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Zeolitic imidazolate frameworks derived Fe 1 -N-C catalysts with isolated single iron atoms have been synthesized and applied for selective ammoxidation reactions. For the preparation of the different Fe-based materials, benzylamine as an additive proved to be essential to tune the morphology and size of ZIFs resulting in uniform and smaller particles, which allow stable atomically dispersed Fe–N 4 active sites. The optimal catalyst Fe 1 -N-C achieves an efficient synthesis of various aryl, heterocyclic, allylic, and aliphatic nitriles from alcohols in water under very mild conditions. With its chemoselectivity, recyclability, high efficiency under mild conditions this new system complements the toolbox of catalysts for nitrile synthesis, which are important intermediates with many applications in life sciences and industry. Exploring benign and green methodologies for the synthesis of functionalized nitriles continues to attract the interest of academic and industrial chemists. Here, the authors efficiently synthesize various aryl, heterocyclic, allylic, and aliphatic nitriles from alcohols in water under very mild conditions using zeolitic imidazolate frameworks derived Fe 1 -N-C catalysts.

Head and neck cancer patients are vulnerable to various psychological complications due to the effects of both cancer itself and cancer treatment on patients' appearance and physical well-being. Nevertheless, few data have been obtained on effective psychosocial interventions that could protect this group of cancer patients' psychological well-being. Therefore, this three-armed, parallel-group, double-blind, randomized control trial (RCT) aims to evaluate and compare the effects of acceptance and commitment therapy (ACT) and mindfulness-based stress reduction (MBSR) on positive psychology (such as posttraumatic growth [PTG], hope, and optimism), quality of life (QoL), and psychological complications (depression, anxiety, and experiential avoidance) among newly diagnosed head and neck cancer patients. This RCT will target newly diagnosed head and neck cancer patients who have been treated only with surgery or who have not yet received any treatment. In total, 120 patients who meet all of the study's inclusion criteria and none of its exclusion criteria will be randomly assigned into three groups-an ACT group, an MBSR group, and a treatment-as-usual control group-at a 1:1:1 allocation ratio. Participants in the two intervention groups (the ACT and MBSR groups) will undergo an eight-week group intervention program. During this program, each intervention will comprise eight modules based on ACT and MBSR, respectively. Outcome assessments will be performed across a three-point timeline, including before the intervention (t0), immediately after the psychosocial intervention at eight weeks (t1), and six months after the intervention (t2). The primary outcome that will be assessed during this RCT is PTG. Meanwhile, the secondary outcomes that will be evaluated in this study are such as QoL, hope, optimism, depression, anxiety, and experiential avoidance. NCT04800419 (ClinicalTrials.gov). Registered on March 16, 2021.

Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .

The purpose of this review is to provide medical researchers, especially those without a bioinformatics background, with an easy-to-understand summary of the concepts and technologies used in microbiome research. First, we define primary concepts such as microbiota, microbiome, and metagenome. Then, we discuss study design schemes, the methods of sample size calculation, and the methods for improving the reliability of research. We emphasize the importance of negative and positive controls in this section. Next, we discuss statistical analysis methods used in microbiome research, focusing on problems with multiple comparisons and ways to compare β-diversity between groups. Finally, we provide step-by-step pipelines for bioinformatics analysis. In summary, the meticulous study design is a key step to obtaining meaningful results, and appropriate statistical methods are important for accurate interpretation of microbiome data. The step-by-step pipelines provide researchers with insights into newly developed bioinformatics analysis methods.

Ascaridoids are among the commonest groups of zooparasitic nematodes (roundworms) and occur in the alimentary canal of all major vertebrate groups, including humans. They have an extremely high diversity and are of major socio-economic importance. However, their evolutionary history remains poorly known. Herein, we performed a comprehensive phylogenetic analysis of the Ascaridoidea. Our results divided the Ascaridoidea into six monophyletic major clades, i.e., the Heterocheilidae, Acanthocheilidae, Anisakidae, Ascarididae, Toxocaridae, and Raphidascarididae, among which the Heterocheilidae, rather than the Acanthocheilidae, represents the sister clade to the remaining ascaridoids. The phylogeny was calibrated using an approach that involves time priors from fossils of the co-evolving hosts, and dates the common ancestor of the Ascaridoidea back to the Early Carboniferous (approximately 360.47–325.27 Ma). The divergence dates and ancestral host types indicated by our study suggest that members of the Ascaridoidea first parasitized terrestrial tetrapods, and subsequently, extended their host range to elasmobranchs and teleosts. We also propose that the fundamental terrestrial-aquatic switches of these nematodes were affected by changes in sea-level during the Triassic to the Early Cretaceous.

This study reports the successful synthesis of core-shell microparticles utilizing coaxial electrospray techniques, with zeolitic imidazolate framework-8 (ZIF-8) encapsulating rhodamine B (RhB) in the core and a phase change material (PCM) shell composed of a eutectic mixture of lauric acid (LA) and stearic acid (SA). ZIF-8 is well-recognized for its pH-responsive degradation and biocompatibility, making it an ideal candidate for targeted drug delivery. The LA-SA PCM mixture, with a melting point near physiological temperature (39 °C), enables temperature-triggered drug release, enhancing therapeutic precision. The structural properties of the microparticles were extensively characterized through scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Drug release studies revealed a dual-stimuli response, where the release of RhB was significantly influenced by both temperature and pH. Under mildly acidic conditions (pH 4.0) at 40 °C, a rapid and complete release of RhB was observed within 120 h, while at 37 °C, the release rate was notably slower. Specifically, the release at 40 °C was 79% higher than at 37 °C, confirming the temperature sensitivity of the system. Moreover, at physiological pH (7.4), minimal drug release occurred, demonstrating the system’s potential for minimizing premature drug release under neutral conditions. This dual-stimuli approach holds promise for improving therapeutic outcomes in cancer treatment by enabling precise control over drug release in response to both pH and localized hyperthermia, reducing off-target effects and improving patient compliance.

Drug-resistant mycobacterial infections are a serious global health challenge, leading to high mortality and socioeconomic burdens in developing countries worldwide. New innovative approaches, from identification of new targets to discovery of novel chemical scaffolds, are urgently needed. Recently, energy metabolism in mycobacteria, in particular the oxidative phosphorylation pathway, has emerged as an object of intense microbiological investigation and as a novel target pathway in drug discovery. New classes of antibacterials interfering with elements of the oxidative phosphorylation pathway are highly active in combating dormant or latent mycobacterial infections, with a promise of shortening tuberculosis chemotherapy. The regulatory approval of the ATP synthase inhibitor bedaquiline and the discovery of Q203, a candidate drug targeting the cytochrome bc 1 complex, have highlighted the central importance of this new target pathway. In this review, we discuss key features and potential applications of inhibiting energy metabolism in our quest for discovering potent novel and sterilizing drug combinations for combating tuberculosis. We believe that the combination of drugs targeting elements of the oxidative phosphorylation pathway can lead to a completely new regimen for drug-susceptible and multidrug-resistant tuberculosis.

Mutations in the proline-rich transmembrane protein 2 （PRRT2） are associated with paroxysmal kinesigenic dys- kinesia （PKD） and several other paroxysmal neurological diseases, but the PRRT2 function and pathogenic mecha- nisms remain largely obscure. Here we show that PRRT2 is a presynaptic protein that interacts with components of the SNARE complex and downregulates its formation. Loss-of-function mutant mice showed PKD-like phenotypes triggered by generalized seizures, hyperthermia, or optogenetic stimulation of the cerebellum. Mutant mice with spe- cific PRRT2 deletion in cerebellar granule cells （GCs） recapitulate the behavioral phenotypes seen in Prrt2-null mice. Furthermore, recording made in cerebellar slices showed that optogenetic stimulation of GCs results in transient elevation followed by suppression of Purkinje cell firing. The anticonvulsant drug carbamazepine used in PKD treat- ment also relieved PKD-like behaviors in mutant mice. Together, our findings identify PRRT2 as a novel regulator of the SNARE complex and provide a circuit mechanism underlying the PRRT2-related behaviors.

Hypernetworks, or hypernets for short, are neural networks that generate weights for another neural network, known as the target network. They have emerged as a powerful deep learning technique that allows for greater flexibility, adaptability, dynamism, faster training, information sharing, and model compression. Hypernets have shown promising results in a variety of deep learning problems, including continual learning, causal inference, transfer learning, weight pruning, uncertainty quantification, zero-shot learning, natural language processing, and reinforcement learning. Despite their success across different problem settings, there is currently no comprehensive review available to inform researchers about the latest developments and to assist in utilizing hypernets. To fill this gap, we review the progress in hypernets. We present an illustrative example of training deep neural networks using hypernets and propose categorizing hypernets based on five design criteria: inputs, outputs, variability of inputs and outputs, and the architecture of hypernets. We also review applications of hypernets across different deep learning problem settings, followed by a discussion of general scenarios where hypernets can be effectively employed. Finally, we discuss the challenges and future directions that remain underexplored in the field of hypernets. We believe that hypernetworks have the potential to revolutionize the field of deep learning. They offer a new way to design and train neural networks, and they have the potential to improve the performance of deep learning models on a variety of tasks. Through this review, we aim to inspire further advancements in deep learning through hypernetworks.