Improved Lignin Conversion to High-Value Aromatic Monomers through Phase Junction CdS with Coexposed Hexagonal (100) and Cubic (220) Facets.

Photocatalysis has the potential for lignin valorization to generate functionalized aromatic monomers, but its application has been limited by the slow conversion rate and the low selectivity to desirable aromatic products. In this work, we designed the phase junction CdS with coexposed hexagonal (100) and cubic (220) facets to improve the photogenerated charge carriers' transfer efficiency from (100) facet to (220) facet and the hydrogen transfer efficiency for an enhanced conversion rate of lignin to aromatic monomers. Water is found as a sufficient external hydrogen supplier to increase the yields of aromatic monomers. These innovative designs in the reaction system promoted complete conversion of PP-ol to around 94% of aromatic monomers after 1 h of visible light irradiation, which shows the highest reaction rate and selectivity of target products in comparison with previous works. PP-one is a byproduct from the overoxidation of PP-ol and is usually difficult to be further cleaved to acetophenone and phenol as the desirable aromatic monomers. TEA was first identified in this study as a sacrificial electron donor, a hydrogen source, and a mediator to enhance the cleavage of the Cß-O bonds in PP-one. With the assistance of TEA, PP-one can be completely cleaved to desirable aromatic monomer products, and the reaction time is reduced from several hours to 10 min of visible light irradiation.

Assessment of the implementation of accelerated drug marketing registration procedures for antineoplastic and immunomodulating agents in China: based on 2016-2022 review data.

Objective: Since 2016, China has successively implemented Accelerated Drug Marketing Registration Procedures (ADMRPs) for drugs, including Breakthrough Therapy Drug (BTD), Conditional Approval (CA), and Priority Review and Approval (PRA), which have played an important role in promoting the development and review of clinically urgently needed drugs. In this study, we focused on the antineoplastic and immunomodulating agents approved for marketing through ADMRPs, to provide a reference for promoting the formation of a stable and mature regulatory system for the review and approval of antineoplastic drugs and immunomodulating agents in China. Methods: Reviewed the National Medical Products Administration (NMPA) drug review reports for the years 2016-2022 and screened the antineoplastic and immunomodulating agents approved through ADMRPs. Then, with the help of the NMPA website and the Yaozhi Database, two researchers independently queried and entered the detailed information of the selected drugs, and checked with each other. The attribute classification and main characteristics of the drugs were then analyzed with descriptive statistics to obtain the trend of drug types, drug review and approval status, and timeliness. Results: A total of 206 antineoplastic and immunomodulating agents were approved for marketing through five accelerated marketing registration procedures (or procedure combinations), with the average review time shortened by about 81 days. Among them, imported drugs accounted for a larger proportion, the most drugs for treating non-small cell lung cancer and lymphoma, and the largest number of PD-1/PDL-1 inhibitors, but pediatric drugs and rare disease drugs accounted for a smaller proportion. Conclusion: ADMRPs can promote the accessibility of antineoplastic and immunomodulating agents in China and safeguard the life and health rights of more patients. Nevertheless, it is necessary to pay attention to the expansion of the types of indications for medicines and to increase the development of drugs that are urgently needed by a small number of patients.

First Results from a Broadband Search for Dark Photon Dark Matter in the 44 to 52 µeV Range with a Coaxial Dish Antenna.

We present first results from a dark photon dark matter search in the mass range from 44 to 52 µeV (10.7-12.5 GHz) using a room-temperature dish antenna setup called GigaBREAD. Dark photon dark matter converts to ordinary photons on a cylindrical metallic emission surface with area 0.5 m^{2} and is focused by a novel parabolic reflector onto a horn antenna. Signals are read out with a low-noise receiver system. A first data taking run with 24 days of data does not show evidence for dark photon dark matter in this mass range, excluding dark photon photon mixing parameters χ≳10^{-12} in this range at 90% confidence level. This surpasses existing constraints by about 2 orders of magnitude and is the most stringent bound on dark photons in this range below 49 µeV.

Detection and coverage estimation of purple nutsedge in turf with image classification neural networks.

BACKGROUND: Accurate detection of weeds and estimation of their coverage is crucial for implementing precision herbicide applications. Deep learning (DL) techniques are typically used for weed detection and coverage estimation by analyzing information at the pixel or individual plant level, which requires a substantial amount of annotated data for training. This study aims to evaluate the effectiveness of using image-classification neural networks (NNs) for detecting and estimating weed coverage in bermudagrass turf. RESULTS: Weed-detection NNs, including DenseNet, GoogLeNet and ResNet, exhibited high overall accuracy and F1 scores (≥0.971) throughout the k-fold cross-validation. DenseNet outperformed GoogLeNet and ResNet with the highest overall accuracy and F1 scores (0.977). Among the evaluated NNs, DenseNet showed the highest overall accuracy and F1 scores (0.996) in the validation and testing data sets for estimating weed coverage. The inference speed of ResNet was similar to that of GoogLeNet but noticeably faster than DenseNet. ResNet was the most efficient and accurate deep convolution neural network for weed detection and coverage estimation. CONCLUSION: These results demonstrated that the developed NNs could effectively detect weeds and estimate their coverage in bermudagrass turf, allowing calculation of the herbicide requirements for variable-rate herbicide applications. The proposed method can be employed in a machine vision-based autonomous site-specific spraying system of smart sprayers. © 2024 Society of Chemical Industry.

PhosAF: An integrated deep learning architecture for predicting protein phosphorylation sites with AlphaFold2 predicted structures.

Phosphorylation is indispensable in comprehending biological processes, while biological experimental methods for identifying phosphorylation sites are tedious and arduous. With the rapid growth of biotechnology, deep learning methods have made significant progress in site prediction tasks. Nevertheless, most existing predictors only consider protein sequence information, that limits the capture of protein spatial information. Building upon the latest advancement in protein structure prediction by AlphaFold2, a novel integrated deep learning architecture PhosAF is developed to predict phosphorylation sites in human proteins by integrating CMA-Net and MFC-Net, which considers sequence and structure information predicted by AlphaFold2. Here, CMA-Net module is composed of multiple convolutional neural network layers and multi-head attention is appended to obtaining the local and long-term dependencies of sequence features. Meanwhile, the MFC-Net module composed of deep neural network layers is used to capture the complex representations of evolutionary and structure features. Furthermore, different features are combined to predict the final phosphorylation sites. In addition, we put forward a new strategy to construct reliable negative samples via protein secondary structures. Experimental results on independent test data and case study indicate that our model PhosAF surpasses the current most advanced methods in phosphorylation site prediction.

Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa.

BACKGROUND: Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa. RESULTS: A novel neural network, ResNet-101-segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet-101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet-101), ResNet-101-segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (>90%) when trained with sufficient data sets. CONCLUSION: ResNet-101-segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet-101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. © 2024 Society of Chemical Industry.

Evaluation of two deep learning-based approaches for detecting weeds growing in cabbage.

BACKGROUND: Machine vision-based precision weed management is a promising solution to substantially reduce herbicide input and weed control cost. The objective of this research was to compare two different deep learning-based approaches for detecting weeds in cabbage: (1) detecting weeds directly, and (2) detecting crops by generating the bounding boxes covering the crops and any green pixels outside the bounding boxes were deemed as weeds. RESULTS: The precision, recall, F1-score, mAP0.5, mAP0.5:0.95 of You Only Look Once (YOLO) v5 for detecting cabbage were 0.986, 0.979, 0.982, 0.995, and 0.851, respectively, while these metrics were 0.973, 0.985, 0.979, 0.993, and 0.906 for YOLOv8, respectively. However, none of these metrics exceeded 0.891 when detecting weeds. The reduced performances for directly detecting weeds could be attributed to the diverse weed species at varying densities and growth stages with different plant morphologies. A segmentation procedure demonstrated its effectiveness for extracting weeds outside the bounding boxes covering the crops, and thereby realizing effective indirect weed detection. CONCLUSION: The indirect weed detection approach demands less manpower as the need for constructing a large training dataset containing a variety of weed species is unnecessary. However, in a certain case, weeds are likely to remain undetected due to their growth in close proximity with crops and being situated within the predicted bounding boxes that encompass the crops. The models generated in this research can be used in conjunction with the machine vision subsystem of a smart sprayer or mechanical weeder. © 2024 Society of Chemical Industry.

Semi-supervised learning methods for weed detection in turf.

BACKGROUND: Accurate weed detection is a prerequisite for precise automatic precision herbicide application. Previous research has adopted the laborious and time-consuming approach of manually labeling and processing large image data sets to develop deep neural networks for weed detection. This research introduces a novel semi-supervised learning (SSL) approach for detecting weeds in turf. The performance of SSL was compared with that of ResNet50, a fully supervised learning (FSL) method, in detecting and differentiating sub-images containing weeds from those containing only turfgrass. RESULTS: Compared with ResNet50, the evaluated SSL methods, Π-model, Mean Teacher, and FixMatch, increased the classification accuracy by 2.8%, 0.7%, and 3.9%, respectively, when only 100 labeled images per class were utilized. FixMatch was the most efficient and reliable model, as it exhibited higher accuracy (≥0.9530) and F1 scores (≥0.951) with fewer labeled data (50 per class) in the validation and testing data sets than the other neural networks evaluated. CONCLUSION: These results reveal that the SSL deep neural networks are capable of being highly accurate while requiring fewer labeled training images, thus being more time- and labor-efficient than the FSL method. © 2024 Society of Chemical Industry.

Efficacy and Safety of Huangqi Guizhi Wuwu Decoction for Oxaliplatin-Induced Peripheral Neurotoxicity: A Systematic Review and Meta-Analysis.

Objective: Oxaliplatin is a first-line chemotherapy drug for the treatment of colorectal cancer, but its induced oxaliplatin-induced peripheral neurotoxicity (OIPN) affect the chemotherapy process and quality of life of tumor patients. OIPN is a serious and potentially permanent side effect of cancer treatment. Currently, no unified standard has been established for preventing and treating OIPN in Western medicine. Therefore, it is very important to seek effective prevention and treatment measures. Many clinical trials have reported that Huangqi Guizhi Wuwu decoction can effectively prevent OIPN, but substantial evidence base to support this treatment is lacking. We collected existing literature and evaluated the clinical efficacy and safety of Huangqi Guizhi Wuwu decoction for OIPN by performing a meta-analysis. Methods: We systematically searched China National Knowledge Internet (CNKI), VIP, Wan Fang Database, Pubmed, EMBASE, and Cochrane Library from inception through to Oct 2022 to identify only randomized controlled trials examining the prevention of OIPN using Huangqi Guizhi Wuwu decoction. This search was supplemented by manual retrieval, including dissertations and conference papers. All data were analyzed using RevMan 5.3 software. Results: A total of 18 papers involving 564 patients in the treatment group and 523 patients in the control group were included. A total of 17 articles reported the overall incidence of peripheral neurotoxicity (I² = 0%), and the overall incidence of peripheral neurotoxicity in the treatment group was 0.27 times higher than in the control group (95% CI: 0.20-0.36). A total of 16 articles reported the incidence of level III-IV severe peripheral neurotoxicity (I² = 0%), which was 0.16 times higher in the treatment group than in the control group (95% CI: 0.09-0.32). In the Huangqi Guizhi Wuwu VS no-interference subgroup, it showed that the incidence of severe peripheral neurotoxicity in the treat group was significantly lower than in the control group (OR:0.13, 95% CI:0.06-0.28). But in the Huangqi Guizhi Wuwu VS west medicine therapy subgroup, no significant difference between Huangqi Quizhi Wuwu and conventional Western medicine was observed for the prevention and treatment of severe OIPN (OR:0.37, 95% CI:0.09-1.53). A total of 2 articles were reported median nerve conduction velocity (I² = 51.2%); and no significant difference was found between the treatment and control groups (SMD: 1.43; 95% CI: 0.80-2.08); 4 studies showed Huangqi Guizhi Wuwu decoction did not increase the incidence of chemotherapy-related adverse reactions and was safe. Conclusions: Our current findings support the application of Huangqi Guizhi Wuwu decoction for the clinical prevention and treatment of patients with OIPN. However, high-quality RCT research is still needed to further exploration. The potential impact of Huangqi Guizhi Wuwu decoction on the quality of life or treatment compliance of cancer patients needs further research.

Pharmacokinetics and safety evaluation of intravenously administered Pseudomonas phage PA_LZ7 in a mouse model.

IMPORTANCE: Phage therapy is gaining traction as an alternative to antibiotics due to the rise of multi-drug-resistant (MDR) bacteria. This study assessed the pharmacokinetics and safety of PA_LZ7, a phage targeting MDR Pseudomonas aeruginosa, in mice. After intravenous administration, the phage showed an exponential decay in plasma and its concentration dropped significantly within 24 h for all dosage groups. Although there was a temporary increase in certain plasma cytokines and spleen weight at higher dosages, no significant toxicity was observed. Therefore, PA_LZ7 shows potential as an effective and safe candidate for future phage therapy against MDR P. aeruginosa infections.

STAGAN: An approach for improve the stability of molecular graph generation based on generative adversarial networks.

With the wide application of deep learning in Drug Discovery, deep generative model has shown its advantages in drug molecular generation. Generative adversarial networks can be used to learn the internal structure of molecules, but the training process may be unstable, such as gradient disappearance and model collapse, which may lead to the generation of molecules that do not conform to chemical rules or a single style. In this paper, a novel method called STAGAN was proposed to solve the difficulty of model training, by adding a new gradient penalty term in the discriminator and designing a parallel layer of batch normalization used in generator. As an illustration of method, STAGAN generated higher valid and unique molecules than previous models in training datasets from QM9 and ZINC-250K. This indicates that the proposed method can effectively solve the instability problem in the model training process, and can provide more instructive guidance for the further study of molecular graph generation.

TRAF6 triggers Mycobacterium-infected host autophagy through Rab7 ubiquitination.

Tumor necrosis factor receptor-associated factor 6 (TRAF6) is an E3 ubiquitin ligase that is extensively involved in the autophagy process by interacting with diverse autophagy initiation and autophagosome maturation molecules. However, whether TRAF6 interacts with lysosomal proteins to regulate Mycobacterium-induced autophagy has not been completely characterized. Herein, the present study showed that TRAF6 interacted with lysosomal key proteins Rab7 through RING domain which caused Rab7 ubiquitination and subsequently ubiquitinated Rab7 binds to STX17 (syntaxin 17, a SNARE protein that is essential for mature autophagosome), and thus promoted the fusion of autophagosomes and lysosomes. Furthermore, TRAF6 enhanced the initiation and formation of autophagosomes in Mycobacterium-induced autophagy in both BMDMs and RAW264.7 cells, as evidenced by autophagic flux, colocalization of LC3 and BCG, autophagy rates, and autophagy-associated protein expression. Noteworthy to mention, TRAF6 deficiency exacerbated lung injury and promoted BCG survival. Taken together, these results identify novel molecular and cellular mechanisms by which TRAF6 positively regulates Mycobacterium-induced autophagy.

Successful treatment of eyebrow intradermal nevi by shearing combined with electrocautery and curettage: Two case reports.

BACKGROUND: An intradermal nevus is a common skin tumour, and the classical method of removal has a risk of recurrence and scarring. It is a challenge for dermatologists to treat eyebrow intradermal nevi quickly and efficiently. This study focused on investigating the efficacy and safety of shearing combined with electrocautery and curettage in the treatment of eyebrow intradermal nevi. CASE SUMMARY: We describe two adult patients with eyebrow intradermal nevi treated by shearing combined with electrocautery and curettage. Both patients were followed up regularly after surgery. At follow-up, no recurrence of eyebrow intradermal nevus and no obvious scars or hypopigmentation were found in either patient. The results indicated that shearing combined with electrocautery and curettage could remove eyebrow intradermal nevus without side effects and confirmed the efficacy and safety of this modality for treating these skin lesions. CONCLUSION: Shearing combined with electrocautery and curettage has superior merits, including simple operation, good cosmetic effects, and high patient satisfaction, presenting great application potential for treating intracutaneous nevus.

Case report: Inspiration from a rare fatal heart perforation after percutaneous vertebroplasty.

The principal benefit of employing percutaneous vertebroplasty (PVP) for managing osteoporotic vertebral compression fractures lies in its capacity to facilitate early mobilization in elderly patients, thereby effectively avoiding the potential catastrophic complications associated with prolonged bedridden states. However, bone cement leakage, as the most common complication of PVP, may have fatal consequences. Here, we report a case involving an 85-year-old male patient with L1 vertebral compression fracture who underwent PVP at our hospital and was discharged on the same day of the surgical intervention. Subsequently, the patient experienced symptoms of chest tightness and palpitations. Cardiac ultrasound examination revealed pericardial effusion, while pulmonary computed tomographic angiography (CTA) demonstrated a strip high-density shadow in the right ventricular area. Finally, it was determined that the perforation of the right ventricular wall was caused by bone cement embolism. Through this comprehensive case report, we aim to deepen the understanding of orthopedic doctors on the importance of preventing bone cement leakage.

Corrigendum to "Photocatalytic oxygen evolution and antibacterial biomimetic repair membrane for diabetes wound repair via HIF1-α pathway" [Mater. Today Bio 20 (2023) 100616].

[This corrects the article DOI: 10.1016/j.mtbio.2023.100616.].

Engineered phage with cell-penetrating peptides for intracellular bacterial infections.

IMPORTANCE: Salmonella infection is a significant threat to global public health, and the increasing prevalence of antibiotic resistance exacerbates the situation. Therefore, finding new and effective ways to combat this pathogen is essential. Phages are natural predators of bacteria and can be used as an alternative to antibiotics to kill specific bacteria, including drug-resistant strains. One significant limitation of using phages as antimicrobial agents is their low cellular uptake, which limits their effectiveness against intracellular bacterial infections. Therefore, finding ways to enhance phage uptake is crucial. Our study provides a straightforward strategy for displaying cell-penetrating peptides on non-model phages, offering a promising novel and effective therapeutic approach for treating intracellular and drug-resistant bacteria. This approach has the potential to address the global challenge of antibiotic resistance and improve public health outcomes.

Integrating host transcriptomic signatures for distinguishing autoimmune encephalitis in cerebrospinal fluid by metagenomic sequencing.

BACKGROUND: The early accurate diagnoses for autoimmune encephalitis (AE) and infectious encephalitis (IE) are essential since the treatments for them are different. This study aims to discover some specific and sensitive biomarkers to distinguish AE from IE at early stage to give specific treatments for good outcomes. RESULTS: We compared the host gene expression profiles and microbial diversities of cerebrospinal fluid (CSF) from 41 patients with IE and 18 patients with AE through meta-transcriptomic sequencing. Significant differences were found in host gene expression profiles and microbial diversities in CSF between patients with AE and patients with IE. The most significantly upregulated genes in patients with IE were enriched in pathways related with immune response such as neutrophil degranulation, antigen processing and presentation and adaptive immune system. In contrast, those upregulated genes in patients with AE were mainly involved in sensory organ development such as olfactory transduction, as well as synaptic transmission and signaling. Based on the differentially expressed genes, a classifier consisting of 5 host genes showed outstanding performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.95. CONCLUSIONS: This study provides a promising classifier and is the first to investigate transcriptomic signatures for differentiating AE from IE by using meta-transcriptomic next-generation sequencing technology.

LncRNA NR_003508 Suppresses Mycobacterium tuberculosis-Induced Programmed Necrosis via Sponging miR-346-3p to Regulate RIPK1.

Emerging evidence suggests that long non-coding RNAs (LncRNAs) are involved in Mtb-induced programmed necrosis. Among these LncRNAs, LncRNA NR_003508 is associated with LPS-induced acute respiratory distress syndrome. However, whether LncRNA NR_003508 contributes to Mtb-induced programmed necrosis remains undocumented. Firstly, the expression of LncRNA NR_003508 was determined using RT-qPCR and FISH. The protein expression of RIPK1, p-RIPK1, RIPK3, p-RIPK3, MLKL, and p-MLKL was measured by Western blot in RAW264.7 and mouse lung tissues. Furthermore, luciferase reporter assays and bioinformatics were used to predict specific miRNA (miR-346-3p) and mRNA (RIPK1) regulated by LncRNA NR_003508. In addition, RT-qPCR was used to detect the RIPK1 expression in TB patients and healthy peripheral blood. The flow cytometry assay was performed to detect cell necrosis rates. Here we show that BCG infection-induced cell necrosis and increased LncRNA NR_003508 expression. si-NR_003508 inhibited BCG/H37Rv-induced programmed necrosis in vitro or in vivo. Functionally, LncRNA NR_003508 has been verified as a ceRNA for absorbing miR-346-3p, which targets RIPK1. Moreover, RIPK1 expression was elevated in the peripheral blood of TB patients compared with healthy people. Knockdown of LncRNA NR_003508 or miR-346-3p overexpression suppresses cell necrosis rate and ROS accumulation in RAW264.7 cells. In conclusion, LncRNA NR_003508 functions as a positive regulator of Mtb-induced programmed necrosis via sponging miR-346-3p to regulate RIPK1. Our findings may provide a promising therapeutic target for tuberculosis.

Photocatalytic oxygen evolution and antibacterial biomimetic repair membrane for diabetes wound repair via HIF1-α pathway.

Diabetic wounds always have puzzled patients and caused serious social problems. Due to the lack of local blood vessels, severe hypoxia is generated in the defect area, which is an essential reason for the difficulty of wound healing. We have constructed a photocatalytic oxygen evolution and antibacterial biomimetic repair membrane to solve the problems of wound repair. A scanning electron microscope and transmission electron microscope characterized the biomimetic repair membrane. The oxygen evolution of the biomimetic membrane was tested by an oxygen meter. The excellent antibacterial performance of the biomimetic repair membrane was also verified by co-culture with Staphylococcus aureus and Escherichia coli. It was confirmed that the expression of collagen and HIF1-α in fibroblasts was significantly increased in vitro. And the mitochondrial activity of the vascular and nerve was increased considerably. In vivo, the healing time of diabetes wounds treated with the biomimetic repair membrane was significantly reduced, the collagen and the number of pores were increased considerably, and vascular regeneration was enhanced. The biomimetic repair membrane has an excellent performance in photocatalytic oxygen evolution and antibacterial and can significantly promote the repair of diabetes wounds. This will provide a promising treatment for diabetes wound repair.

An easy and straightforward synthesized nano calcium phosphate for highly capture of multiply phosphorylated peptides.

Multisite phosphorylation of proteins regulates various cellular life activities, however, the capture of low abundance multi-phosphopeptides from biosamples and identification of phosphorylation sites are largely limited due to the limited enrichment materials and their unclear interactions with multi-phosphopeptides. Here we propose using two cheap raw materials (CaCl2·2H2O and Na2HPO4·12H2O) in 10 min at room temperature to synthesize the structurally simple Nanometric Calcium Phosphate (CaP) to resolve this challenge. The current results showed that the "simple" CaP has good selection specificity, high sensitivity and stability for multi-phosphopeptides enrichment and the identification of phosphorylation sites, which facilitate the popularization and application of phosphoproteomics research. Further, the interaction of CaP and multi-phosphopeptides were qualitatively characterized at the molecular/atomic level and the high affinity between them was quantified by the isothermal titration microcalorimeter based on the laws of thermodynamics. The results indicated that the interaction was a spontaneous (ΔG < 0) exothermic reaction with enthalpy reduction (ΔH < 0) and driven mainly by hydrogen bond and electrostatic interaction process.