Adenosine mediates the amelioration of social novelty deficits during rhythmic light treatment of 16p11.2 deletion female mice.

Non-invasive brain stimulation therapy for autism spectrum disorder (ASD) has shown beneficial effects. Recently, we and others demonstrated that visual sensory stimulation using rhythmic 40 Hz light flicker effectively improved cognitive deficits in mouse models of Alzheimer's disease and stroke. However, whether rhythmic visual 40 Hz light flicker stimulation can ameliorate behavioral deficits in ASD remains unknown. Here, we show that 16p11.2 deletion female mice exhibit a strong social novelty deficit, which was ameliorated by treatment with a long-term 40 Hz light stimulation. The elevated power of local-field potential (LFP) in the prefrontal cortex (PFC) of 16p11.2 deletion female mice was also effectively reduced by 40 Hz light treatment. Importantly, the 40 Hz light flicker reversed the excessive excitatory neurotransmission of PFC pyramidal neurons without altering the firing rate and the number of resident PFC neurons. Mechanistically, 40 Hz light flicker evoked adenosine release in the PFC to modulate excessive excitatory neurotransmission of 16p11.2 deletion female mice. Elevated adenosine functioned through its cognate A1 receptor (A1R) to suppress excessive excitatory neurotransmission and to alleviate social novelty deficits. Indeed, either blocking the A1R using a specific antagonist DPCPX or knocking down the A1R in the PFC using a shRNA completely ablated the beneficial effects of 40 Hz light flicker. Thus, this study identified adenosine as a novel neurochemical mediator for ameliorating social novelty deficit by reducing excitatory neurotransmission during 40 Hz light flicker treatment. The 40 Hz light stimulation warrants further development as a non-invasive ASD therapeutics.

Palladium-Catalyzed Defluorinative Alkylation of gem-Difluoroalkenes with Cyclopropanols: Stereoselective Synthesis of γ-Fluorinated γ,δ-Unsaturated Ketones.

A palladium-catalyzed defluorinative alkylation of gem-difluoroalkenes with cyclopropyl alcohols was developed. A range of γ-fluorinated γ,δ-unsaturated ketones were constructed in good yields with excellent stereoselectivities. In addition, by base-mediated intramolecular nucleophilic vinylic substitution (SNV), the products could be further transformed to 2,5-dimethylenetetrahydrofurans and analogues with excellent stereoselectivities.

Targeting the TGF-ß signaling pathway: an updated patent review (2021-present).

INTRODUCTION: The TGF-ß signaling pathway is a complex network that plays a crucial role in regulating essential biological functions and is implicated in the onset and progression of multiple diseases. This review highlights the recent advancements in developing inhibitors targeting the TGF-ß signaling pathway and their potential therapeutic applications in various diseases. AREA COVERED: The review discusses patents on active molecules related to the TGF-ß signaling pathway, focusing on three strategies: TGF-ß activity inhibition, blocking TGF-ß receptor binding, and disruption of the signaling pathway using small molecule inhibitors. Combination therapies and the development of fusion proteins targeting multiple pathways are also explored. The literature search was conducted using the Cortellis Drug Discovery Intelligence database, covering patents from 2021 onwards. EXPERT OPINION: The development of drugs targeting the TGF-ß signaling pathway has made significant progress in recent years. However, addressing challenges such as specificity, systemic toxicity, and patient selection is crucial for their successful clinical application. Targeting the TGF-ß signaling pathway holds promise as a promising approach for the treatment of various diseases.

Design, synthesis and biological evaluation of novel 3C-like protease inhibitors as lead compounds against SARS-CoV-2.

Background: The epidemic caused by SARS-CoV-2 swept the world in 2019. The 3C-like protease (3CLpro) of SARS-CoV-2 plays a key role in viral replication, and its inhibition could inhibit viral replication. Materials & methods: The virtual screen based on receptor-ligand pharmacophore models and molecular docking were conducted to obtain the novel scaffolds of the 3CLpro. The molecular dynamics simulation was also carried out. All compounds were synthesized and evaluated in biochemical assays. Results: The compound C2 could inhibit 3CLpro with a 72% inhibitory rate at 10 µM. The covalent docking showed that C2 could form a covalent bond with the Cys145 in 3CLpro. Conclusion: C2 could be a potent lead compound of 3CLpro inhibitors against SARS-CoV-2.

Discovery of Covalent Lead Compounds Targeting 3CL Protease with a Lateral Interactions Spiking Neural Network.

Covalent drugs exhibit advantages in that noncovalent drugs cannot match, and covalent docking is an important method for screening covalent lead compounds. However, it is difficult for covalent docking to screen covalent compounds on a large scale because covalent docking requires determination of the covalent reaction type of the compound. Here, we propose to use deep learning of a lateral interactions spiking neural network to construct a covalent lead compound screening model to quickly screen covalent lead compounds. We used the 3CL protease (3CL Pro) of SARS-CoV-2 as the screen target and constructed two classification models based on LISNN to predict the covalent binding and inhibitory activity of compounds. The two classification models were trained on the covalent complex data set targeting cysteine (Cys) and the compound inhibitory activity data set targeting 3CL Pro, respected, with good prediction accuracy (ACC > 0.9). We then screened the screening compound library with 6 covalent binding screening models and 12 inhibitory activity screening models. We tested the inhibitory activity of the 32 compounds, and the best compound inhibited SARS-CoV-2 3CL Pro with an IC50 value of 369.5 nM. Further assay implied that dithiothreitol can affect the inhibitory activity of the compound to 3CL Pro, indicating that the compound may covalently bind 3CL Pro. The selectivity test showed that the compound had good target selectivity to 3CL Pro over cathepsin L. These correlation assays can prove the rationality of the covalent lead compound screening model. Finally, covalent docking was performed to demonstrate the binding conformation of the compound with 3CL Pro. The source code can be obtained from the GitHub repository (https://github.com/guzh970630/Screen_Covalent_Compound_by_LISNN).

Graphical Presentations in Systemic Anticancer Treatment Network Meta-Analyses: A Systematic Review.

Network meta-analysis (NMA) draws conclusions about indirect comparisons of randomized clinical trials and is considered high-level evidence. Most NMA publications make use of network plots to portray results. Network plots are complex graphics that can have many visual attributes to portray useful information, such as node size, color, and graph layout. We analyzed the network plots from 162 NMAs of systemic anticancer therapy efficacy using a set of 16 attributes. Our review showed that the current state of network plot data visualizations within the NMA space lacks diversity and does not make use of many of the visual attributes available to convey information. More thoughtful design choices should be placed behind these important visualizations, which can carry clinical significance and help derive treatment plans for patients.

GcForest-based compound-protein interaction prediction model and its application in discovering small-molecule drugs targeting CD47.

Identifying compound-protein interaction plays a vital role in drug discovery. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL) algorithms, are playing increasingly important roles in compound-protein interaction (CPI) prediction. However, ML relies on learning from large sample data. And the CPI for specific target often has a small amount of data available. To overcome the dilemma, we propose a virtual screening model, in which word2vec is used as an embedding tool to generate low-dimensional vectors of SMILES of compounds and amino acid sequences of proteins, and the modified multi-grained cascade forest based gcForest is used as the classifier. This proposed method is capable of constructing a model from raw data, adjusting model complexity according to the scale of datasets, especially for small scale datasets, and is robust with few hyper-parameters and without over-fitting. We found that the proposed model is superior to other CPI prediction models and performs well on the constructed challenging dataset. We finally predicted 2 new inhibitors for clusters of differentiation 47(CD47) which has few known inhibitors. The IC50s of enzyme activities of these 2 new small molecular inhibitors targeting CD47-SIRPα interaction are 3.57 and 4.79 µM respectively. These results fully demonstrate the competence of this concise but efficient tool for CPI prediction.

A novel deep learning model for glioma epilepsy associated with the identification of human cytomegalovirus infection injuries based on head MR.

Purpose: In this study, a deep learning model was established based on head MRI to predict a crucial evaluation parameter in the assessment of injuries resulting from human cytomegalovirus infection: the occurrence of glioma-related epilepsy. The relationship between glioma and epilepsy was investigated, which serves as a significant indicator of labor force impairment. Methods: This study enrolled 142 glioma patients, including 127 from Shengjing Hospital of China Medical University, and 15 from the Second Affiliated Hospital of Dalian Medical University. T1 and T2 sequence images of patients' head MRIs were utilized to predict the occurrence of glioma-associated epilepsy. To validate the model's performance, the results of machine learning and deep learning models were compared. The machine learning model employed manually annotated texture features from tumor regions for modeling. On the other hand, the deep learning model utilized fused data consisting of tumor-containing T1 and T2 sequence images for modeling. Results: The neural network based on MobileNet_v3 performed the best, achieving an accuracy of 86.96% on the validation set and 75.89% on the test set. The performance of this neural network model significantly surpassed all the machine learning models, both on the validation and test sets. Conclusion: In this study, we have developed a neural network utilizing head MRI, which can predict the likelihood of glioma-associated epilepsy in untreated glioma patients based on T1 and T2 sequence images. This advancement provides forensic support for the assessment of injuries related to human cytomegalovirus infection.

A comprehensive review for machine learning on neuroimaging in obsessive-compulsive disorder.

Obsessive-compulsive disorder (OCD) is a common mental disease, which can exist as a separate disease or become one of the symptoms of other mental diseases. With the development of society, statistically, the incidence rate of obsessive-compulsive disorder has been increasing year by year. At present, in the diagnosis and treatment of OCD, The clinical performance of patients measured by scales is no longer the only quantitative indicator. Clinical workers and researchers are committed to using neuroimaging to explore the relationship between changes in patient neurological function and obsessive-compulsive disorder. Through machine learning and artificial learning, medical information in neuroimaging can be better displayed. In this article, we discuss recent advancements in artificial intelligence related to neuroimaging in the context of Obsessive-Compulsive Disorder.

Identification and Characterization of Avoidable Hospital Admissions in Patients With Lung Cancer.

BACKGROUND: More than 50% of patients with lung cancer are admitted to the hospital while receiving treatment, which is a burden to patients and the healthcare system. This study characterizes the risk factors and outcomes of patients with lung cancer who were admitted to the hospital. METHODS: A multidisciplinary oncology care team conducted a retrospective medical record review of patients with lung cancer admitted in 2018. Demographics, disease and admission characteristics, and end-of-life care utilization were recorded. Following a multidisciplinary consensus review process, admissions were determined to be either "avoidable" or "unavoidable." Generalized estimating equation logistic regression models assessed risks and outcomes associated with avoidable admissions. RESULTS: In all, 319 admissions for 188 patients with a median age of 66 years (IQR, 59-74 years) were included. Cancer-related symptoms accounted for 65% of hospitalizations. Common causes of unavoidable hospitalizations were unexpected disease progression causing symptoms, chronic obstructive pulmonary disease exacerbation, and infection. Of the 47 hospitalizations identified as avoidable (15%), the median overall survival was 1.6 months compared with 9.7 months (hazard ratio, 2.07; 95% CI, 1.34-3.19; P<.001) for unavoidable hospitalizations. Significant reasons for avoidable admissions included cancer-related pain (P=.02), hypervolemia (P=.03), patient desire to initiate hospice services (P=.01), and errors in medication reconciliation or distribution (P<.001). Errors in medication management caused 26% of the avoidable hospitalizations. Of admissions in patients receiving immunotherapy (n=102) or targeted therapy (n=44), 9% were due to adverse effects of treatment. Patients receiving immunotherapy and targeted therapy were at similar risk of avoidable hospitalizations compared with patients not receiving treatment (P=.3 and P=.1, respectively). CONCLUSIONS: We found that 15% of hospitalizations among patients with lung cancer were potentially avoidable. Uncontrolled symptoms, delayed implementation of end-of-life care, and errors in medication reconciliation were associated with avoidable inpatient admissions. Symptom management tools, palliative care integration, and medication reconciliations may mitigate hospitalization risk.

Clinical characteristics, racial inequities, and outcomes in patients with breast cancer and COVID-19: A COVID-19 and cancer consortium (CCC19) cohort study.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32-1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70-6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83-12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63-3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20-2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66-3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89-22.6]). Hispanic ethnicity, timing, and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC-related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to non-Hispanic White patients. Funding: This study was partly supported by National Cancer Institute grant number P30 CA068485 to Tianyi Sun, Sanjay Mishra, Benjamin French, Jeremy L Warner; P30-CA046592 to Christopher R Friese; P30 CA023100 for Rana R McKay; P30-CA054174 for Pankil K Shah and Dimpy P Shah; KL2 TR002646 for Pankil Shah and the American Cancer Society and Hope Foundation for Cancer Research (MRSG-16-152-01-CCE) and P30-CA054174 for Dimpy P Shah. REDCap is developed and supported by Vanderbilt Institute for Clinical and Translational Research grant support (UL1 TR000445 from NCATS/NIH). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. Clinical trial number: CCC19 registry is registered on ClinicalTrials.gov, NCT04354701.

Realistic Spin Model for Multiferroic NiI_{2}.

A realistic first-principle-based spin Hamiltonian is constructed for the type-II multiferroic NiI_{2}, using a symmetry-adapted cluster expansion method. Besides single ion anisotropy and isotropic Heisenberg terms, this model further includes the Kitaev interaction and a biquadratic term, and can well reproduce striking features of the experimental helical ground state, that are, e.g., a proper screw state, canting of rotation plane, propagation direction, and period. Using this model to build a phase diagram, it is demonstrated that, (i) the in-plane propagation direction of ⟨11[over ¯]0⟩ is determined by the Kitaev interaction, instead of the long-believed exchange frustrations and (ii) the canting of rotation plane is also dominantly determined by Kitaev interaction, rather than interlayer couplings. Furthermore, additional Monte Carlo simulations reveal three equivalent domains and different topological defects. Since the ferroelectricity is induced by spins in type-II multiferroics, our work also implies that Kitaev interaction is closely related to the multiferroicity of NiI_{2}.

Echocardiographic evaluation of right heart failure which might be associated with DNA damage response in SU5416-hypoxia induced pulmonary hypertension rat model.

Right heart failure is the leading cause of death in pulmonary hypertension (PH), and echocardiography is a commonly used tool for evaluating the risk hierarchy of PH. However, few studies have explored the dynamic changes in the structural and functional changes of the right heart during the process of PH. Previous studies have found that pulmonary circulation coupling right ventricular adaptation depends on the degree of pressure overload and other factors. In this study, we performed a time-dependent evaluation of right heart functional changes using transthoracic echocardiography in a SU5416 plus hypoxia (SuHx)-induced PH rat model. Rats were examined in 1-, 2-, 4-, and 6-week using right-heart catheterization, cardiac echocardiography, and harvested heart tissue. Our study found that echocardiographic measures of the right ventricle (RV) gradually worsened with the increase of right ventricular systolic pressure, and right heart hypofunction occurred at an earlier stage than pulmonary artery thickening during the development of PH. Furthermore, sarco-endoplasmic reticulum calcium ATPase 2 (SERCA2), a marker of myocardial damage, was highly expressed in week 2 of SuHx-induced PH and had higher levels of expression of γ-H2AX at all timepoints, as well as higher levels of DDR-related proteins p-ATM and p53/p-p53 and p21 in week 4 and week 6. Our study demonstrates that the structure and function of the RV begin to deteriorate with DNA damage and cellular senescence during the early stages of PH development.

Clinical Characteristics, Racial Inequities, and Outcomes in Patients with Breast Cancer and COVID-19: A COVID-19 and Cancer Consortium (CCC19) Cohort Study.

Background: Limited information is available for patients with breast cancer (BC) and coronavirus disease 2019 (COVID-19), especially among underrepresented racial/ethnic populations. Methods: This is a COVID-19 and Cancer Consortium (CCC19) registry-based retrospective cohort study of females with active or history of BC and laboratory-confirmed severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection diagnosed between March 2020 and June 2021 in the US. Primary outcome was COVID-19 severity measured on a five-level ordinal scale, including none of the following complications, hospitalization, intensive care unit admission, mechanical ventilation, and all-cause mortality. Multivariable ordinal logistic regression model identified characteristics associated with COVID-19 severity. Results: 1,383 female patient records with BC and COVID-19 were included in the analysis, the median age was 61 years, and median follow-up was 90 days. Multivariable analysis revealed higher odds of COVID-19 severity for older age (aOR per decade, 1.48 [95% CI, 1.32 - 1.67]); Black patients (aOR 1.74; 95 CI 1.24-2.45), Asian Americans and Pacific Islander patients (aOR 3.40; 95 CI 1.70 - 6.79) and Other (aOR 2.97; 95 CI 1.71-5.17) racial/ethnic groups; worse ECOG performance status (ECOG PS ≥2: aOR, 7.78 [95% CI, 4.83 - 12.5]); pre-existing cardiovascular (aOR, 2.26 [95% CI, 1.63 - 3.15])/pulmonary comorbidities (aOR, 1.65 [95% CI, 1.20 - 2.29]); diabetes mellitus (aOR, 2.25 [95% CI, 1.66 - 3.04]); and active and progressing cancer (aOR, 12.5 [95% CI, 6.89 - 22.6]). Hispanic ethnicity, timing and type of anti-cancer therapy modalities were not significantly associated with worse COVID-19 outcomes. The total all-cause mortality and hospitalization rate for the entire cohort was 9% and 37%, respectively however, it varied according to the BC disease status. Conclusions: Using one of the largest registries on cancer and COVID-19, we identified patient and BC related factors associated with worse COVID-19 outcomes. After adjusting for baseline characteristics, underrepresented racial/ethnic patients experienced worse outcomes compared to Non-Hispanic White patients.

Tunable Topological States in Stacked Chern Insulator Bilayers.

The emergence of intrinsic quantum anomalous Hall (QAH) insulators with a long-range ferromagnetic (FM) order triggers unprecedented prosperity for combining topology and magnetism in low dimensions. Built upon atom-thin Chern insulator monolayer MnBr3, we propose that the topologically nontrivial electronic states can be systematically tuned by inherent magnetic orders and external electric/optical fields in stacked Chern insulator bilayers. The FM bilayer illustrates a high-Chern-number QAH state characterized by both quantized Hall plateaus and specific magneto-optical Kerr angles. In antiferromagnetic bilayers, Berry curvature singularity induced by electrostatic fields or lasers emerges, which further leads to a novel implementation of the layer Hall effect depending on the chirality of irradiated circularly polarized light. These results demonstrate that abundant tunable topological properties can be achieved in stacked Chern insulator bilayers, thereby suggesting a universal routine to modulate d-orbital-dominated topological Dirac fermions.

Elimination of perineuronal nets in CA1 disrupts GABA release and long-term contextual fear memory retention.

Perineuronal nets (PNNs) which mostly surround the parvalbumin (PV) neurons, have been shown to play critical roles in neural plasticity. Recently, PNNs have been shown to regulate fear-associated memory, but the molecular mechanism is still unclear. In this study, we found that removal of PNNs in vivo using chondroitinase ABC (ChABC) injection resulted in reduced firing rate of PV neurons and decreased inhibitory synaptic transmission in both PV neurons and excitatory neurons in the CA1 hippocampus. Interestingly, altered synaptic transmission appears to be mediated by presynaptic changes. Furthermore, ChABC treatment disrupts long-term contextual fear memory retention. These results suggest PNNs might alter fear memory by reducing the presynaptic GABA release.

Sestrin 2 Deficiency Exacerbates Noise-Induced Cochlear Injury Through Inhibiting ULK1/Parkin-Mediated Mitophagy.

Aims: Noise damage to auditory hair cells is associated with oxidative stress and mitochondrial dysfunction. This study aimed to investigate the possible effect of sestrin 2 (SESN2), an endogenous antioxidant protein, on noise-induced hearing loss (NIHL) and the underlying mechanisms. Results: We identified SESN2 as a protective factor against oxidative stress in NIHL through activation of Parkin-mediated mitophagy. Consistently, SESN2 expression was increased and mitophagy was induced during the early stage after a temporary threshold shift due to noise exposure or hydrogen peroxide(H2O2) stimulation; conversely, SESN2 deficiency blocked mitophagy and exacerbated acoustic trauma. Mechanistically, SESN2 interacted with Unc-51-like protein kinase 1(ULK1), promoting ULK1 protein-level stabilization by interfering with its proteasomal degradation. This stabilization is essential for mitophagy initiation, since restoring ULK1 expression in SESN2-silenced cells rescued mitophagy defects. Innovation and Conclusion: Our results provide novel insights regarding SESN2 as a therapeutic target against noise-induced cochlear injury, possibly through improved mitophagy. Antioxid. Redox Signal. 38, 115-136.

Hypothermia evoked by stimulation of medial preoptic nucleus protects the brain in a mouse model of ischaemia.

Therapeutic hypothermia at 32-34 °C during or after cerebral ischaemia is neuroprotective. However, peripheral cold sensor-triggered hypothermia is ineffective and evokes vigorous counteractive shivering thermogenesis and complications that are difficult to tolerate in awake patients. Here, we show in mice that deep brain stimulation (DBS) of warm-sensitive neurones (WSNs) in the medial preoptic nucleus (MPN) produces tolerable hypothermia. In contrast to surface cooling-evoked hypothermia, DBS mice exhibit a torpor-like state without counteractive shivering. Like hypothermia evoked by chemogenetic activation of WSNs, DBS in free-moving mice elicits a rapid lowering of the core body temperature to 32-34 °C, which confers significant brain protection and motor function reservation. Mechanistically, activation of WSNs contributes to DBS-evoked hypothermia. Inhibition of WSNs prevents DBS-evoked hypothermia. Maintaining the core body temperature at normothermia during DBS abolishes DBS-mediated brain protection. Thus, the MPN is a DBS target to evoke tolerable therapeutic hypothermia for stroke treatment.