Advancements in inhibitors of crucial enzymes in the cysteine biosynthetic pathway: Serine acetyltransferase and O-acetylserine sulfhydrylase.

Infectious diseases have been jeopardized problem that threaten public health over a long period of time. The growing prevalence of drug-resistant pathogens and infectious cases have led to a decrease in the number of effective antibiotics, which highlights the urgent need for the development of new antibacterial agents. Serine acetyltransferase (SAT), also known as CysE in certain bacterial species, and O-acetylserine sulfhydrylase (OASS), also known as CysK in select bacteria, are indispensable enzymes within the cysteine biosynthesis pathway of various pathogenic microorganisms. These enzymes play a crucial role in the survival of these pathogens, making SAT and OASS promising targets for the development of novel anti-infective agents. In this comprehensive review, we present an introduction to the structure and function of SAT and OASS, along with an overview of existing inhibitors for SAT and OASS as potential antibacterial agents. Our primary focus is on elucidating the inhibitory activities, structure-activity relationships, and mechanisms of action of these inhibitors. Through this exploration, we aim to provide insights into promising strategies and prospects in the development of antibacterial agents that target these essential enzymes.

Bioluminescence Imaging of Potassium Ion Using a Sensory Luciferin and an Engineered Luciferase.

Bioluminescent indicators are power tools for studying dynamic biological processes. In this study, we present the generation of novel bioluminescent indicators by modifying the luciferin molecule with an analyte-binding moiety. Specifically, we have successfully developed the first bioluminescent indicator for potassium ions (K+), which are critical electrolytes in biological systems. Our approach involved the design and synthesis of a K+-binding luciferin named potassiorin. Additionally, we engineered a luciferase enzyme called BRIPO (bioluminescent red indicator for potassium) to work synergistically with potassiorin, resulting in optimized K+-dependent bioluminescence responses. Through extensive validation in cell lines, primary neurons, and live mice, we demonstrated the efficacy of this new tool for detecting K+. Our research demonstrates an innovative concept of incorporating sensory moieties into luciferins to modulate luciferase activity. This approach has great potential for developing a wide range of bioluminescent indicators, advancing bioluminescence imaging (BLI), and enabling the study of various analytes in biological systems.

Bioluminescence Imaging of Potassium Ion Using a Sensory Luciferin and an Engineered Luciferase.

Bioluminescent indicators are power tools for studying dynamic biological processes. In this study, we present the generation of novel bioluminescent indicators by modifying the luciferin molecule with an analyte-binding moiety. Specifically, we have successfully developed the first bioluminescent indicator for potassium ions (K+), which are critical electrolytes in biological systems. Our approach involved the design and synthesis of a K+-binding luciferin named potassiorin. Additionally, we engineered a luciferase enzyme called BRIPO (bioluminescent red indicator for potassium) to work synergistically with potassiorin, resulting in optimized K+-dependent bioluminescence responses. Through extensive validation in cell lines, primary neurons, and live mice, we demonstrated the efficacy of this new tool for detecting K+. Our research demonstrates an innovative concept of incorporating sensory moieties into luciferins to modulate luciferase activity. This approach has great potential for developing a wide range of bioluminescent indicators, advancing bioluminescence imaging (BLI), and enabling the study of various analytes in biological systems.

Enable the Domino-Like Structural Recovering in Bismuth Anode to Achieve Fast and Durable Na/K Storages.

Alloying-type anodes show capacity and density advantages for sodium/potassium-ion batteries (SIBs/PIBs), but they encounter serious structural degradation upon cycling, which cannot be resolved through conventional nanostructuring techniques. Herein, we present an in-depth study to reveal the intrinsic reason for the pulverization of bismuth (Bi) materials upon (de)alloying, and report a novel particle-in-bulk architecture with Bi nanospheres inlaid in the bulk carbon (BiNC) to achieve durable Na/K storage. We simulate the volume-expansion-resistant mechanism of Bi during the (de)alloying reaction, and unveil that the irreversible phase transition upon (de)alloying underlies the fundamental origin for the structural degradation of Bi anode, while a proper compressive stress (~10 %) raised by the bulk carbon can trigger a "domino-like" Bi crystal recovering. Consequently, the as obtained BiNC exhibits a record high volumetric capacity (823.1 mAh cm-3 for SIBs, 848.1 mAh cm-3 for PIBs) and initial coulombic efficiency (95.3 % for SIBs, 96.4 % for PIBs), and unprecedented cycling stability (15000 cycles for SIBs with only 0.0015 % degradation per cycle), outperforming the state-of-the-art literature. This work provides new insights on the undesirable structural evolution, and proposes basic guidelines for design of the anti-degradation structure for alloy-type electrode materials.

Land Use and Land Cover Classification Meets Deep Learning: A Review.

As one of the important components of Earth observation technology, land use and land cover (LULC) image classification plays an essential role. It uses remote sensing techniques to classify specific categories of ground cover as a means of analyzing and understanding the natural attributes of the Earth's surface and the state of land use. It provides important information for applications in environmental protection, urban planning, and land resource management. However, remote sensing images are usually high-dimensional data and have limited available labeled samples, so performing the LULC classification task faces great challenges. In recent years, due to the emergence of deep learning technology, remote sensing data processing methods based on deep learning have achieved remarkable results, bringing new possibilities for the research and development of LULC classification. In this paper, we present a systematic review of deep-learning-based LULC classification, mainly covering the following five aspects: (1) introduction of the main components of five typical deep learning networks, how they work, and their unique benefits; (2) summary of two baseline datasets for LULC classification (pixel-level, patch-level) and performance metrics for evaluating different models (OA, AA, F1, and MIOU); (3) review of deep learning strategies in LULC classification studies, including convolutional neural networks (CNNs), autoencoders (AEs), generative adversarial networks (GANs), and recurrent neural networks (RNNs); (4) challenges faced by LULC classification and processing schemes under limited training samples; (5) outlooks on the future development of deep-learning-based LULC classification.

A Stress Self-Adaptive Structure to Suppress the Chemo-mechanical Degradation for High Rate and Ultralong Cycle Life Sodium Ion Batteries.

Transition-metal phosphides (TMPs) as typical conversion-type anode materials demonstrate extraordinary theoretical charge storage capacity for sodium ion batteries, but the unavoidable volume expansion and irreversible capacity loss upon cycling represent their long-standing limitations. Herein we report a stress self-adaptive structure with ultrafine FeP nanodots embedded in dense carbon microplates skeleton (FeP@CMS) via the spatial confinement of carbon quantum dots (CQDs). Such an architecture delivers a record high specific capacity (778 mAh g-1 at 0.05 A g-1 ) and ultra-long cycle stability (87.6 % capacity retention after 10 000 cycles at 20 A g-1 ), which outperform the state-of-the-art literature. We decode the fundamental reasons for this unprecedented performance, that such an architecture allows the spontaneous stress transfer from FeP nanodots to the surrounding carbon matrix, thus overcomes the intrinsic chemo-mechanical degradation of metal phosphides.

A genetically encoded far-red fluorescent indicator for imaging synaptically released Zn2.

Synaptic zinc ion (Zn2+) has emerged as a key neuromodulator in the brain. However, the lack of research tools for directly tracking synaptic Zn2+ in the brain of awake animals hinders our rigorous understanding of the physiological and pathological roles of synaptic Zn2+. In this study, we developed a genetically encoded far-red fluorescent indicator for monitoring synaptic Zn2+ dynamics in the nervous system. Our engineered far-red fluorescent indicator for synaptic Zn2+ (FRISZ) displayed a substantial Zn2+-specific turn-on response and low-micromolar affinity. We genetically anchored FRISZ to the mammalian extracellular membrane via a transmembrane (TM) ⍺ helix and characterized the resultant FRISZ-TM construct at the mammalian cell surface. We used FRISZ-TM to image synaptic Zn2+ in the auditory cortex in acute brain slices and awake mice in response to electric and sound stimuli, respectively. Thus, this study establishes a technology for studying the roles of synaptic Zn2+ in the nervous system.

Zinc Single-Atom-Regulated Hard Carbons for High-Rate and Low-Temperature Sodium-Ion Batteries.

Hard carbons, as one of the most commercializable anode materials for sodium-ion batteries (SIBs), have to deal with the trade-off between the rate capability and specific capacity or initial Columbic efficiency (ICE), and the fast performance decline at low temperature (LT) remains poorly understood. Here, a comprehensive regulation on the interfacial/bulk electrochemistry of hard carbons through atomic Zn doping is reported, which demonstrates a record-high reversible capacity (546 mAh g-1 ), decent ICE (84%), remarkable rate capability (140 mAh g-1 @ 50 A g-1 ), and excellent LT capacity (443 mAh g-1 @ -40 °C), outperforming the state-of-the-art literature. This work reveals that the Zn doping can generally induce a local electric field to enable fast bulk Na+ transportation, and meanwhile catalyze the decomposition of NaPF6 to form a robust inorganic-rich solid-electrolyte interphase, which elaborates the underlying origin of the boosted electrochemical performance. Importantly, distinguished from room temperature, the intrinsic Na+ migration/desolvation ability of the electrolyte is disclosed to be the crucial rate-determining factors for the SIB performance at LT. This work provides a fundamental understanding on the charge-storage kinetics at varied temperatures.

PVNAS: 3D Neural Architecture Search With Point-Voxel Convolution.

3D neural networks are widely used in real-world applications (e.g., AR/VR headsets, self-driving cars). They are required to be fast and accurate; however, limited hardware resources on edge devices make these requirements rather challenging. Previous work processes 3D data using either voxel-based or point-based neural networks, but both types of 3D models are not hardware-efficient due to the large memory footprint and random memory access. In this paper, we study 3D deep learning from the efficiency perspective. We first systematically analyze the bottlenecks of previous 3D methods. We then combine the best from point-based and voxel-based models together and propose a novel hardware-efficient 3D primitive, Point-Voxel Convolution (PVConv). We further enhance this primitive with the sparse convolution to make it more effective in processing large (outdoor) scenes. Based on our designed 3D primitive, we introduce 3D Neural Architecture Search (3D-NAS) to explore the best 3D network architecture given a resource constraint. We evaluate our proposed method on six representative benchmark datasets, achieving state-of-the-art performance with 1.8-23.7× measured speedup. Furthermore, our method has been deployed to the autonomous racing vehicle of MIT Driverless, achieving larger detection range, higher accuracy and lower latency.

A fast, high-affinity fluorescent serotonin biosensor engineered from a tick lipocalin.

Serotonin (5-HT) is an important signaling monoamine and neurotransmitter. We report structure-guided engineering of a green fluorescent, genetically encoded serotonin sensor (G-GESS) from a 5-HT-binding lipocalin in the soft tick Argas monolakensis. G-GESS shows fast response kinetics and high affinity, specificity, brightness and photostability. We used G-GESS to image 5-HT dynamics in cultured cells, brain slices and behaving mice.

Comparison and identification of metabolic profiling of bicyclol in rats, dogs and humans in vitro and in vivo.

Bicyclol, a novel hepatoprotective agent, has been widely used to treat chronic viral hepatitis and drug-induced liver injury (DILI). However, its metabolic characteristics remains to be explored, especially in humans. The current study aimed to identify major metabolites and specific metabolizing enzymes involved in bicyclol metabolism in vitro and in vivo using high performance liquid chromatography coupled with Q-Exactive orbitrap mass spectrometry (HPLC-Q-Exactive Orbitrap/MS). After incubation with liver microsomes and oral administration to rats, dogs and humans, a total of nine metabolites of bicyclol were identified including M1 (methyl ester hydrolysate product), M2-M3 (demethylated bicyclol), M4-M5 (demethoxy or dehydroxymethyl bicyclol), M6 (glucuronidated bicyclol) and M7-M9 (glucuronide conjugates of metabolites). Among these metabolites, M2 and M3 were the major phase I metabolites mainly mediated by CYP2C19 and CYP3A4, while M6 was the dominant phase II metabolite primarily catalyzed by UGT2B4. In this study, species-related metabolic difference among rats, dogs and humans were observed. In humans and dogs, M6 (glucuronidated bicyclol) was the most abundant circulating metabolite (higher than the parent drug) in the blood after oral administration, while the parent drug was the highest in rats. M4 and M5 were rats-specific metabolites whereas M1 and M9 were absent in dogs in vivo. The metabolism of bicyclol was demonstrated as demethylation and glucuronidation mediated by multiple drug metabolizing enzymes in different species. Our findings systematically elucidated the metabolic sites and routes of bicyclol in human for the first time, which may be helpful for rational combined application in clinic and further study of metabolites-related efficacy or toxicity.

ANHIR: Automatic Non-Rigid Histological Image Registration Challenge.

Automatic Non-rigid Histological Image Registration (ANHIR) challenge was organized to compare the performance of image registration algorithms on several kinds of microscopy histology images in a fair and independent manner. We have assembled 8 datasets, containing 355 images with 18 different stains, resulting in 481 image pairs to be registered. Registration accuracy was evaluated using manually placed landmarks. In total, 256 teams registered for the challenge, 10 submitted the results, and 6 participated in the workshop. Here, we present the results of 7 well-performing methods from the challenge together with 6 well-known existing methods. The best methods used coarse but robust initial alignment, followed by non-rigid registration, used multiresolution, and were carefully tuned for the data at hand. They outperformed off-the-shelf methods, mostly by being more robust. The best methods could successfully register over 98% of all landmarks and their mean landmark registration accuracy (TRE) was 0.44% of the image diagonal. The challenge remains open to submissions and all images are available for download.

Drug interaction study of flavonoids toward OATP1B1 and their 3D structure activity relationship analysis for predicting hepatoprotective effects.

Organic anion transporting polypeptide 1B1 (OATP1B1), a liver-specific uptake transporter, was associated with drug induced liver injury (DILI). Screening and identifying potent OATP1B1 inhibitors with little toxicity is of great value in reducing OATP1B1-mediated DILI. Flavonoids are a group of polyphenols ubiquitously present in vegetables, fruits and herbal products, some of them were reported to produce transporter-mediated DDI. Our objective was to investigate potential inhibitors of OATP1B1 from 99 flavonoids, and to assess the hepatoprotective effects on bosentan induced liver injury. Eight flavonoids, including biochanin A, hispidulin, isoliquiritigenin, isosinensetin, kaempferol, licochalcone A, luteolin and sinensetin exhibited significant inhibition (>50 %) on OATP1B1 in OATP1B1-HEK293 cells, which reduced the OATP1B1-mediated influx of methotrexate, accordingly decreased its cytotoxicity in OATP1B1-HEK293 cells and increased its AUC0-t in different extents in rats, from 28.27%-82.71 %. In bosentan-induced rat liver injury models, 8 flavonoids reduced the levels of serum total bile acid (TBA) and the liver concentration of bosentan in different degrees. Among them, kaempferol decreased the concentration most significantly, by 54.17 %, which indicated that flavonoids may alleviate bosentan-induced liver injury by inhibiting OATP1B1-mediated bosentan uptake. Furthermore, the pharmacophore model indicated the hydrogen bond acceptors and hydrogen bond donors may play critical role in the potency of flavonoids inhibition on OATP1B1. Taken together, our findings would provide helpful information for predicting the potential risks of flavonoid-containing food/herb-drug interactions in humans and alleviating bosentan -induced liver injury by OATP1B1 regulation.

Simultaneous determination of a promising anti-brain tumor agent CAT3 and its two major metabolites in mouse plasma and brain by a LC-MS/MS method.

A rapid and reproducible method with high selectivity was developed for simultaneous determination of a promising anti-brain tumor agent CAT3 and its two metabolites PF403 and GLU-PF403 in mouse plasma and brain. An economic deproteinization with septuple acetonitrile (v/v) was applied to pretreat the samples in this study. All analytes were well retained and separated on a CAPCELL CORE PC (2.7 µm, 2.1 mm I.D. × 150 mm, SHISEIDO Technologies) column with an eluting solvent of acetonitrile /water containing 0.1 % formic acid (v/v) at the flow rate of 0.2 mL per minute. The detection was carried out on a Q Exactive high resolution mass spectrometer equipped with a HESI ion source in parallel reaction monitoring (PRM) mode. The corresponding transitions for quantitation were 434.23→ 70.07 for CAT3, 350.17→70.07 for PF403, 526.21→70.07 for GLU-PF403, 364.19→70.07 for IS-1 and 625.18→317.07 for IS-2, respectively. A well-linear fit curve was achieved among the range of 0.1∼50 ng/mL for CAT3, 0.2∼100 ng/mL for PF403 and 2.5∼600 ng/mL for GLU-PF403 both in mouse plasma and brain homogenate. The intra-/inter-day accuracies of three analytes were within ±14.5 % and precisions were below to 13.44 %. The mean values of recovery of three compounds in mouse plasma and brain homogenate were among 98.06 ∼ 118.63 % and 81.04∼108.69 %. The analytes in NaF-treated ice cold blood of mouse was stable within tested 30 min. Plasma and brain homogenate samples had no obvious changes during all storage, sample treatment and analytic process of mouse plasma sample. The reproducible and reliable method was well employed to the research of CAT3 pharmacokinetic characteristics in mouse plasma and brain after a single intragastric administration at dose of 10 mg/kg.

Unsupervised 3D End-to-End Medical Image Registration With Volume Tweening Network.

3D medical image registration is of great clinical importance. However, supervised learning methods require a large amount of accurately annotated corresponding control points (or morphing), which are very difficult to obtain. Unsupervised learning methods ease the burden of manual annotation by exploiting unlabeled data without supervision. In this article, we propose a new unsupervised learning method using convolutional neural networks under an end-to-end framework, Volume Tweening Network (VTN), for 3D medical image registration. We propose three innovative technical components: (1) An end-to-end cascading scheme that resolves large displacement; (2) An efficient integration of affine registration network; and (3) An additional invertibility loss that encourages backward consistency. Experiments demonstrate that our algorithm is 880x faster (or 3.3x faster without GPU acceleration) than traditional optimization-based methods and achieves state-of-the-art performance in medical image registration.

Heterotropic activation of flavonoids on cytochrome P450 3A4: A case example of alleviating dronedarone-induced cytotoxicity.

The clinical drug-drug interactions mediated by heterotropic activation on cytochrome P450 (CYP450) kinetics, especially CYP3A4, have received wide concern in recent years. Flavonoids, a group of important natural substances with various pharmacological activities, distribute widely among vegetables, fruits and herbs. The frequent and numerous uses of flavonoids may increase the risk of food/herb-drug interactions. However, little is known about activation effects of flavonoids on CYP3A4. The aim of this study was to investigate activation of CYP3A4 by flavonoids, explore the molecular mechanism, and assess the biological effects on dronedarone (DND) induced toxicity. The results showed that flavone, tangeretin, sinensetin and 6-hydroxyflavone increased the cell viability by decreasing DND-induced cytotoxicity. These four flavonoids could activate the metabolism of DND in hamster pharmacokinetics study. Furthermore, both molecular docking and circular dichroism analysis partially illustrated the molecular mechanism of heterotropic activation. Finally, the pharmacophore model suggested B aromatic ring, hydrophobic groups at 7-position and hydrogen bond acceptors at 4-position may play a vital role in activation of flavonoids on CYP3A4. Taken together, our findings would provide useful information for predicting the potential risks of flavonoid-containing food/herb-drug interactions in humans.

Exploration on the Approaches of Diverse Sedimentations in Polyphenol Solutions: An Integrated Chain of Evidence Based on the Physical Phase, Chemical Profile, and Sediment Elements.

Triphala is a famous herbal formula originated in Asia and is popular in America. Due to the high abundance of polyphenols, its oral liquid is unstable and easy to cause precipitate, which results in the loss of activities. However, complex composition and unclear precipitation mechanism hinders the improvement of stability. In this study, the accumulation of precipitation in the storage and its effect on activity were investigated. Then, an integrated chain of evidence was proposed based on the physical phase, chemical profile, and sediment elements. The results showed that antioxidant activity decreased from IC50 115 to 146 µl before and after 90 days of storage, and the anti-fatigue activity decreased from 30.54 to 28.47 min. Turbiscan Lab Expert observed that particle size increased from 106 to 122 nm, and the turbiscan stability index increased from 0 to 14, which indicated that its stability is continuously decreasing. High performance liquid chromatography (HPLC) fingerprint coupled with multivariate statistical analysis identified that these chemical markers changed significantly, such as gallic acid, catechins, and ellagic acid. Loss of catechins tends to be involved in the formation of phlobaphene precipitation. The fact that the new-born ellagic acid in precipitation (0.47 mg/ml) is significantly higher than that reduced in solution (0.25 mg/ml) indicates that it is not only derived from colloid aging. Microscopic observation combined with energy spectrum analysis further confirmed the existence of the multi-precipitates. The crystalline precipitate is ellagic acid, and the other is phlobaphene. In conclusion, based on the evidence chain analysis, this study revealed a systematic change of the whole polyphenol solution system. It provides a novel perspective to understand the sedimentation formation of polyphenol solution, which is an important theoretical contribution to the preparation of polyphenol solutions.

Evaluation of inhibitory effects of flavonoids on breast cancer resistance protein (BCRP): From library screening to biological evaluation to structure-activity relationship.

Flavonoids are a group of polyphenols ubiquitously present in vegetables, fruits and herbal products, despite various known pharmacological activities, few researches have been done about the interaction of flavonoids with breast cancer resistance protein (BCRP). The present study was designed to investigate the inhibitory effects of 99 flavonoids on BCRP in vitro and in vivo and to clarify structure-activity relationships of flavonoids with BCRP. Eleven flavonoids, including amentoflavone, apigenin, biochanin A, chrysin, diosimin, genkwanin, hypericin, kaempferol, kaempferide, licochalcone A and naringenin, exhibited significant inhibition (>50%) on BCRP in BCRP-MDCKII cells, which reduced the BCRP-mediated efflux of doxorubicin and temozolomide, accordingly increased their cytotoxicity. In addition, co-administration of mitoxantrone with the 11 flavonoids increased the AUC0-t of mitoxantrone in different extents in rats. Among them, chrysin increased the AUC0-t most significantly, by 81.97%. Molecular docking analysis elucidated the inhibition of flavonoids on BCRP might be associated with Pi-Pi stacked interactions and/or potential Pi-Alkyl interactions, but not conventional hydrogen bonds. The pharmacophore model indicated the aromatic ring B, hydrophobic groups and hydrogen bond acceptors may play critical role in the potency of flavonoids inhibition on BCRP. Thus, our findings would provide helpful information for predicting the potential risks of flavonoid-containing food/herb-drug interactions in humans.

Seeing the Unseen of the Combination of Two Natural Resins, Frankincense and Myrrh: Changes in Chemical Constituents and Pharmacological Activities.

For the treatment of diseases, especially chronic diseases, traditional natural drugs have more effective therapeutic advantages because of their multi-target and multi-channel characteristics. Among many traditional natural medicines, resins frankincense and myrrh have been proven to be effective in the treatment of inflammation and cancer. In the West, frankincense and myrrh have been used as incense in religious and cultural ceremonies since ancient times; in traditional Chinese and Ayurvedic medicine, they are used mainly for the treatment of chronic diseases. The main chemical constituents of frankincense and myrrh are terpenoids and essential oils. Their common pharmacological effects are anti-inflammatory and anticancer. More interestingly, in traditional Chinese medicine, frankincense and myrrh have been combined as drug pairs in the same prescription for thousands of years, and their combination has a better therapeutic effect on diseases than a single drug. After the combination of frankincense and myrrh forms a blend, a series of changes take place in their chemical composition, such as the increase or decrease of the main active ingredients, the disappearance of native chemical components, and the emergence of new chemical components. At the same time, the pharmacological effects of the combination seem magically powerful, such as synergistic anti-inflammation, synergistic anticancer, synergistic analgesic, synergistic antibacterial, synergistic blood-activation, and so on. In this review, we summarize the latest research on the main chemical constituents and pharmacological activities of these two natural resins, along with chemical and pharmacological studies on the combination of the two.