbiological parameters were monitored during 14 days of chilled storage (0-4 °C). Results showed that MAP samples obtained higher and more stable redness, better sensory scores, and lower lipid oxidation, compared with OWP, in which CO-MAP samples had the lowest TBARS values (0.13-0.22 MDA/kg) during storage. Moreover, 30% CO2-MAP, 50% CO2-MAP, and CO-MAP effectively retarded the onset of bacterial spoilage and extended shelf life by 7 days compared with 100% N2-MAP and OWP treatments. Additionally, bacterial succession was significantly affected by the gas composition used in the packages, especially the dominant biota at the end of storage, which played an important role in the spoilage of roast duck meat under specific packaging. On day 14, Pseudoalteromonas spp., Lactobacillus spp., and Pseudomonas spp. became the most predominate genera in OWP, 100% N2-MAP, and 50% CO2-MAP, respectively. Notably, Vibrio spp. was dominant in both 30% CO2-MAP and CO-MAP, indicating 0.4% CO did not exert a further inhibitory effect on this genus. Additionally, the growth inhibition of Pseudoalteromonas spp., Lactobacillus spp., and Leuconostoc spp. by high CO2 concentration might be the reason for MAP (CO2/N2) samples having lower levels of TVC. Globally, these results indicate that 30% CO2-MAP, 50% CO2-MAP, and CO-MAP are promising packaging methods to improve roast duck meat quality and achieve shelf life extension.

In situ thermal ablation of tumors in combination with nano-adjuvant and immune checkpoint blockade to inhibit cancer metastasis and recurrence.

Tumor ablation therapies provide a minimally invasive approach to treat cancer. However, inhibition of cancer metastasis and recurrence after ablation is still a challenge in clinical trials. Here, we propose a strategy using combinatorial thermal ablation, adjuvants and immune checkpoint blockade (ICB) to inhibit metastatic tumor and recurrence via antitumor immune responses post tumor thermal ablation, which are frequently used in the clinic. Furthermore, a strong immune memory against cancer was observed 80 days after the primary tumor was ablated. Considering that all components in our design are approved by Food and Drug Administration (FDA), we provide a strategy based on clinically used cancer treatment technique that is promising in clinical translation.

Core-shell TaOx@MnO2 nanoparticles as a nano-radiosensitizer for effective cancer radiotherapy.

Improving tumor oxygenation and concentrating X-ray radiation energy inside the tumor have received considerable attention in cancer radiotherapy. Herein, core-shell tantalum oxide@manganese dioxide (TaOx@MnO2) nanostructures are prepared as an efficient radiosensitizer for enhancing radiotherapy (RT). In these nanostructures, the TaOx core serves as a RT sensitizer that efficiently concentrates X-ray radiation energy inside the tumor, while the MnO2 shell may trigger the decomposition of endogenous H2O2 in the tumor microenvironment (TME) to generate oxygen and overcome hypoxia-associated radiation resistance. In vitro and in vivo experiments demonstrated that the synthesized TaOx@MnO2-PEG nanostructures could accomplish an excellent synergistic radiotherapy sensitization effect. Furthermore, TaOx@MnO2-PEG nanoparticles could also serve as promising agents for MR/CT dual-modal imaging. In brief, our study highlights a new type of multifunctional radiosensitizer agent to enhance radiotherapy treatment by means of simultaneously concentrating radiation energy inside tumors and overcoming tumor hypoxia, promising for applications in tumor radiotherapy.

One-pot synthesis of pH-responsive charge-switchable PEGylated nanoscale coordination polymers for improved cancer therapy.

Nanoscale coordination polymers (NCPs) are promising nanomedicine platforms featured with biodegradability and versatile functionalities. However, multi-step post-synthesis surface modification is usually required to functionalize as-made NCPs before their biomedical applications. Moreover, efforts are still required to design therapeutic NCPs responsive to the unique tumor microenvironment to achieve more specific and effective therapy. Herein, we uncover a simple yet general strategy to synthesize a series of polyethylene glycol (PEG) modified NCPs via a one-step method by adding poly-histidine-PEG co-polymer into the mixture of metal ions and organic ligands during NCPs formation. With NCPs consisting Ca2+/dicarboxylic cisplatin (IV) prodrug as the example, we show that such Ca/Pt(IV)@pHis-PEG NCPs are highly sensitive to pH changes. With slightly negative charges and compact structure under pH 7.4 during blood circulation, those NCPs exhibit efficient passive accumulation in the tumor, in which the reduced pH (c.a. 6.5) would trigger charge conversion and size expansion to enhance their tumor retention and cell internationalization. After cellular uptake, NCPs within cell endo-/lysosomes with further reduced pH would then lead to decomposition of those NCPs and thus drug release. Chemotherapy with Ca/Pt(IV)@pHis-PEG NCPs in our animal tumor model demonstrates great efficacy under low drug doses, and is found to be particularly effective towards solid tumors with reduced pH.

Redox-Sensitive Nanoscale Coordination Polymers for Drug Delivery and Cancer Theranostics.

Nanoscale coordination polymers (NCPs), with inherent biodegradability, chemical diversities, and porous structures, are a promising class of nanomaterials in the nanomedicine field. Herein, a unique type of redox-sensitive NCPs is constructed with manganese ions (Mn2+) and dithiodiglycolic acid as the disulfide (SS)-containing organic bridging ligand. The obtained Mn-SS NCPs with a mesoporous structure could be efficiently loaded with doxorubicin (DOX), a chemotherapeutics. The yielded Mn-SS/DOX nanoparticles are coated with a layer of polydopamine (PDA) and then modified by poly(ethylene glycol) (PEG). In such a Mn-SS/DOX@PDA-PEG NCP structure, the disulfide linkage (SS) within dithiodiglycolic acid can be cleaved in the presence of glutathione (GSH), leading to efficient redox-responsive dissociation of NCPs and the subsequent drug release. Meanwhile, Mn2+ in Mn-SS/DOX@PDA-PEG NCPs would offer a strong T1 contrast in magnetic resonance (MR) imaging, Upon intravenous injection, these Mn-SS/DOX@PDA-PEG NCPs show efficient tumor homing, as revealed by MR imaging, and offer an obviously improved in vivo therapeutic outcome compared to that achieved with free DOX.

Three-Dimensional Porous Si and SiO2 with In Situ Decorated Carbon Nanotubes As Anode Materials for Li-ion Batteries.

A high-capacity Si anode is always accompanied by very large volume expansion and structural collapse during the lithium-ion insertion/extraction process. To stabilize the structure of the Si anode, magnesium vapor thermal reduction has been used to synthesize porous Si and SiO2 (pSS) particles, followed by in situ growth of carbon nanotubes (CNTs) in pSS pores through a chemical vapor deposition (CVD) process. Field-emission scanning electron microscopy and high-resolution transmission electron microscopy have shown that the final product (pSS/CNTs) possesses adequate void space intertwined by uniformly distributed CNTs and inactive silica in particle form. pSS/CNTs with such an elaborate structural design deliver improved electrochemical performance, with better coulombic efficiency (70% at the first cycle), cycling capability (1200 mAh g-1 at 0.5 A g-1 after 200 cycles), and rate capability (1984, 1654, 1385, 1072, and 800 mAh g-1 at current densities of 0.1, 0.2, 0.5, 1, and 2 A g-1, respectively), compared to pSS and porous Si/CNTs. These merits of pSS/CNTs are attributed to the capability of void space to absorb the volume changes and that of the silica to confine the excessive lithiation expansion of the Si anode. In addition, CNTs have interwound the particles, leading to significant enhancement of electronic conductivity before and after Si-anode pulverization. This simple and scalable strategy makes it easy to expand the application to manufacturing other alloy anode materials.

Nano polyurethane-assisted ultrasensitive biodetection of H(2)O(2) over immobilized microperoxidase-11.

Nanostructured polyurethane (PU) synthesized by an emulsion polymerization with narrow size distribution was employed for the first time directly as a novel matrix for enzyme immobilization to develop sensitively amperometric biosensors. When Microperoxidase-11 (MP-11) was selected as a model protein, the resulting hydrogen peroxide (H(2)O(2)) biosensor exhibited improved sensitivity of 29.6µAmM(-1)cm(-2) with quite good response time of (1.3±0.4)s and remarkable limit of detection as low as 10pM (S/N 3) over existing protocols. A linear calibration curve for hydrogen peroxide was obtained up to 1.3µM under the optimized conditions with a relative low calculated Michaelis-Menten constant (K(M)(app)) (1.87±0.05)µM, which indicated the enhanced enzymatic affinity of MP-11 to H(2)O(2) via PU. The possible interferents had negligible effect on the response current and time of the prepared biosensor. Results suggest that the PU nanoparticles (PU-NPs) with good biocompatibility and sufficient interfacial adhesion hold promise as an attractive support material for construction of ultrasensitive amperometric biosensor.