Visible-light-enabled stereoselective synthesis of functionalized cyclohexylamine derivatives via [4 + 2] cycloadditions.

An unprecedented intermolecular [4 + 2] cycloaddition of benzocyclobutylamines with α-substituted vinylketones, enabled by photoredox catalysis, has been developed. The current method enables facile access to highly functionalized cyclohexylamine derivatives that were otherwise inaccessible, in moderate to good yields with excellent diastereoselectivities. This protocol has some excellent features, such as full atom economy, good functional-group compatibility, mild reaction conditions, and an overall redox-neutral process. Additionally, an asymmetric version of this cycloaddition was preliminarily investigated via the incorporation of a chiral phosphoric acid (CPA), and moderate to good enantioselectivity could be effectively realized with excellent diastereoselectivity. Synthetic applications were demonstrated via a scale-up experiment and elaborations to access amino alcohol and cyclobutene derivatives. Based on the results of control experiments, a reasonable reaction mechanism was proposed to elucidate the reaction pathway.

SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction.

Drug combination therapies are well-established strategies for the treatment of cancer with low toxicity and fewer adverse effects. Computational drug synergy prediction approaches can accelerate the discovery of novel combination therapies, but the existing methods do not explicitly consider the key role of important substructures in producing synergistic effects. To this end, we propose a significant substructure-aware anticancer drug synergy prediction method, named SDDSynergy, to adaptively identify critical functional groups in drug synergy. SDDSynergy splits the task of predicting drug synergy into predicting the effect of individual substructures on cancer cell lines and highlights the impact of important substructures through a novel drug-cell line attention mechanism. And a substructure pair attention mechanism is incorporated to capture the information on internal substructure pairs interaction in drug combinations, which aids in predicting synergy. The substructures of different sizes and shapes are directly obtained from the molecular graph of the drugs by multilayer substructure information passing networks. Extensive experiments on three real-world data sets demonstrate that SDDSynergy outperforms other state-of-the-art methods. We also verify that many of the novel drug combinations predicted by SDDSynergy are supported by previous studies or clinical trials through an in-depth literature survey.

Minimally Invasive Resection of a Foraminal Lumbar Root Schwannoma.

Background and Importance: This video article reports a rare case of a right L5/S1 foraminal root schwannoma that presents the natural course of the disease, imaging findings, treatment protocol, operative procedure, and highlights some of the possible surgical complications. Clinical Presentation: Magnetic resonance imaging (MRI) revealed an intradural, extramedullary, well-enhanced mass at the right L5/S1 level. The operative procedure involved a right minimal L5/S1 laminotomy/foraminotomy posteriorly to open the right L5 root. The facet joints were preserved to prevent spinal instability. The tumor was located along the root after opening the right L5/S1 foramen. Intraoperative electromyography (IOM) was conducted to detect any nerve injury in the patient. After opening the dura, the tumor was carefully separated from the normal root nerve under IOM monitoring. The mass was removed piece-by-piece using mini-forceps. Conclusion: Histopathological examination confirmed the diagnosis of a schwannoma. The patient recovered without incident after surgery with minimal soreness and numbness in the right leg.

A simple, organized web-based system improved the transfer efficiency and patient outcomes for endovascular thrombectomy in regional stroke network.

BACKGROUND: Endovascular thrombectomy (EVT) is a time-sensitive treatment for acute ischemic stroke with large vessel occlusion. To optimize transfer efficiency, a web-based platform was introduced in the Tainan Stroke Network (TSN). We assessed its application and effectiveness in regional stroke care. METHOD: This new web-based platform containing a questionnaire-style interface was introduced on October 1, 2021. To assess the transfer efficiency and patient outcomes, acute stroke patients transferred from PSCs to CSC for EVT from April 01, 2020, to December 30, 2022, were enrolled. The patients were classified into the traditional transferal pathway (TTP) group and the new transferal pathway (NTP) group depending on mode of transfer. Patient characteristics, time segments after stroke onset and outcome were compared between groups. RESULT: A total of 104 patients were enrolled, with 77 in the TTP group and 27 in the NTP group. Compared to the TTP group, the NTP group had a significantly shorter onset-to-CSC door time (TTP vs. NTP: 267 vs. 198 min; p = 0.041) and a higher EVT rate (TTP vs. NTP: 18.2% vs. 48.1%, p = 0.002). Among EVT patients, those in the NTP group had a significantly shorter CSC door-to-puncture time (TTP vs. NTP: 131.5 vs. 110 min; p = 0.029). The NTP group had a higher rate of good functional outcomes at 3 months (TTP vs. NTP: 21% vs. 61.5%; p = 0.034). CONCLUSION: This new web-based EVT transfer system provides notable improvements in clinical outcomes, transfer efficiency, and EVT execution for potential EVT candidates without markedly changing the regional stroke care paradigm.

Molecular Targets and Mechanisms of Hedyotis diffusa Willd. for Esophageal Adenocarcinoma Treatment Based on Network Pharmacology and Weighted Gene Co-expression Network Analysis.

BACKGROUND: Hedyotis diffusa Willd. (HDW) is a common anticancer herbal medicine in China, and its therapeutic effectiveness has been demonstrated in a range of cancer patients. There is no consensus about the therapeutic targets and molecular mechanisms of HDW, which contains many active ingredients. AIM: To clarify the mechanism of HDW for esophageal adenocarcinoma (EAC), we utilized network pharmacology and weighted gene co-expression network analysis methods (WGCNA). METHODS: The gene modules that were linked with the clinical features of EAC were obtained through the WGCNA method. Then, the potential target genes were retrieved through the network pharmacology method in order to determine the targets of the active components. After enrichment analysis, a variety of signaling pathways with significant ratios of target genes were found, including regulation of trans-synaptic signaling, neuroactive ligand-receptor interaction and modulation of chemical synaptic transmission. By means of protein-protein interaction (PPI) network analysis, we have successfully identified the hub genes, which were AR, CNR1, GRIK1, MAPK10, MAPT, PGR and PIK3R1. RESULT: Our study employed molecular docking simulations to evaluate the binding affinity of the active components with the hub gene. The identified active anticancer constituents in HDW are scopoletol, quercetin, ferulic acid, coumarin, and trans-4-methoxycinnamyl alcohol. CONCLUSION: Our findings shed light on the molecular underpinnings of HDW in the treatment of EAC and hold great promise for the identification of potential HDW compounds and biomarkers for EAC therapy.

SSF-DDI: a deep learning method utilizing drug sequence and substructure features for drug-drug interaction prediction.

BACKGROUND: Drug-drug interactions (DDI) are prevalent in combination therapy, necessitating the importance of identifying and predicting potential DDI. While various artificial intelligence methods can predict and identify potential DDI, they often overlook the sequence information of drug molecules and fail to comprehensively consider the contribution of molecular substructures to DDI. RESULTS: In this paper, we proposed a novel model for DDI prediction based on sequence and substructure features (SSF-DDI) to address these issues. Our model integrates drug sequence features and structural features from the drug molecule graph, providing enhanced information for DDI prediction and enabling a more comprehensive and accurate representation of drug molecules. CONCLUSION: The results of experiments and case studies have demonstrated that SSF-DDI significantly outperforms state-of-the-art DDI prediction models across multiple real datasets and settings. SSF-DDI performs better in predicting DDI involving unknown drugs, resulting in a 5.67% improvement in accuracy compared to state-of-the-art methods.

Visible-Light-Promoted C(sp3)-C(sp3) Cross-Coupling of Amino Acids and Aryl Trifluoromethyl Ketones Through Simultaneous Decarboxylation and Defluorination.

A photoredox-catalyzed approach for the difluoroalkylation of amino acids was achieved through simultaneous decarboxylation and defluorination processes. This innovative protocol employs commonly available amino acids and trifluoroacetophenones as the primary starting materials, eliminating the necessity for preactivation. This strategy has enabled the synthesis of several difluoroketone functionalized amines in moderate to impressive yields. These synthesized compounds are presented as foundational molecules for subsequent modification. The underlying mechanism for the transformation is anchored in a single electron transfer (SET) radical pathway.

Clinical Outcome and Safety of Lumboperitoneal Shunt in the Treatment of Non-Obstructive Hydrocephalus.

Objective: This study aimed to evaluate the functional outcomes of lumboperitoneal (LP) shunt for the treatment of non-obstructive hydrocephalus. Methods: We retrospectively studied the clinical surgical results of 172 adult patients with hydrocephalus who underwent LP shunt surgery between June 2014 and June 2019. Data regarding the following were collected: pre- and postoperative symptom status, third ventricle width changes, Evans index, and postoperative complications. Additionally, the baseline and follow-up Glasgow Coma Scale (GCS) score, Glasgow Outcome Scale (GOS), and Modified Rankin Scale (mRS) scores were investigated. All patients were followed up for ≥12 months using clinical interview and braining imaging using computed tomography (CT) scan or magnetic resonance imaging (MRI). Results: Majority of patients presented with normal pressure hydrocephalus as the etiology of their disease (48.8%), followed by cardiovascular accident (28.5%), trauma (19.7%), and brain tumor (3%). The mean GCS, GOS, and mRS improved postoperatively. The average period from symptomatic onset to surgery was 402 days. The average width of the third ventricle on CT scan or MRI was 11.43 mm preoperatively and 10.8 mm postoperatively (P<0.001). The Evans index improved from 0.258 to 0.222 after operation. The symptomatic improvement score was 7.0, with a complication rate of 7%. Conclusion: Significant improvement was observed in the functional score and brain image after LP shunt placement. Moreover, the satisfaction with symptomatic improvement after surgery remains high. LP shunt operation is a viable alternative in the treatment of non-obstructive hydrocephalus due to the low complication rate, fast recovery, and high satisfaction.

B-LBConA: a medical entity disambiguation model based on Bio-LinkBERT and context-aware mechanism.

BACKGROUND: The main task of medical entity disambiguation is to link mentions, such as diseases, drugs, or complications, to standard entities in the target knowledge base. To our knowledge, models based on Bidirectional Encoder Representations from Transformers (BERT) have achieved good results in this task. Unfortunately, these models only consider text in the current document, fail to capture dependencies with other documents, and lack sufficient mining of hidden information in contextual texts. RESULTS: We propose B-LBConA, which is based on Bio-LinkBERT and context-aware mechanism. Specifically, B-LBConA first utilizes Bio-LinkBERT, which is capable of learning cross-document dependencies, to obtain embedding representations of mentions and candidate entities. Then, cross-attention is used to capture the interaction information of mention-to-entity and entity-to-mention. Finally, B-LBConA incorporates disambiguation clues about the relevance between the mention context and candidate entities via the context-aware mechanism. CONCLUSIONS: Experiment results on three publicly available datasets, NCBI, ADR and ShARe/CLEF, show that B-LBConA achieves a signifcantly more accurate performance compared with existing models.

Enantio- and Diastereoselective De Novo Synthesis of 3-Substituted Proline Derivatives via Cooperative Photoredox/Brønsted Acid Catalysis and Epimerization.

Herein, a novel strategy for the catalytic asymmetric synthesis of enantioenriched 3-cis- and 3-trans-substituted prolines has been successfully established via an unprecedented cascade radical addition/cyclization enabled by synergistic photoredox/Brønsted acid catalysis and subsequent base-assisted epimerization. The current protocol provides a unique de novo access to all four stereoisomers of 3-substituted prolines which are not readily achieved via currently established methods. This methodology could be further extended to the asymmetric synthesis of the full complement of stereoisomers of 3-substituted pipecolinic acids.

Predicting Drug-Target Interaction Via Self-Supervised Learning.

Recent advances in graph representation learning provide new opportunities for computational drug-target interaction (DTI) prediction. However, it still suffers from deficiencies of dependence on manual labels and vulnerability to attacks. Inspired by the success of self-supervised learning (SSL) algorithms, which can leverage input data itself as supervision,we propose SupDTI, a SSL-enhanced drug-target interaction prediction framework based on a heterogeneous network (i.e., drug-protein, drug-drug, and protein-protein interaction network; drug-disease, drug-side-effect, and protein-disease association network; drug-structure and protein-sequence similarity network). Specifically, SupDTI is an end-to-end learning framework consisting of five components. First, localized and globalized graph convolutions are designed to capture the nodes' information from both local and global perspectives, respectively. Then, we develop a variational autoencoder to constrain the nodes' representation to have desired statistical characteristics. Finally, a unified self-supervised learning strategy is leveraged to enhance the nodes' representation, namely, a contrastive learning module is employed to enable the nodes' representation to fit the graph-level representation, followed by a generative learning module which further maximizes the node-level agreement across the global and local views by learning the probabilistic connectivity distribution of the original heterogeneous network. Experimental results show that our model can achieve better prediction performance than state-of-the-art methods.

Effect of load training on physical fitness in running athletes / Efeito do treinamento de corrida com carga sobre a aptidão física em atletas de corrida / Efecto del entrenamiento de carrera con carga sobre la aptitud física en corredores

ABSTRACT Introduction: Cardiopulmonary capacity and sports endurance are very important for running athletes. Objective: Study whether running with load training can improve physical fitness, focusing on running athletes' cardiopulmonary capacity and exercise endurance. Methods: 36 professional athletes were selected. One group was trained with normal running and the other group was trained with running with a weight load. Results: In the weight-bearing training group, FVC went from 5.13 ± 0.20 to 5.68 ± 1.25, FEV1 went from 4.38 ± 0.33 to 4.56 ± 0.35, PEF went from 8.27 ± 0.98 to 8.88 ± 1.02, ERV went from 1.44 ± 0.37 to 1.68 ± 0.34, IVC went from 4.91 ± 0.45 to 5.28 ± 1.46, VT went from 1.10 ± 0.25 to 1.42 ± 1.26, systolic blood pressure varied from 115.16 ± 8.18 to 117.02 ± 5.47, diastolic blood pressure varied from 78.58 ± 8.97 78.16 ± 7.42, BPM varied from 73.42 ± 9.27 to 73.52 ± 9.02, serum lactic acid was from 1.36 ± 0.28 to 1.31 ± 0.27, and serum creatine kinase was from 21.85 ± 59.02 to 208.11 ± 49.20. Conclusion: Load running training positively impacts athletes' physical fitness and may be added to improve athletes' cardiorespiratory capacity and exercise endurance. Level of evidence II; Therapeutic studies - investigation of treatment outcomes.

RESUMO Introdução: A capacidade cardiopulmonar e a resistência esportiva são muito importantes para os atletas de corrida. Objetivo: Estudar se o treinamento de corrida com carga pode melhorar a aptidão física, com enfoque na capacidade cardiopulmonar e a resistência ao exercício dos atletas de corrida. Métodos: Foram selecionados 36 atletas profissionais. Um grupo foi treinado com corrida normal e o outro grupo foi treinado com corrida com carga de peso. Resultados: No grupo de treinamento com rolamentos de peso, FVC passou de 5,13 ± 0.20 para 5,68 ± 1,25, FEV1 passou de 4,38 ± 0,33 para 4,56 ± 0,35, PEF passou de 8,27 ± 0,98 para 8,88 ± 1,02, ERV passou de 1,44 ± 0,37 para 1,68 ± 0,3431, IVC passou de 4,91 ± 0,45 para 5,28 ± 1,46, VT passou de 1,10 ± 0,25 para 1,42 ± 1,26, pressão arterial sistólica variou de 115,16 ± 8,18 para 117,02 ± 5,47, pressão arterial diastólica variou de 78,58 ± 8,97 78,16 ± 7,42, BPM variou de 73,42 ± 9,27 para 73,52 ± 9,02, o valor de ácido lático sanguíneo foi de 1,36 ± 0,28 para 1,31 ± 0,27, e a creatina quinase sérica foi de 219,85 ± 59,02 para 208,11 ± 49,20. Conclusão: O treinamento de corrida com carga impacta positivamente a aptidão física dos atletas, podendo ser acrescido para melhorar a capacidade cardiorrespiratória e a resistência ao exercício dos atletas. Nível de evidência II; Estudos terapêuticos - investigação dos resultados do tratamento.

RESUMEN Introducción: La capacidad cardiopulmonar y la resistencia al deporte son muy importantes para los atletas que corren. Objetivo: Estudiar si correr con entrenamiento de carga puede mejorar la forma física, centrándose en la capacidad cardiopulmonar y la resistencia al ejercicio en atletas corredores. Métodos: Se seleccionaron 36 atletas profesionales. Un grupo fue entrenado con carrera normal y el otro con carrera con carga de peso. Resultados: En el grupo de entrenamiento con carga de peso, la FVC pasó de 5,13 ± 0,20 a 5,68 ± 1,25, el FEV1 pasó de 4,38 ± 0,33 a 4,56 ± 0,35, el PEF pasó de 8,27 ± 0. 98 a 8,88 ± 1,02, ERV pasó de 1,44 ± 0,37 a 1,68 ± 0,34, IVC pasó de 4,91 ± 0,45 a 5,28 ± 1,46, VT pasó de 1,10 ± 0,25 a 1,42 ± 1. 2645, la presión arterial sistólica varió de 115,16 ± 8,18 a 117,021 ± 5,47, PPM varió de 78,58 ± 8,97 a 78,16 ± 7,42, las pulsaciones por minuto variaron de 73,42 ± 9,27 a 73,52 ± 9,02, el valor de ácido láctico en sangre fue de 1,36 ± 0,28 a 1,31 ± 0,27, y la creatina quinasa sérica fue de 219,85 ± 59,02 a 208,11 ± 49,20. Conclusión: El entrenamiento de carrera con carga influye positivamente en la forma física de los atletas, y puede añadirse para mejorar la capacidad cardiorrespiratoria y la resistencia al ejercicio de los atletas. Nivel de evidencia II; Estudios terapéuticos - investigación de los resultados del tratamiento.

Intradural lumbar disc herniation: A case report and literature review.

Study design and objection: Intradural disc herniation is a unusual disease associated with spinal surgery. The definitive diagnosis of intradural herniation depends on intraoperative findings. Summary of background data: We present the case of a 63-year-old woman with backache and left sciatica radiation for more than two months. The L2/3 laminectomy and discectomy were performed after magnetic resonance imaging (MRI) study; however, no disc rupture was noted during surgery. Follow-up lumbar spine MRI revealed one large, ruptured disc. The patient underwent revision surgery with durotomy. The large intradural disc was found and removed piece by piece. Methods Results and Conclusions: Intradural disc herniation, especially large herniation, is hard to diagnose specifically despite the progression of neuroradiologic imaging techniques. A durotomy procedure should be considered if there is a missing ruptured disc or a palpable intradural mass during surgery.

Easily automated radiosynthesis of [18F]P10A-1910 and its clinical translation to quantify phosphodiesterase 10A in human brain.

Our previous work showed that [18F]P10A-1910 was a potential radioligand for use in imaging phosphodiesterase 10A (PDE10A). Specifically, it had high brain penetration and specific binding that was demonstrated in both rodents and non-human primates. Here, we present the first automatic cGMP-level production of [18F]P10A-1910 and translational PET/MRI study in living human brains. Successful one-step radiolabeling of [18F]P10A-1910 on a GE TRACERlab FX2N synthesis module was realized via two different methods. First, formulated [18F]P10A-1910 was derived from heating spirocyclic iodonium ylide in a tetra-n-butyl ammonium methanesulfonate solution. At the end of synthesis, it was obtained in non-decay corrected radiochemical yields (n.d.c. RCYs) of 12.4 ± 1.3%, with molar activities (MAs) of 90.3 ± 12.6 µmol (n = 7) (Method I). The boronic pinacol ester combined with copper and oxygen also delivered the radioligand with 16.8 ± 1.0% n. d.c. RCYs and 77.3 ± 20.7 GBq/µmol (n = 7) MAs after formulation (Method II). The radiochemical purity, radionuclidic purity, solvent residue, sterility, endotoxin content and other parameters were all validated for human use. Consistent with the distribution of PDE10A in the brain, escalating uptake of [18F]P10A-1910 was observed in the order of cerebellum (reference region), substantial nigra, caudate and putamen. The non-displaceable binding potential (BP ND) was estimated by simplified reference-tissue model (SRTM); linear regressions demonstrated that BP ND was well correlated with the most widely used semiquantitative parameter SUV. The strongest correlation was observed with SUV(50-60 min) (R 2 = 0.966, p < 0.01). Collectively, these results indicated that a static scan protocol could be easily performed for PET imaging of PDE10A. Most importantly, that [18F]P10A-1910 is a promising radioligand to clinically quantify PDE10A.

Tricyclic Aza-Andrographolide Derivatives from Late-Stage Hydroamination and Their Anti-human Coronavirus (Anti-HCoV) Activity.

A late-stage functionalization (LSF) of the natural product andrographolide for the efficient assembly of a range of structurally interesting and diverse tricyclic-aza derivatives was developed. The key to the diversification is a photo-catalyzed intramolecular hydroamination reaction, and acridinium derivatives were demonstrated to be the optimal catalysts. Additionally, the synthesized tricyclic aza-andrographolide derivatives were found to inhibit human coronavirus with high potency.

Discovery of a highly specific 18F-labeled PET ligand for phosphodiesterase 10A enabled by novel spirocyclic iodonium ylide radiofluorination.

As a member of cyclic nucleotide phosphodiesterase (PDE) enzyme family, PDE10A is in charge of the degradation of cyclic adenosine (cAMP) and guanosine monophosphates (cGMP). While PDE10A is primarily expressed in the medium spiny neurons of the striatum, it has been implicated in a variety of neurological disorders. Indeed, inhibition of PDE10A has proven to be of potential use for the treatment of central nervous system (CNS) pathologies caused by dysfunction of the basal ganglia-of which the striatum constitutes the largest component. A PDE10A-targeted positron emission tomography (PET) radioligand would enable a better assessment of the pathophysiologic role of PDE10A, as well as confirm the relationship between target occupancy and administrated dose of a given drug candidate, thus accelerating the development of effective PDE10A inhibitors. In this study, we designed and synthesized a novel 18F-aryl PDE10A PET radioligand, codenamed [18F]P10A-1910 ([18F]9), in high radiochemical yield and molar activity via spirocyclic iodonium ylide-mediated radiofluorination. [18F]9 possessed good in vitro binding affinity (IC50 = 2.1 nmol/L) and selectivity towards PDE10A. Further, [18F]9 exhibited reasonable lipophilicity (logD = 3.50) and brain permeability (P app > 10 × 10-6 cm/s in MDCK-MDR1 cells). PET imaging studies of [18F]9 revealed high striatal uptake and excellent in vivo specificity with reversible tracer kinetics. Preclinical studies in rodents revealed an improved plasma and brain stability of [18F]9 when compared to the current reference standard for PDE10A-targeted PET, [18F]MNI659. Further, dose-response experiments with a series of escalating doses of PDE10A inhibitor 1 in rhesus monkey brains confirmed the utility of [18F]9 for evaluating target occupancy in vivo in higher species. In conclusion, our results indicated that [18F]9 is a promising PDE10A PET radioligand for clinical translation.

DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction.

Drug-target interaction (DTI) prediction reduces the cost and time of drug development, and plays a vital role in drug discovery. However, most of research does not fully explore the molecular structures of drug compounds in DTI prediction. To this end, we propose a deep learning model to capture the molecular structure information of drug compounds for DTI prediction. This model utilizes a transformer network incorporating multilayer graph information, which captures the features of a drug's molecular structure so that the interactions between atoms of drug compounds can be explored more deeply. At the same time, a convolutional neural network is employed to capture the local residue information in the target sequence, and effectively extract the feature information of the target. The experiments on the DrugBank dataset showed that the proposed model outperformed previous models based on the structure of target sequences. The results indicate that the improved transformer network fuses the feature information between layers in the graph convolutional neural network and extracts the interaction data for the molecular structure. The drug repositioning experiment on COVID-19 and Alzheimer's disease demonstrated the proposed model's ability to find therapeutic drugs in drug discovery. The code of our model is available at https://github.com/zhangpl109/DeepMGT-DTI.

Short-term lithium treatment protects the brain against ischemia-reperfusion injury by enhancing the neuroplasticity of cortical neurons.

OBJECTIVES: Lithium exerts a broad neuroprotective effect on the brain. This study examined whether lithium exerts therapeutic effects on stroke by restoring neural connections at the ischemic core of cortices post brain insult. METHODS: We treated rats with lithium or vehicle (saline) every 24 h for the first 72 h, starting at the beginning of reperfusion after inducing middle cerebral artery occlusion (MCAO) in rats. Somatosensory evoked potential (SSEP) recording and behavioral testing were employed to evaluate the beneficial effects of lithium treatment. To examine the effects of lithium-induced neuroplasticity, we evaluated the dendritic morphology in cortex pyramidal cells and the primary neuronal cell culture that underwent brain insults and oxygen and glucose deprivation (OGD), respectively. RESULTS: The results demonstrated that rats subjected to MCAO had prolonged N1 latency and a decreased N1/P1 amplitude at the ipsilateral cortex. Four doses of lithium reduced the brain infarction volume and enhanced the SSEP amplitude. The results of neurobehavioral tests demonstrated that lithium treatment improved sensory function, as demonstrated by improved 28-point clinical scale scores. In vitro study results showed that lithium treatment increased the dendritic lengths and branches of cultured neurons and reversed the suppressive effects of OGD. The in vivo study results indicated that lithium treatment increased cortical spine density in various layers and resulted in the development of the dendritic structure in the contralateral hemisphere. CONCLUSION: Our study confirmed that neuroplasticity in cortical neurons is crucial for lithium-induced brain function 50 recovery after brain ischemia.

The distinct role of orbitofrontal and medial prefrontal cortex in encoding impulsive choices in an animal model of attention deficit hyperactivity disorder.

Attention deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder affecting up to 5% of children worldwide. The lack of understanding of ADHD etiology prevented the development of effective treatment for the disease. Here, using in vivo electrophysiology recordings, we have recorded and analyzed the neuronal encoding of delay discounting behavior in prefrontal and orbitofrontal cortex of spontaneously hypertensive rat (SHR). We found that in the presence of rewards, neurons in the orbitofrontal cortex (OFC) were activated regardless to the value of the rewards and OFC neurons in SHR exhibited significantly higher rates of neuronal discharging towards the presence of rewards. While in the medial prefrontal cortex (mPFC), neurons of SHR responded similarly in the presence of large rewards compared with control rats whereas they displayed higher firing rates towards smaller rewards. In addition, the reward-predicting neurons in the OFC encodes for value of rewards in control animals and they were strongly activated upon receiving a small immediate reinforcer in the SHR whereas the reward-predicting neurons in the mPFC neurons generally did not respond to the value of the rewards. Our study characterized the neuronal discharging patterns of OFC and mPFC neurons in the SHR and the control animals and provided novel insights for further understanding the neuronal basis of ADHD pathology.

Combining visible-light induction and copper catalysis for chemo-selective nitrene transfer for late-stage amination of natural products.

Nitrene transfer chemistry is an effective strategy for introducing C-N bonds, which are ubiquitous in pharmaceuticals, agrochemicals and diverse bioactive natural products. The development of chemical methodology that can functionalize unique sites within natural products through nitrene transfer remains a challenge in the field. Herein, we developed copper catalyzed chemoselective allylic C-H amination and catalyst-free visible-light induced aziridination of alkenes through nitrene transfer. In general, both reactions tolerate a wide range of functional groups and occur with predictable regioselectivity. Furthermore, combination of these two methods enable the intermolecular chemo-selective late-stage amination of biologically active natural products, leading to C-H amination or C=C aziridination products in a tunable way. A series of control experiments indicate two-step radical processes were involved in both reaction systems.