Phase and detuning control of the unidirectional reflection amplification based on the broken spatial symmetry.

In order to achieve the tunable unidirectional reflection amplification in a uniform atomic medium that is of vital importance to design high-quality nonreciprocal photonic devices, we propose a coherent closed three-level Δ-type atomic system by applying a microwave field, and a strong coupling field of linear variation along the x direction to control a probe field. In our scheme, the linearly increased coupling field destroys the spatial symmetry of probe susceptibility and effectively suppresses the reflection of one side; the microwave field constructs closed loop transitions to amplify the probe field and causes phase changes. The numerical simulation indicates that the unidirectional reflection amplification is sensitive to the relative phase ϕ and the coupling detuning Δc. Our results will open a new route toward harnessing optical non-reciprocity, which can provide more convenience and possibilities in the experimental realization.

Strong associations between dissolved organic matter and microbial communities in the sediments of Qinghai-Tibetan Plateau lakes depend on salinity.

In aquatic ecosystems, dissolved organic matter (DOM) plays a vital role in microbial communities and the biogeochemical cycling of elements. However, little is known about the associations between DOM and microbial communities in lake sediments. This study investigated the composition of water-extractable organic matter and microbial communities in surface sediments of lakes with different salinities on the Qinghai-Tibet Plateau. Ultrahigh-resolution mass spectrometry and high-throughput microbial sequencing techniques were employed to assess the associations between molecular diversity and microbial diversity and the effects of salinity in 19 lakes spanning a salinity range from 0.22 ‰ to 341.87 ‰. Our results show that increasing salinity of lake water led to higher molecular diversity of DOM in surface sediments. High-salinity lakes exhibited distinct DOM characteristics, such as lower aromaticity, smaller molecular weight, and higher oxidation degree, compared to freshwater lakes. The complexity of the microbial network composition of sediments first increased and then decreased with the increase of salinity. Moreover, as salinity increases, the dominant species transitioned from Gammaproteobacteria to Bacteroidia, and this transition was accompanied by a decrease in microbial diversity and an increase in molecular diversity. Microbial factors accounted for 34.68 % of the variation in the molecular composition of DOM. Overall, this study emphasizes the significant effects of salinity on both molecular and microbial diversity in lake sediments. Furthermore, our findings underscore the importance of microbes in controlling the range of organic compounds present in lakes and deepen our knowledge of the biogeochemical cycling of DOM.

Platycodin D facilitates antiviral immunity through inhibiting cytokine storm via targeting K63-linked TRAF6 ubiquitination.

Influenza virus infection is a worldwide challenge that causes heavy burdens on public health. The mortality rate of severe influenza patients is often associated with hyperactive immunological abnormalities characterized by hypercytokinemia. Due to the continuous mutations and the occurrence of drug-resistant influenza virus strains, the development of host-directed immunoregulatory drugs is urgently required. Platycodon grandiflorum is among the top 10 herbs of traditional Chinese medicine used to treat pulmonary diseases. As one of the major terpenoid saponins extracted from Platycodon grandiflorum, Platycodin D (PD) has been reported to play several roles, including anti-inflammation, analgesia, anti-cancer, hepatoprotection, and immunoregulation. However, the therapeutic roles of PD to treat influenza virus infection remains unknown. Here, we show that PD can protect the body weight loss in severely infected influenza mice, alleviate lung damage, and thus improve the survival rate. More specifically, PD protects flu mice via decreasing the immune cell infiltration into lungs and downregulating the overactivated inflammatory response. Western blot and immunofluorescence assays exhibited that PD could inhibit the activation of TAK1/IKK/NF-κB and MAPK pathways. Besides that, CETSA, SPR and immunoprecipitation assays indicated that PD binds with TRAF6 to decrease its K63 ubiquitination after R837 stimulation. Additionally, siRNA interference experiments exhibited that PD could inhibit the secretion of IL-1ß and TNF-α in TRAF6-dependent manner. Altogether, our results suggested that PD is a promising drug candidate for treating influenza. Our study also offered a scientific explanation for the commonly used Platycodon grandiflorum in many anti-epidemic classic formulas. Due to its host-directed regulatory role, PD may serve as an adjuvant therapeutic drug in conjunction with other antiviral drugs to treat the flu.

The causal relationship of female infertility and psychiatric disorders in the European population: a bidirectional two-sample Mendelian randomization study.

BACKGROUND: Infertility affects many couples globally, causing physical, emotional, and financial burdens. While observational studies suggest a link between psychiatric disorders and female infertility, causal relationships remain uncertain. Mendelian randomization analysis, using genome-wide association studies data, minimizes confounding factors and reverse causation, providing valuable insights into causal associations. METHODS: We conducted Mendelian randomization analysis to explore the potential causal relationship between female infertility and psychiatric disorders. Genome-wide association studies summary data for female infertility (112,105 individuals of European ancestry, comprising 11,442 cases and 100,663 controls), depression (807,553 individuals of European ancestry, comprising 246,363 cases and 561,190 controls), anxiety (21,763 individuals of European ancestry, comprising 7,016 cases and 14,745 controls), bipolar disorder (51,710 individuals of European ancestry, comprising 20,352 cases and 31,358 controls), and eating disorders (72,517 individuals of European ancestry, comprising 16,992 cases and 55,525 controls) were utilized. Instrumental variables were selected based on significant single nucleotide polymorphisms associated with each phenotype. We assessed instrumental variable strength, examined confounding factors, and employed inverse variance weighting, weighted median, and MR-Egger approaches for analysis. RESULTS: Our analysis included 85 single nucleotide polymorphisms for female infertility and 62 single nucleotide polymorphisms for psychiatric disorders. Results suggest a potential causal relationship between depression and female infertility, with both inverse variance weighting and weighted median methods showing increased infertility risk in depressed patients. Evidence is weak regarding bipolar disorder not increasing female infertility risk. We found no evidence supporting causal links between anxiety, eating disorders, and female infertility. Similarly, no causal relationship was found between female infertility and psychiatric disorders in the opposite direction. Sensitivity analyses and tests for heterogeneity and polymorphism supported result robustness. CONCLUSIONS: This analysis provides evidence for a potential causal relationship between depression and female infertility. Addressing depression in infertile women may improve fertility outcomes. Further research is needed to explore underlying mechanisms and potential interventions for improving fertility outcomes in women with psychiatric disorders.

Affinity-Guided Coevolution of Aptamers for Guanine, Xanthine, Hypoxanthine, and Adenine.

The simultaneous evolution of multiple aptamers can drastically increase the speed of aptamer discovery. Most previous studies used the same concentration for different targets, leading to the dominance of the libraries by one or a few aptamers and a low success rate. To foster the best aptamers to grow independently in the sequence space, it is important to (1) use low target concentrations close to their dissociation constants and (2) stop at an early round before any sequence starts to dominate. In this study, we demonstrate this affinity-guided selection concept using the capture-SELEX method to isolate aptamers for four important purines: guanine (5 µM), xanthine (50 µM), hypoxanthine (10 µM), and adenine (10 µM). The round 9 library was split, and in round 10, the four targets were individually used to elute the binding sequences. Using thioflavin T fluorescence spectroscopy and isothermal titration calorimetry, we confirmed highly selective aptamers for xanthine, guanine, and adenine. These aptamers have Kd values below 1 µM and around 100-fold selectivity against most competing analytes, and they compare favorably with existing RNA aptamers and riboswitches. A separate selection was performed using hypoxanthine alone, and no selective aptamer was achieved, even with negative selection, explaining the lack of its aptamer in our mixed selection. This affinity-guided multiplex SELEX study offers fundamental insights into aptamer selection and provides high-quality aptamers for three important purines.

Therapeutic targets for endometriosis: Genome-wide Mendelian randomization and colocalization analyses.

BACKGROUND: Endometriosis (EM) greatly affects women's reproductive health, identifying new drug targets for EM is urgently needed. This study utilizes comprehensive genome-wide Mendelian randomization (MR) and colocalization analyses, using genomic data, to identify potential therapeutic approaches for EM. METHODS: Genome-wide cis-expression quantitative trait loci (cis-eQTL) data were obtained from GTEx V8, which included 838 participants across 49 tissues or cells, and the eQTLGen consortium, which included 31,684 participants. Genome-wide association analysis (GWAS) data for EM were sourced from the FinnGen study, which consisted of 8,288 cases and 68,969 controls, as well as the UK Biobank study, which included 1,496 cases and 359,698 controls. This study utilized MR analysis to assess the correlation between genes and the risk of EM. Subsequently, colocalization analysis was conducted to investigate potential shared causal variants between the identified genes and EM. RESULTS: After conducting MR and colocalization analyses, we identified a total of 13 genes that showed significant evidence of colocalization. These genes are considered promising therapeutic candidates for treating EM. Among them, inner membrane mitochondrial protein (IMMT), src kinase associated phosphoprotein 1 (SKAP1), lysine methyltransferase 5A (KMT5A), KLF transcription factor 12 (KLF12), GRB10 interacting GYF protein 1 (GIGYF1), Wnt family member 7A (WNT7A), Sad1 and UNC84 domain containing 1 (SUN1), and poly (ADP-ribose) polymerase family member 3 (PARP3) were found to have positive associations with the risk of EM. On the other hand, progestin and adipoQ receptor family member 8 (PAQR8), adaptor related protein complex 3 subunit mu 1 (AP3M1), surfeit 6 (SURF6), TUB bipartite transcription factor (TUB), and DNA polymerase delta interacting protein 2 (POLDIP2) were found to have inverse relationships with the risk of EM. CONCLUSIONS: Through genome-wide MR studies, a comprehensive set of genes associated with EM has been identified. Among them, IMMT, PAQR8, SKAP1, KMT5A, AP3M1, SURF6, KLF12, GIGYF1, TUB, WNT7A, SUN1, POLDIP2, and PARP3 show potential as therapeutic targets for EM treatment. Nonetheless, it is crucial to conduct further rigorous investigations to validate these prospects.

Inactivation of harmful cyanobacteria Microcystis aeruginosa by Cu2+ doped corn stalk biochar treated with different pyrolysis temperatures.

In this study, biochars (BCs) derived from corn stalk treated at various pyrolysis temperatures (350-950 °C) were prepared and then loaded with Cu2+ to form highly efficient algaecide, i.e. Cu2+-doped BC composites (Cu-BCs). The results showed BCs pyrolyzed at higher temperatures suppressed the growth of Microcystis aeruginosa in the order of BC550 ≫ BC750 > BC950, while BC350 accelerated cell growth due to the release of inorganic nutrients. The difference could be attributed to the physicochemical characteristics, including specific surface area, adsorption capacity of nutrients and the presence of particularly persistent free radicals. Furthermore, Cu-BCs exhibited the improved inactivation performance, but the 72 h growth inhibition rates and reaction activities of Cu-BCs were still influenced by the Cu2+ loading ratio and pyrolysis temperature. These results, reported for the first time, demonstrated the algae inactivation efficiency of pristine BCs, and Cu-BCs were principally manipulated by the biochar pyrolysis temperature.

CMTM6 promotes hepatocellular carcinoma progression through stabilizing ß-catenin.

CMTM6, a regulator of PD-L1 stability, has been implicated in the development of various cancers. However, the expression and role of CMTM6 in hepatocellular carcinoma (HCC) remains controversial. Our study revealed a negative correlation between CMTM6 expression and HCC prognosis through bioinformatics analysis and immunofluorescence staining. CMTM6 expression was also positively associated with alpha-fetoprotein (AFP) levels, supporting its potential as a prognostic marker for HCC. Using Cmtm6 knockout mice, we found that Cmtm6 deficiency inhibited HCC formation and cell proliferation in primary liver cancer models induced by DEN and DEN/CCl4. In HCC cell lines, CMTM6 promoted cell proliferation and interacted with ß-catenin, stabilizing it by preventing ubiquitination. In conclusion, our study suggested that CMTM6 upregulation promotes HCC cell proliferation through the ß-catenin pathway, making it a potential therapeutic target for HCC treatment.

Arsenic Removal via the Biomineralization of Iron-Oxidizing Bacteria Pseudarthrobacter sp. Fe7.

Arsenic (As) is a highly toxic metalloid, and its widespread contamination of water is a serious threat to human health. This study explored As removal using Fe(II)-oxidizing bacteria. The strain Fe7 isolated from iron mine soil was classified as the genus Pseudarthrobacter based on 16S rRNA gene sequence similarities and phylogenetic analyses. The strain Fe7 was identified as a strain of Gram-positive, rod-shaped, aerobic bacteria that can oxidize Fe(II) and produce iron mineral precipitates. X-ray diffraction, X-ray photoelectron spectroscopy, and energy-dispersive X-ray spectroscopy patterns showed that the iron mineral precipitates with poor crystallinity consisted of Fe(III) and numerous biological impurities. In the co-cultivation of the strain Fe7 with arsenite (As(III)), 100% of the total Fe and 99.9% of the total As were removed after 72 h. During the co-cultivation of the strain Fe7 with arsenate (As(V)), 98.4% of the total Fe and 96.9% of the total As were removed after 72 h. Additionally, the iron precipitates produced by the strain Fe7 removed 100% of the total As after 3 h in both the As(III) and As(V) pollution systems. Furthermore, enzyme activity experiments revealed that the strain Fe7 oxidized Fe(II) by producing extracellular enzymes. When 2% (v/v) extracellular enzyme liquid of the strain Fe7 was added to the As(III) or As(V) pollution system, the total As removal rates were 98.6% and 99.4%, respectively, after 2 h, which increased to 100% when 5% (v/v) and 10% (v/v) extracellular enzyme liquid of the strain Fe7 were, respectively, added to the As(III) and As(V) pollution systems. Therefore, iron biomineralized using a co-culture of the strain Fe7 and As, iron precipitates produced by the strain Fe7, and the extracellular enzymes of the strain Fe7 could remove As(III) and As(V) efficiently. This study provides new insights and strategies for the efficient remediation of arsenic pollution in aquatic environments.

Enabling AI in synthetic biology through Construction File specification.

The Construction File (CF) specification establishes a standardized interface for molecular biology operations, laying a foundation for automation and enhanced efficiency in experiment design. It is implemented across three distinct software projects: PyDNA_CF_Simulator, a Python project featuring a ChatGPT plugin for interactive parsing and simulating experiments; ConstructionFileSimulator, a field-tested Java project that showcases 'Experiment' objects expressed as flat files; and C6-Tools, a JavaScript project integrated with Google Sheets via Apps Script, providing a user-friendly interface for authoring and simulation of CF. The CF specification not only standardizes and modularizes molecular biology operations but also promotes collaboration, automation, and reuse, significantly reducing potential errors. The potential integration of CF with artificial intelligence, particularly GPT-4, suggests innovative automation strategies for synthetic biology. While challenges such as token limits, data storage, and biosecurity remain, proposed solutions promise a way forward in harnessing AI for experiment design. This shift from human-driven design to AI-assisted workflows, steered by high-level objectives, charts a potential future path in synthetic biology, envisioning an environment where complexities are managed more effectively.

Spatial susceptibility modulation and controlled unidirectional reflection amplification via four-wave mixing.

Control of unidirectional light propagation is of paramount importantance to optical signal processing and optical communication. Especially, the amplified optical signal can isolate noise well that may provide more applications. In this work, we propose a dynamically modulated regime to realize unidirectional reflection amplification in a short and dense uniform atomic medium, and all atoms are driven into four-level double-Λ type by two coupling fields with linearly varied intensities along x direction and two weak probe fields. Based on four-wave mixing resonance and the broken spatial symmetry, the complete nonreciprocal reflection (unidirectional reflection) can be amplified with reflectivity more than 2.0, even to 6.0. In addition, the width, height, and position of the unidirectional reflection bands can be tunable. Thus, our regime is feasible and may inspire further applications in all-optical networks that require controllable unidirectional light amplification.

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort.

PURPOSE: To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results. MATERIALS AND METHODS: A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions. RESULTS: The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively. CONCLUSIONS: The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.

Multi-site, Multi-domain Airway Tree Modeling.

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution. Since EXACT'09 pulmonary airway segmentation, limited effort has been directed to the quantitative comparison of newly emerged algorithms driven by the maturity of deep learning based approaches and extensive clinical efforts for resolving finer details of distal airways for early intervention of pulmonary diseases. Thus far, public annotated datasets are extremely limited, hindering the development of data-driven methods and detailed performance evaluation of new algorithms. To provide a benchmark for the medical imaging community, we organized the Multi-site, Multi-domain Airway Tree Modeling (ATM'22), which was held as an official challenge event during the MICCAI 2022 conference. ATM'22 provides large-scale CT scans with detailed pulmonary airway annotation, including 500 CT scans (300 for training, 50 for validation, and 150 for testing). The dataset was collected from different sites and it further included a portion of noisy COVID-19 CTs with ground-glass opacity and consolidation. Twenty-three teams participated in the entire phase of the challenge and the algorithms for the top ten teams are reviewed in this paper. Both quantitative and qualitative results revealed that deep learning models embedded with the topological continuity enhancement achieved superior performance in general. ATM'22 challenge holds as an open-call design, the training data and the gold standard evaluation are available upon successful registration via its homepage (https://atm22.grand-challenge.org/).

[Detection methods for polyethylene terephthalate degrading enzymes: a review].

Polyethylene terephthalate (PET) is one of the most widely used synthetic polyester. It poses serious threat to terrestrial, aquatic ecosystems and human health since it is difficult to be broken down and deposited in the environment. The biodegradation based on enzymatic catalysis offers a sustainable method for recycling PET. A number of PET hydrolases have been discovered in the last 20 years, and protein engineering has increased their degradation capabilities. However, no PET hydrolases that are practical for widespread industrial use have been identified. Screening of PET hydrolase using conventional detection techniques is laborious and inefficient process. Effective detection techniques are required to promote the commercialization of PET hydrolases. Using efficient detection techniques to screen potent industrial enzymes is essential for supporting the widespread industrial implementation of PET hydrolases. To define PET hydrolase, scientists have created a number of analytical techniques recently. The detection techniques that can be used to screen PET hydrolase, including high performance liquid chromatography, ultraviolet absorption spectrometric, and fluorescence activated droplet sorting method, are summarized in this study along with their potential applications.

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.

PURPOSE: To assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol. METHODS: A chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared. RESULTS: With each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all P > 0.05). Of the four scan protocols and ten reconstruction images, APEs for nodule volume had no significant differences (all P > 0.05). At 100 kVp/10 mAs, APEs for density using IMR were the lowest (APE-mean: 6.69), but no significant difference was detected between VMIs at 50 keV (APE-mean: 11.69) and IMR (P = 0.666). In the subgroup analysis, at 100 kVp/10 mAs, there were no significant differences between VMIs at 50 keV and IMR in diameter and density (all P > 0.05). The radiation dose at 100 kVp/10 mAs was reduced by 77.8% compared with that at 120 kVp/30 mAs. CONCLUSION: Compared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

A Semisynthetic Bioluminescence Sensor for Ratiometric Imaging of Metal Ions In Vivo Using DNAzymes Conjugated to An Engineered Nano-Luciferase.

DNA-based probes have gained significant attention as versatile tools for biochemical analysis, benefiting from their programmability and biocompatibility. However, most existing DNA-based probes rely on fluorescence as the signal output, which can be problematic due to issues like autofluorescence and scattering when applied in complex biological materials such as living cells or tissues. Herein, we report the development of bioluminescent nucleic acid (bioLUNA) sensors that offer laser excitation-independent and ratiometric imaging of the target in vivo. The system is based on computational modelling and mutagenesis investigations of a genetic fusion between circular permutated Nano-luciferase (NLuc) and HaloTag, enabling the conjugation of the protein with a DNAzyme. In the presence of Zn2+ , the DNAzyme sensor releases the fluorophore-labelled strand, leading to a reduction in bioluminescent resonance energy transfer (BRET) between the luciferase and fluorophore. Consequently, this process induces ratiometric changes in the bioluminescent signal. We demonstrated that this bioLUNA sensor enabled imaging of both exogenous Zn2+ in vivo and endogenous Zn2+ efflux in normal epithelial prostate and prostate tumors. This work expands the DNAzyme sensors to using bioluminescence and thus has enriched the toolbox of nucleic acid sensors for a broad range of biomedical applications.

Radiomics-based Machine Learning Methods for Volume Doubling Time Prediction of Pulmonary Ground-glass Nodules With Baseline Chest Computed Tomography.

PURPOSE: Reliable prediction of volume doubling time (VDT) is essential for the personalized management of pulmonary ground-glass nodules (GGNs). We aimed to determine the optimal VDT prediction method by comparing different machine learning methods only based on the baseline chest computed tomography (CT) images. MATERIALS AND METHODS: Seven classical machine learning methods were evaluated in terms of their stability and performance for VDT prediction. The VDT, calculated by the preoperative and baseline CT, was divided into 2 groups with a cutoff value of 400 days. A total of 90 GGNs from 3 hospitals constituted the training set, and 86 GGNs from the fourth hospital served as the external validation set. The training set was used for feature selection and model training, and the validation set was used to evaluate the predictive performance of the model independently. RESULTS: The eXtreme Gradient Boosting showed the highest predictive performance (accuracy: 0.890±0.128 and area under the ROC curve (AUC): 0.896±0.134), followed by the neural network (NNet) (accuracy: 0.865±0.103 and AUC: 0.886±0.097). While regarding stability, the NNet showed the highest robustness against data perturbation (relative SDs [%] of mean AUC: 10.9%). Therefore, the NNet was chosen as the final model, achieving high accuracy of 0.756 in the external validation set. CONCLUSION: The NNet is a promising machine learning method to predict the VDT of GGNs, which would assist in the personalized follow-up and treatment strategies for GGNs reducing unnecessary follow-up and radiation dose.

Synthesis of Cu2+ doped biochar and its inactivation performance of Microcystis aeruginosa: Significance of synergetic effect.

The harmful cyanobacteria bloom is frequently occurring in the aquatic environment and poses a tremendous threat to both aquatic organisms and ecological function. In this study, a series of Cu2+ doped biochar (BC) composites (Cu-BCs) with different loading ratios (0.1 %-5 wt %) (Cu-BC-0.1/0.5/1/2.5/5) was successfully fabricated through a one-step adsorption method for in-situ inactivation of Microcystis aeruginosa and simultaneous removal of microcystin-LR (MC-LR). Compared with the single BC/CuSO4 and other Cu-BCs composites, the Cu-BC-2.5 exhibited the best algae inactivation performance with the lowest 72 h medium effective concentration (EC50) value of 0.34 mg/L and highest chlorophyll α degradation efficiency of 8.31 g/g. Notably, the as-prepared Cu-BC-2.5 maintained good inactivation performance in the near-neutral pH (6.5-8.5), and the presence of humic acid and salts such as Na2CO3 and NaCl. The outstanding inhibitory effect of the Cu-BC-2.5 could be explained by the synergetic effect between biochar and Cu2+, which greatly elevated reactive oxygen species (ROS) intensity and in turn led to severe membrane damage and collapse of the antioxidant system. Additionally, the Cu-BC-2.5 could simultaneously remove the released microcystin-LR (MC-LR) throughout the inactivation process and prevent secondary pollution, thus offering a new insight into the alleviation of harmful cyanobacteria in aquatic environment.

The Role of mTORC1 Pathway and Autophagy in Resistance to Platinum-Based Chemotherapeutics.

Cisplatin (cis-diamminedichloroplatinum I) is a platinum-based drug, the mainstay of anticancer treatment for numerous solid tumors. Since its approval by the FDA in 1978, the drug has continued to be used for the treatment of half of epithelial cancers. However, resistance to cisplatin represents a major obstacle during anticancer therapy. Here, we review recent findings on how the mTORC1 pathway and autophagy can influence cisplatin sensitivity and resistance and how these data can be applicable for the development of new therapeutic strategies.