Association between serum cytokines and timeframe for conversion from clinical high-risk to psychosis.

AIM: Although many studies have explored the link between inflammatory markers and psychosis, there is a paucity of research investigating the temporal progression in individuals at clinical high-risk (CHR) who eventually develop full psychosis. To address this gap, we investigated the correlation between serum cytokine levels and Timeframe for Conversion to Psychosis (TCP) in individuals with CHR. METHODS: We enrolled 53 individuals with CHR who completed a 5-year follow-up with a confirmed conversion to psychosis. Granulocyte macrophage-colony stimulating factor (GM-CSF), interleukin (IL)-1ß, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor (VEGF) levels were measured at baseline and 1-year. Correlation and quantile regression analyses were performed. RESULTS: The median TCP duration was 14 months. A significantly shorter TCP was associated with higher levels of TNF-α (P = 0.022) and VEGF (P = 0.016). A negative correlation was observed between TCP and TNF-α level (P = 0.006) and VEGF level (P = 0.04). Quantile regression indicated negative associations between TCP and GM-CSF levels below the 0.5 quantile, IL-10 levels below the 0.3 quantile, IL-2 levels below the 0.25 quantile, IL-6 levels between the 0.65 and 0.75 quantiles, TNF-α levels below the 0.8 quantile, and VEGF levels below the 0.7 quantile. A mixed linear effects model identified significant time effects for IL-10 and IL-2, and significant group effects for changes in IL-2 and TNF-α. CONCLUSIONS: Our findings underscore that a more pronounced baseline inflammatory state is associated with faster progression of psychosis in individuals with CHR. This highlights the importance of considering individual inflammatory profiles during early intervention and of tailoring preventive measures for risk profiles.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Comparison of Direct Pars Repair Techniques for Spondylolysis in Young Patients: Pedicle Screw Hook System versus Pedicle Screw Rod.

Background: Lumbar spondylolysis (LS) poses a potential threat, and there is a need to evaluate and compare the effectiveness of direct pars repair techniques. Objective: To assess and compare the clinical and radiographic outcomes of direct pars repair techniques using the pedicle screw hook system (PSHS) and the pedicle screw rod system (PSRS) in young symptomatic patients with lumbar spondylolysis. Methods: A retrospective study was conducted to compare clinical and radiological data in young symptomatic LS patients after surgery. Records of 45 post-surgery LS patients with a minimum 24-month follow-up (January 2014 to June 2019) were reviewed. A total of 26 patients underwent PSHS, and 19 had PSRS. Treatment outcomes were analyzed using the visual analog pain scale (VAS), Oswestry disability index (ODI), MacNab criteria, lumbar fusion status, and Pfirrmann grading standards. Patient baseline characteristics were also compared between the two groups. Results: No disc degeneration was observed in either PSHS or PSRS groups at 24 months postoperatively, according to the Pfirrmann grading scale. The PSRS group outperformed the PSHS group in operative time, intraoperative blood loss, postoperative drainage, length of hospital stays, ODI, VAS values at 3 months postoperatively, and fusion status at 6 months postoperatively. No notable differences were observed in other parameters during the 24-month follow-up period, and no significant surgical complications were recorded. Conclusions: Direct pars repair techniques using PSHS and PSRS yielded satisfactory clinical and radiographic results in young patients with symptomatic LS. PSRS, compared to PSHS, demonstrated greater effectiveness in young individuals with LS and promoted early recovery.

Epigenetic target identification strategy based on multi-feature learning.

The identification of potential epigenetic targets for a known bioactive compound is essential and promising as more and more epigenetic drugs are used in cancer clinical treatment and the availability of chemogenomic data related to epigenetics increases. In this study, we introduce a novel epigenetic target identification strategy (ETI-Strategy) that integrates a multi-task graph convolutional neural network prior model and a protein-ligand interaction classification discriminating model using large-scale bioactivity data for a panel of 55 epigenetic targets. Our approach utilizes machine learning techniques to achieve an AUC value of 0.934 for the prior model and 0.830 for the discriminating model, outperforming inverse docking in predicting protein-ligand interactions. When comparing with other open-source target identification tools, it was found that only our tool was able to accurately predict all the targets corresponding to each compound. This further demonstrates the ability of our strategy to take full advantage of molecular-level information as well as protein-level information in molecular activity prediction. Our work highlights the contribution of machine learning in the identification of potential epigenetic targets and offers a novel approach for epigenetic drug discovery and development.Communicated by Ramaswamy H. Sarma.

Serum Levels of Tumor Necrosis Factor-α and Vascular Endothelial Growth Factor in the Subtypes of Clinical High Risk Individuals: A Prospective Cohort Study.

Introduction: Numerous studies have established the roles of inflammation and angioneurins in the pathogenesis of schizophrenia (SCZ). This study aimed to compare the serum levels of tumour necrosis factor (TNF)-α and vascular endothelial growth factor (VEGF) in patients at clinical high risk (CHR) for psychosis or SCZ at baseline and one year after treatment. Methods: A total of 289 CHR participants from the Shanghai At Risk for Psychosis Extended Program (SHARP) were tracked for a year. They were divided into two and four subtypes based on symptom severity according to the Structured Interview for Prodromal Syndromes (SIPS) and received standard medical care. At baseline and one-year follow-up, TNF-α and VEGF were detected using enzyme-linked immunosorbent assay, and pathological features were assessed using the Global Assessment of Function (GAF) score. Results: Baseline TNF-α levels did not differ significantly, while VEGF levels were lower in patients with more severe symptoms. VEGF showed a negative correlation with negative features, both overall (r = -0.212, p = 0.010) and in the subgroup with higher positive scores (r = -0.370, p = 0.005). TNF-α was positively correlated with negative symptoms in the subgroup with higher negative scores (r = 0.352, p = 0.002). A three-way multivariate analysis of variance demonstrated that participants in Subtype 1 of positive or negative symptoms performed better than those in Subtype 2, with significant main effects and interactions of group and both cytokines. Discussion: TNF-α and VEGF levels are higher and lower, respectively, in CHR patients with more severe clinical symptoms, particularly negative symptoms, which point to a worsening inflammatory and vascular status in the brain.

Association of Attenuated Niacin Response With Inflammatory Imbalance and Prediction of Conversion to Psychosis From Clinical High-risk Stage.

Objective: Attenuated niacin responses and changes in cytokine levels have been reported in schizophrenia. However, prior studies have typically focused on schizophrenia, and little is known about the association between niacin response and inflammatory imbalance in clinically high-risk psychosis (CHR). This study aimed to assess the niacin response to inflammatory imbalance for association with conversion to psychosis within 2 years.Methods: A prospective case-control study was performed to assess the niacin response and interleukin (IL)-1ß, IL-2, IL-6, IL-8, IL-10, and tumor necrosis factor-α levels in 60 CHR individuals and 60 age- and sex-matched healthy controls (HC) from May 2019 to December 2021. Participants with CHR were identified using the Structured Interview for Prodromal Syndromes. The niacin-induced responses were measured using laser Doppler flowmetry. From the dose-response curves, the log-transferred concentration of methylnicotinate required to elicit a half-maximal blood flow response (LogEC50) and maximal minus minimal blood flow response (Span) values were calculated for each subject. Serum cytokine levels were measured using enzyme-linked immunosorbent assay. Individuals with CHR were then divided into converters (CHR-C, n = 15) and non-converters (CHR-NC, n = 45) to psychosis based on their 2-year follow-up clinical status.Results: The CHR group exhibited significantly higher LogEC50 (t = 3.650, P < .001) and Span (t = 2.657, P = .009) values than the HC group. The CHR-C group exhibited a significantly shorter Span (t = 4.027, P < .001) than the CHR-NC group. The LogEC50 showed a trend toward significance (t = 1.875, P = .066). None of the cytokine levels were significant. The conversion outcome can therefore be predicted by applying LogEC50 (P = .049) and Span (P < .001). The regression model with variables of LogEC50, Span, family history, and scores of positive symptoms showed good discrimination of subsequent conversion to psychosis and achieved a classification accuracy of 91.7%. The decreased LogEC50 in the CHR-C group was significantly correlated with the increased IL-1ß/IL-10 ratio (Spearman ρ = -0.600, P = .018), but this correlation was nonsignificant in the CHR-NC group.Conclusions: Our findings indicate a significant association between niacin response and psychosis conversion outcomes in individuals with CHR. Compared with peripheral inflammatory cytokines, the niacin response can better predict conversion, although there may be an intersection between the two in biological mechanisms.

Visible trephine-based foraminoplasty in PTED leads to asymmetrical stress changes and instability in the surgical and adjacent segments: a finite element analysis.

This study aimed to construct a multi-segment lumbar finite element model (FEM) of PTED surgery to analyze the changes in stress and ROM after visible trephine-based foraminoplasty. The CT scans of a 35-year-old healthy male were used to develop a multi-segment lumbar FEM with Mimic, Geomagic Studio, Hypermesh and MSC.Patran. Different foraminoplasty was performed on the model, and these were grouped into normal group (A), the ventral resection group (B), the apex resection group (C), the ventral + apex + isthmus resection group (D), and the SAP + isthmus + lateral recess resection group (E). A vertical load of 500N and a torque of 10N·M were applied to the upper surface of the L3 vertebral body to simulate the biomechanical characteristics under the motion of flexion, extension, lateral bending, and rotation. The von Mises stress maps of the intervertebral f, vertebral body, facet joints, and the ROM of the L3-S1 intervertebral disk were calculated and analyzed. The changes of peak stress on the vertebral body for each group were not significant in the same motion state. Significant stress differences were observed in the L4/5 intervertebral disks, while no obvious stress changes were observed for the L3/4 and L5/S1 intervertebral disks. The stress of the L3/4 and L5/S1 facet joints decreased after L4/5 foraminoplasty, while the stress of L4/5 facet joints displayed an overall increasing trend. Significant asymmetrical stress changes of bilateral facet joints were observed in all three segments, particularly during bilateral rotation movements. The ROM of L3-S1 gradually increased from Group A to Group E, especially during flexion, left lateral bending, and right rotation, with the highest elevation observed for the L45 ROM. Our FEM indicated that enlarged resection and exposure of the articular surface could lead to significant asymmetrical stress changes in the bilateral facet joints and ROM instability of the surgical and adjacent segments. These findings suggested that unnecessary and excessive resection should be avoided in PTED to reduce the incidence of low back pain and the risk of postsurgical degeneration.

An Interpretable Multitask Framework BiLAT Enables Accurate Prediction of Cyclin-Dependent Protein Kinase Inhibitors.

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases with crucial effects on the regulation of cell cycle and transcription. CDKs can be a hallmark of cancer since their excessive expression could lead to impaired cell proliferation. However, the selectivity profile of most developed CDK inhibitors is not enough, which have hindered the therapeutic use of CDK inhibitors. In this study, we propose a multitask deep learning framework called BiLAT based on SMILES representation for the prediction of the inhibitory activity of molecules on eight CDK subtypes (CDK1, 2, 4-9). The framework is mainly composed of an improved bidirectional long short-term memory module BiLSTM and the encode layer of the Transformer framework. Additionally, the data enhancement method of SMILES enumeration is applied to improve the performance of the model. Compared with baseline predictive models based on three conventional machine learning methods and two multitask deep learning algorithms, BiLAT achieves the best performance with the highest average AUC, ACC, F1-score, and MCC values of 0.938, 0.894, 0.911, and 0.715 for the test set. Moreover, we constructed a targeted external data set CDK-Dec for the CDK family, which mainly contains bait values screened by 3D similarity with active compounds. This dataset was utilized in the subsequent evaluation of our model. It is worth mentioning that the BiLAT model is interpretable and can be used by chemists to design and synthesize compounds with improved activity. To further verify the generalization ability of the multitask BiLAT model, we also conducted another evaluation on three public datasets (Tox21, ClinTox, and SIDER). Compared with several currently popular models, BiLAT shows the best performance on two datasets. These results indicate that BiLAT is an effective tool for accelerating drug discovery.

Prediction of preoperative in-hospital mortality rate in patients with acute aortic dissection by machine learning: a two-centre, retrospective cohort study.

OBJECTIVES: To conduct a comprehensive analysis of demographic information, medical history, and blood pressure (BP) and heart rate (HR) variability during hospitalisation so as to establish a predictive model for preoperative in-hospital mortality of patients with acute aortic dissection (AD) by using machine learning techniques. DESIGN: Retrospective cohort study. SETTING: Data were collected from the electronic records and the databases of Shanghai Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine and the First Affiliated Hospital of Anhui Medical University between 2004 and 2018. PARTICIPANTS: 380 inpatients diagnosed with acute AD were included in the study. PRIMARY OUTCOME: Preoperative in-hospital mortality rate. RESULTS: A total of 55 patients (14.47%) died in the hospital before surgery. The results of the areas under the receiver operating characteristic curves, decision curve analysis and calibration curves indicated that the eXtreme Gradient Boosting (XGBoost) model had the highest accuracy and robustness. According to the SHapley Additive exPlanations analysis of the XGBoost model, Stanford type A, maximum aortic diameter >5.5 cm, high variability in HR, high variability in diastolic BP and involvement of the aortic arch had the greatest impact on the occurrence of in-hospital deaths before surgery. Moreover, the predictive model can accurately predict the preoperative in-hospital mortality rate at the individual level. CONCLUSION: In the current study, we successfully constructed machine learning models to predict the preoperative in-hospital mortality of patients with acute AD, which can help identify high-risk patients and optimise the clinical decision-making. Further applications in clinical practice require the validation of these models using a large-sample, prospective database. TRIAL REGISTRATION NUMBER: ChiCTR1900025818.

Serum angioneurin levels following electroconvulsive therapy for mood disorders.

OBJECTIVES: The efficacy of electroconvulsive therapy (ECT) in treating mood disorders (MDs) is hypothesized to be mediated by the induction of neurotrophic factors (denoted "angioneurins") that trigger neuronal plasticity. This study aimed to assess the effects of ECT on serum angioneurin levels in patients with MD. METHODS: A total of 110 patients with MDs including 30 with unipolar depression, 25 with bipolar depression (BD), 55 with bipolar mania (BM), and 50 healthy controls were included in the study. Patients were subdivided into two groups: those who received ECT + medication (12 ECT sessions) and those who received only medication (no-ECT). Depressive and manic symptom assessments and measurements of vascular endothelial growth factor (VEGF), fibroblast growth factor-2, nerve growth factor (NGF), and insulin-like growth factor-1 levels in blood samples were performed at baseline and week 8. RESULTS: Patients in the ECT group, specifically those with BD and BM, had significantly increased levels of VEGF compared to their baseline VEGF levels (p = 0.002). No significant changes in angioneurin levels were observed in the no-ECT group. Serum NGF levels were significantly associated with a reduction in depressive symptoms. Angioneurin levels were not associated with manic symptom reduction. CONCLUSIONS: This study hints that ECT may increase VEGF levels with angiogenic mechanisms that amplify NGF signaling to promote neurogenesis. It may also contribute to changes in brain function and emotional regulation. However, further animal experiments and clinical validation are needed.

Changes in Inflammatory Markers in Clinical High Risk of Developing Psychosis.

INTRODUCTION: Immune alterations are associated with the progression of psychosis. However, there are few studies designed to longitudinally measure inflammatory biomarkers during psychotic episodes. We aimed to assess changes in biomarkers from the prodromal phase to psychotic episodes in individuals with clinical high risk (CHR) of psychosis and compare converters and non-converters to psychosis as well as healthy controls (HCs). METHODS: We enrolled 394 individuals with CHR and 100 HCs. A total of 263 individuals with CHR completed the 1-year follow-up, and 47 had converted to psychosis. Interleukin (IL)-1ß, 2, 6, 8, 10, tumor necrosis factor-α (TNF-α), and vascular endothelial growth factor levels were measured at baseline and 1 year after completion of the clinical assessment. RESULTS: The baseline serum levels of IL-10, IL-2, and IL-6 were significantly lower in the conversion group than in the non-conversion group (IL-10, p = 0.010; IL-2, p = 0.023; IL-6, p = 0.012) and HC (IL-6: p = 0.034). Self-controlled comparisons showed that IL-2 changed significantly (p = 0.028), and IL-6 levels tended toward significance (p = 0.088) in the conversion group. In the non-conversion group, serum levels of TNF-α (p = 0.017) and VEGF (p = 0.037) changed significantly. Repeated measures analysis of variance revealed a significant time effect related to TNF-α (F = 4.502, p = 0.037, effect size (η2) = 0.051), a group effect related to IL-1ß (F = 4.590, p = 0.036, η2 = 0.062), and IL-2 (F = 7.521, p = 0.011, η2 = 0.212), but no time × group effect. DISCUSSION: Alterations in the serum levels of inflammatory cytokines were found to precede the first episode of psychosis in the CHR population, particularly for those who later converted to psychosis. Longitudinal analysis supports the varied roles of cytokines in individuals with CHR with later psychotic conversion or non-conversion outcomes.

Serum complement proteins rather than inflammatory factors is effective in predicting psychosis in individuals at clinical high risk.

Immunological/inflammatory factors are implicated in the development of psychosis. Complement is a key driver of inflammation; however, it remains unknown which factor is better at predicting the onset of psychosis. This study aimed to compare the alteration and predictive performance of inflammation and complement in individuals at clinical high risk (CHR). We enrolled 49 individuals at CHR and 26 healthy controls (HCs). Twenty-five patients at CHR had converted to psychosis (converter) by the 3-year follow-up. Inflammatory cytokines, including interleukin (IL)-1ß, 6, 8, 10, tumor necrosis factor-alpha (TNF-alpha), macrophage colony-stimulating factor levels, and complement proteins (C1q, C2, C3, C3b, C4, C4b, C5, C5a, factor B, D, I, H) were measured by enzyme-linked immunosorbent assay at baseline. Except for TNF- alpha, none of the inflammatory cytokines reached a significant level in either the comparison of CHR individuals and HC or between CHR-converters and non-converters. The C5, C3, D, I, and H levels were significantly lower (C5, p = 0.006; C3, p = 0.009; D, p = 0.026; I, p = 0.016; H, p = 0.019) in the CHR group than in the HC group. Compared to non-converters, converters had significantly lower levels of C5 (p = 0.012) and C5a (p = 0.007). None of the inflammatory factors, but many complement factors, showed significant correlations with changes in general function and symptoms. None of the inflammatory markers, except for C5a and C5, were significant in the discrimination of conversion outcomes in CHR individuals. Our results suggest that altered complement levels in the CHR population are more associated with conversion to psychosis than inflammatory factors. Therefore, an activated complement system may precede the first-episode of psychosis and contribute to neurological pathogenesis at the CHR stage.

Application of EfficientNet-B0 and GRU-based deep learning on classifying the colposcopy diagnosis of precancerous cervical lesions.

BACKGROUND: Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high-grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. METHODS: The images were collected from 6002 colposcopy examinations of normal control, low-grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic-acid images were all collected. We built a new neural network classification model based on the hybrid algorithm. EfficientNet-b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). RESULTS: The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to "Trichotomy", which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. CONCLUSION: Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.

Developing and optimizing a machine learning predictive model for post-thrombotic syndrome in a longitudinal cohort of patients with proximal deep venous thrombosis.

BACKGROUND: Post-thrombotic syndrome (PTS) is the most common chronic complication of deep venous thrombosis (DVT). Risk measurement and stratification of PTS are crucial for patients with DVT. This study aimed to develop predictive models of PTS using machine learning for patients with proximal DVT. METHODS: Herein, hospital inpatients from a DVT registry electronic health record database were randomly divided into a derivation and a validation set, and four predictive models were constructed using logistic regression, simple decision tree, eXtreme Gradient Boosting (XGBoost), and random forest (RF) algorithms. The presence of PTS was defined according to the Villalta scale. The areas under the receiver operating characteristic curves, decision-curve analysis, and calibration curves were applied to evaluate the performance of these models. The Shapley Additive exPlanations analysis was performed to explain the predictive models. RESULTS: Among the 300 patients, 126 developed a PTS at 6 months after DVT. The RF model exhibited the best performance among the four models, with an area under the receiver operating characteristic curves of 0.891. The RF model demonstrated that Villalta score at admission, age, body mass index, and pain on calf compression were significant predictors for PTS, with accurate prediction at the individual level. The Shapley Additive exPlanations analysis suggested a nonlinear correlation between age and PTS, with two peak ages of onset at 50 and 70 years. CONCLUSIONS: The current predictive model identified significant predictors and accurately predicted PTS for patients with proximal DVT. Moreover, the model demonstrated a nonlinear correlation between age and PTS, which might be valuable in risk measurement and stratification of PTS in patients with proximal DVT.

Recent advances in ATM inhibitors as potential therapeutic agents.

ATM, a member of the PIKK-like protein family, plays a central role in responding to DNA double-strand breaks and other lesions to protect the genome against DNA damage. Loss of ATM's kinase function has been shown to increase the sensitivity of most cells to ionizing radiation. Therefore, ATM is thought to be a promising target for chemotherapy as a radiotherapy sensitizer. The mechanism of ATM in cancer treatment and the development of its inhibitors have become research hotspots. Here we present an overview of research concerning ATM protein domains, functions and inhibitors, as well as perspectives and insights for future development of ATM-targeting agents.

Changes in inflammatory balance correlates with conversion to psychosis among individuals at clinical high-risk: A prospective cohort study.

Previous studies have revealed that the imbalance between Th1 cytokines and Th2 cytokines plays a role in disturbance of cellular responses in the brain during psychosis. Cross-sectional studies have implied that inflammatory cytokine changes emerge in early psychosis, even at the at-risk stage. This study aimed to test the hypothesis that inflammatory imbalance in clinical high-risk (CHR) individuals is associated with an increased risk of future psychosis. A prospective case-control study was performed to assess the Th1(interleukin (IL)-1ß)/Th2(IL-6) balance in 84 CHR individuals and 65 age- and sex-matched healthy controls (HC). Serum IL-1ß and IL-6 levels were measured by enzyme-linked immunosorbent assay at baseline and 1-year after the completion of the clinical assessment. Sixteen (19.0%) CHR participants converted to full psychosis during the 1-year follow-up period. At baseline, serum IL-1ß level was significantly lower in the CHR-converter group - resulting in decreased IL-1ß/IL-6 ratios - compared to those of the CHR-non-converter and HC groups. At the 1-year follow-up, IL-1ß level had decreased, and IL-1ß/IL-6 ratios had decreased in the CHR-non-converter group, such that these were comparable to values in the CHR-converter at this time point. Analysis of the changes in IL-1ß/IL-6 ratio between the baseline and 1-year follow-up measurements identified different trajectories in the CHR-converter and CHR-non-converter groups. Our findings demonstrate that a specific pattern of Th1/Th2 imbalance (decreased IL-1ß/IL-6 ratios with lower serum IL-1ß level) is associated with an increased risk of developing psychosis. Such specific pattern has potential for predicting conversion outcomes and selecting a distinct subgroup of CHR with immune-imbalanced-phenotype, that relevance in precise prevention.

Accurate calculation of absolute free energy of binding for SHP2 allosteric inhibitors using free energy perturbation.

Accurate prediction of binding affinity is a primary objective in structure-based drug discovery. A free energy perturbation (FEP) method based on molecular dynamics simulation shows great promise for protein-ligand binding affinity predictions. However, accurate calculation of binding affinity for allosteric inhibitors remains unknown and elusive, which hampers the discovery of allosteric inhibitors. Allosteric inhibitors exhibit several significant advantages over orthosteric inhibitors including higher specificity and lower side effects. Allosteric inhibitors against SHP2 are thought to be beneficial not only for diseases related to metabolism, but also for cancer, which make SHP2 a potential drug target. However, high structural sensitivity makes structural optimization of SHP2 allosteric inhibitors face challenges. Herein, we calculated the absolute binding free energy of SHP2 allosteric inhibitors using the FEP method by employing different λ-windows/simulation time sampling strategies. A simulation run with 32 λ-windows/64 ps sampling strategy delivered an excellent correlation (r = 0.96) and an unprecedented low mean absolute error of 0.5 kcal mol-1 between predicted binding free energies and experimental ones, outperforming the MM/PBSA method. Our study demonstrates the possibility to accurately calculate the absolute binding free energy of allosteric inhibitors using FEP, which offers exciting prospects for the discovery of more effective allosteric inhibitors.

Effective Reaction-Based De Novo Strategy for Kinase Targets: A Case Study on MERTK Inhibitors.

Reaction-based de novo design is the computational generation of novel molecular structures by linking building blocks using reaction vectors derived from chemistry knowledge. In this work, we first adopted a recurrent neural network (RNN) model to generate three groups of building blocks with different functional groups and then constructed an in silico target-focused combinatorial library based on chemical reaction rules. Mer tyrosine kinase (MERTK) was used as a study case. Combined with a scaffold enrichment analysis, 15 novel MERTK inhibitors covering four scaffolds were achieved. Among them, compound 5a obtained an IC50 value of 53.4 nM against MERTK without any further optimization. The efficiency of hit identification could be significantly improved by shrinking the compound library with the fragment iterative optimization strategy and enriching the dominant scaffold in the hinge region. We hope that this strategy can provide new insights for accelerating the drug discovery process.

Drug repositioning: Progress and challenges in drug discovery for various diseases.

Compared with traditional de novo drug discovery, drug repurposing has become an attractive drug discovery strategy due to its low-cost and high efficiency. Through a comprehensive analysis of the candidates that have been identified with drug repositioning potentials, it is found that although some drugs do not show obvious advantages in the original indications, they may exert more obvious effects in other diseases. In addition, some drugs have a synergistic effect to exert better clinical efficacy if used in combination. Particularly, it has been confirmed that drug repositioning has benefits and values on the current public health emergency such as the COVID-19 pandemic, which proved the great potential of drug repositioning. In this review, we systematically reviewed a series of representative drugs that have been repositioned for different diseases and illustrated successful cases in each disease. Especially, the mechanism of action for the representative drugs in new indications were explicitly explored for each disease, we hope this review can provide important insights for follow-up research.

Hepatic Artery Injection of 131I-Metuximab Combined with Transcatheter Arterial Chemoembolization for Unresectable Hepatocellular Carcinoma: A Prospective Nonrandomized, Multicenter Clinical Trial.

This prospective nonrandomized, multicenter clinical trial was performed to investigate the efficacy and safety of 131I-labeled metuximab in adjuvant treatment of unresectable hepatocellular carcinoma. Methods: Patients were assigned to treatment with transcatheter arterial chemoembolization (TACE) combined with 131I-metuximab or TACE alone. The primary outcome was overall tumor recurrence. The secondary outcomes were safety and overall survival. Results: The median time to tumor recurrence was 6 mo in the TACE + 131I-metuximab group (n = 160) and 3 mo in the TACE group (n = 160) (hazard ratio, 0.55; 95% CI, 0.43-0.70; P < 0.001). The median overall survival was 28 mo in the TACE + 131I-metuximab group and 19 mo in the TACE group (hazard ratio, 0.62; 95% CI, 0.47-0.82; P = 0.001). Conclusion: TACE + 131I-metuximab showed a greater antirecurrence benefit, significantly improved the 5-y survival of patients with advanced hepatocellular carcinoma, and was well tolerated by patients.