Growth Process of Fe-O Nanoclusters with Different Sizes Biosynthesized by Protein Nanocages.

All protein-directed syntheses of metal nanoclusters (NCs) and nanoparticles (NPs) have attracted considerable attention because protein scaffolds provide a unique metal coordination environment and can adjust the shape and morphology of NCs and NPs. However, the detailed formation mechanisms of NCs or NPs directed by protein templates remain unclear. In this study, by taking advantage of the ferritin nanocage as a biotemplate to monitor the growth of Fe-O NCs as a function of time, we synthesized a series of iron NCs with different sizes and shapes and subsequently solved their corresponding three-dimensional atomic-scale structures by X-ray protein crystallography and cryo-electron microscopy. The time-dependent structure analyses revealed the growth process of these Fe-O NCs with the 4-fold channel of ferritin as nucleation sites. To our knowledge, the newly biosynthesized Fe35O23Glu12 represents the largest Fe-O NCs with a definite atomic structure. This study contributes to our understanding of the formation mechanism of iron NCs and provides an effective method for metal NC synthesis.

Resveratrol regulates Thoc5 to improve maternal immune activation-induced autism-like behaviors in adult mouse offspring.

Maternal infection during pregnancy is an important cause of autism spectrum disorder (ASD) in offspring, and inflammatory infiltration caused by maternal immune activation (MIA) can cause neurodevelopmental disorders in the fetus. Medicine food homologous (MFH) refers to a Traditional Chinese Medicine (TCM) concept, which effectively combines food functions and medicinal effects. However, no previous study has screened, predicted, and validated the potential targets of MFH herbs for treating ASD. Therefore, in this study, we used comprehensive bioinformatics methods to screen and analyze MFH herbs and drug targets on a large scale, and identified resveratrol and Thoc5 as the best small molecular ingredient and drug target, respectively, for the treatment of MIA-induced ASD. Additionally, the results of in vitro experiments revealed that resveratrol increased the expression of Thoc5 and effectively inhibited lipopolysaccharide-induced inflammatory factor production by BV2 cells. Moreover, in vivo, resveratrol increased the expression of Thoc5 and effectively inhibited placental and fetal brain inflammation in MIA pregnancy mice, and improved ASD-like behaviors in offspring.

Potential Anti-Inflammatory Constituents from Aesculus wilsonii Seeds.

A chemical study of Aesculus wilsonii Rehd. (also called Suo Luo Zi) and the in vitro anti-inflammatory effects of the obtained compounds was conducted. Retrieving results through SciFinder showed that there were four unreported compounds, aeswilosides I-IV (1-4), along with fourteen known isolates (5-18). Their structures were elucidated by extensive spectroscopic methods such as UV, IR, NMR, [α]D, and MS spectra, as well as acid hydrolysis. Among the known ones, compounds 5, 6, 8-10, and 12-16 were obtained from the Aesculus genus for the first time; compounds 7, 11, 17, and 18 were first identified from this plant. The NMR data of 5 and 18 were reported first. The effects of 1-18 on the release of nitric oxide (NO) from lipopolysaccharide (LPS)-induced RAW264.7 cells were determined. The results showed that at concentrations of 10, 25, and 50 µM, the novel compounds, aeswilosides I (1) and IV (4), along with the known ones, 1-(2-methylbutyryl)phloroglucinyl-glucopyranoside (10) and pisuminic acid (15), displayed significant inhibitory effects on NO production in a concentration-dependent manner. It is worth mentioning that compound 10 showed the best NO inhibitory effect with a relative NO production of 88.1%, which was close to that of the positive drug dexamethasone. The Elisa experiment suggested that compounds 1, 4, 10, and 15 suppressed the release of TNF-α and IL-1ß as well. In conclusion, this study enriches the spectra of compounds with potential anti-inflammatory effects in A. wilsonii and provides new references for the discovery of anti-inflammatory lead compounds, but further mechanistic research is still needed.

Predicting autism spectrum disorder using maternal risk factors: A multi-center machine learning study.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a complex environmental etiology involving maternal risk factors, which have been combined with machine learning to predict ASD. However, limited studies have considered the factors throughout preconception, perinatal, and postnatal periods, and even fewer have been conducted in multi-center. In this study, five predictive models were developed using 57 maternal risk factors from a cohort across ten cities (ASD:1232, typically developing[TD]: 1090). The extreme gradient boosting model performed best, achieving an accuracy of 66.2 % on the external cohort from three cities (ASD:266, TD:353). The most important risk factors were identified as unstable emotions and lack of multivitamin supplementation using Shapley values. ASD risk scores were calculated based on predicted probabilities from the optimal model and divided into low, medium, and high-risk groups. The logistic analysis indicated that the high-risk group had a significantly increased risk of ASD compared to the low-risk group. Our study demonstrated the potential of machine learning models in predicting the risk for ASD based on maternal factors. The developed model provided insights into the maternal emotion and nutrition factors associated with ASD and highlighted the potential clinical applicability of the developed model in identifying high-risk populations.

Yigansan ameliorates maternal immune activation-induced autism-like behaviours by regulating the IL-17A/TRAF6/MMP9 pathway: Network analysis and experimental validation.

BACKGROUND: Maternal immune activation (MIA) is a significant factor inducing to autism spectrum disorder (ASD) in offspring. The fundamental principle underlying MIA is that inflammation during pregnancy impedes fetal brain development and triggers behavioural alterations in offspring. The intricate pathogenesis of ASD renders drug treatment effects unsatisfactory. Traditional Chinese medicine has strong potential due to its multiple therapeutic targets. Yigansan, composed of seven herbs, is one of the few that has been proven to be effective in treating neuro-psychiatric disorders among numerous traditional Chinese medicine compounds, but its therapeutic effect on ASD remains unknown. HYPOTHESIS: Yigansan improves MIA-induced ASD-like behaviours in offspring by regulating the IL-17 signalling pathway. METHODS: Pregnant C57BL/6J mice were intraperitoneally injected with poly(I:C) to construct MIA models and offspring ASD models. Network analysis identified that the IL-17A/TRAF6/MMP9 pathway is a crucial pathway, and molecular docking confirmed the binding affinity between the monomer of Yigansan and target proteins. qRT-PCR and Western blot were used to detect the expression levels of inflammatory factors and pathway proteins, immunofluorescence was used to detect the distribution of IL-17A, and behavioural tests were used to evaluate the ASD-like behaviours of offspring. RESULTS: We demonstrated that Yigansan can effectively alleviate MIA-induced neuroinflammation of adult offspring by regulating the IL-17A/TRAF6/MMP9 pathway, and the expression of IL-17A was reduced in the prefrontal cortex. Importantly, ASD-like behaviours have been significantly improved. Moreover, we identified that quercetin is the effective monomer for Yigansan to exert therapeutic effects. CONCLUSION: Overall, this study was firstly to corroborate the positive therapeutic effect of Yigansan in the treatment of ASD. We elucidated the relevant molecular mechanism and regulatory pathway involved, determined the optimal therapeutic dose and effective monomer, providing new solutions for the challenges of drug therapy for ASD.

Variability of ambient air ammonia in urban Europe (Finland, France, Italy, Spain, and the UK).

This study addressed the scarcity of NH3 measurements in urban Europe and the diverse monitoring protocols, hindering direct data comparison. Sixty-nine datasets from Finland, France, Italy, Spain, and the UK across various site types, including industrial (IND, 8), traffic (TR, 12), urban (UB, 22), suburban (SUB, 12), and regional background (RB, 15), are analyzed to this study. Among these, 26 sites provided 5, or more, years of data for time series analysis. Despite varied protocols, necessitating future harmonization, the average NH3 concentration across sites reached 8.0 ± 8.9 µg/m3. Excluding farming/agricultural hotspots (FAHs), IND and TR sites had the highest concentrations (4.7 ± 3.2 and 4.5 ± 1.0 µg/m3), followed by UB, SUB, and RB sites (3.3 ± 1.5, 2.7 ± 1.3, and 1.0 ± 0.3 µg/m3, respectively) indicating that industrial, traffic, and other urban sources were primary contributors to NH3 outside FAH regions. When referring exclusively to the FAHs, concentrations ranged from 10.0 ± 2.3 to 15.6 ± 17.2 µg/m3, with the highest concentrations being reached in RB sites close to the farming and agricultural sources, and that, on average for FAHs there is a decreasing NH3 concentration gradient towards the city. Time trends showed that over half of the sites (18/26) observed statistically significant trends. Approximately 50 % of UB and TR sites showed a decreasing trend, while 30 % an increasing one. Meta-analysis revealed a small insignificant decreasing trend for non-FAH RB sites. In FAHs, there was a significant upward trend at a rate of 3.51[0.45,6.57]%/yr. Seasonal patterns of NH3 concentrations varied, with urban areas experiencing fluctuations influenced by surrounding emissions, particularly in FAHs. Diel variation showed differing patterns at urban monitoring sites, all with higher daytime concentrations, but with variations in peak times depending on major emission sources and meteorological patterns. These results offer valuable insights into the spatio-temporal patterns of gas-phase NH3 concentrations in urban Europe, contributing to future efforts in benchmarking NH3 pollution control in urban areas.

Untargeted urine metabolomics and machine learning provide potential metabolic signatures in children with autism spectrum disorder.

Background: Complementary to traditional biostatistics, the integration of untargeted urine metabolomic profiling with Machine Learning (ML) has the potential to unveil metabolic profiles crucial for understanding diseases. However, the application of this approach in autism remains underexplored. Our objective was to delve into the metabolic profiles of autism utilizing a comprehensive untargeted metabolomics platform coupled with ML. Methods: Untargeted metabolomics quantification (UHPLC/Q-TOF-MS) was performed for urine analysis. Feature selection was conducted using Lasso regression, and logistic regression, support vector machine, random forest, and extreme gradient boosting were utilized for significance stratification. Pathway enrichment analysis was performed to identify metabolic pathways associated with autism. Results: A total of 52 autistic children and 40 typically developing children were enrolled. Lasso regression identified ninety-two urinary metabolites that significantly differed between the two groups. Distinct metabolites, such as prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, were revealed to be associated with autism through the application of four different ML methods (p<0.05). The alterations observed in the phosphatidylinositol and inositol phosphate metabolism pathways were linked to the pathophysiology of autism (p<0.05). Conclusion: Significant urinary metabolites, including prostaglandin E2, phosphonic acid, lysine, threonine, and phenylalanine, exhibit associations with autism. Additionally, the involvement of the phosphatidylinositol and inositol phosphate pathways suggests their potential role in the pathophysiology of autism.

Novel Cocrystal Structures of Peptide Antagonists Bound to the Human Melanocortin Receptor 4 Unveil Unexplored Grounds for Structure-Based Drug Design.

Melanocortin 4 receptor (MC4-R) antagonists are actively sought for treating cancer cachexia. We determined the structures of complexes with PG-934 and SBL-MC-31. These peptides differ from SHU9119 by substituting His6 with Pro6 and inserting Gly10 or Arg10. The structures revealed two subpockets at the TM7-TM1-TM2 domains, separated by N2857.36. Two peptide series based on the complexed peptides led to an antagonist activity and selectivity SAR study. Most ligands retained the SHU9119 potency, but several SBL-MC-31-derived peptides significantly enhanced MC4-R selectivity over MC1-R by 60- to 132-fold. We also investigated MC4-R coupling to the K+ channel, Kir7.1. Some peptides activated the channel, whereas others induced channel closure independently of G protein coupling. In cell culture studies, channel activation correlated with increased feeding, while a peptide with Kir7.1 inhibitory activity reduced eating. These results highlight the potential for targeting the MC4-R:Kir7.1 complex for treating positive and restrictive eating disorders.

Unveiling the structural mechanisms of nonpeptide ligand recognition and activation in human chemokine receptor CCR8.

The human CC chemokine receptor 8 (CCR8) is an emerging therapeutic target for cancer immunotherapy and autoimmune diseases. Understanding the molecular recognition of CCR8, particularly with nonpeptide ligands, is valuable for drug development. Here, we report three cryo-electron microscopy structures of human CCR8 complexed with Gi trimers in the ligand-free state or activated by nonpeptide agonists LMD-009 and ZK 756326. A conserved Y1.39Y3.32E7.39 motif in the orthosteric binding pocket is shown to play a crucial role in the chemokine and nonpeptide ligand recognition. Structural and functional analyses indicate that the lack of conservation in Y1143.33 and Y1724.64 among the CC chemokine receptors could potentially contribute to the selectivity of the nonpeptide ligand binding to CCR8. These findings present the characterization of the molecular interaction between a nonpeptide agonist and a chemokine receptor, aiding the development of therapeutics targeting related diseases through a structure-based approach.

Economic Evaluation of an Enhanced Post-Discharge Home-Based Care Program for Stroke Survivors.

OBJECTIVES: To examine the cost-effectiveness of an enhanced postdischarge home-based care program for stroke survivors compared with usual care. METHODS: This was a trial-based economic evaluation study. One hundred and sixteen patients with ischemic stroke were recruited from neurology units in a Chinese hospital and randomized into intervention (n = 58) or usual care groups (n = 58). The intervention commenced with predischarge planning and transitioned to home follow-up postdischarge. Trained nurse case managers supported by an interdisciplinary team provided comprehensive assessment, individualized goal setting, and skill training to support home-based rehabilitation for intervention group participants. Standard care was provided to usual care group participants. Total cost and quality-adjusted life-years gained at 3-month (T1), 6-month (T2), and 12-month (T3) follow-ups were calculated. The incremental cost-effectiveness ratios between the groups were obtained. RESULTS: The intervention group showed a significant increase in utility compared with the usual care group at T1 (P = .003), T2 (P = .007), and T3 (P < .001). The average total QALY gain from baseline for the intervention group was higher than for the usual care group at all time points. The likelihood of being cost-effective ranged from 61.9% to 67.2% from the provider perspective, and from 59.7% to 66.8% from the societal perspective. CONCLUSIONS: The results showed that the intervention program was cost-effective with significantly higher quality-adjusted life-years for stroke survivors when compared with usual care. It provides economic evidence to support the development of home-based stroke rehabilitation program, especially in the low- and middle-income countries.

A framework for Frizzled-G protein coupling and implications to the PCP signaling pathways.

The ten Frizzled receptors (FZDs) are essential in Wnt signaling and play important roles in embryonic development and tumorigenesis. Among these, FZD6 is closely associated with lens development. Understanding FZD activation mechanism is key to unlock these emerging targets. Here we present the cryo-EM structures of FZD6 and FZD3 which are known to relay non-canonical planar cell polarity (PCP) signaling pathways as well as FZD1 in their G protein-coupled states and in the apo inactive states, respectively. Comparison of the three inactive/active pairs unveiled a shared activation framework among all ten FZDs. Mutagenesis along with imaging and functional analysis on the human lens epithelial tissues suggested potential crosstalk between the G-protein coupling of FZD6 and the PCP signaling pathways. Together, this study provides an integrated understanding of FZD structure and function, and lays the foundation for developing therapeutic modulators to activate or inhibit FZD signaling for a range of disorders including cancers and cataracts.

A potent neutralizing nanobody targeting a unique epitope on the receptor-binding domain of SARS-CoV-2 spike protein.

SARS-CoV-2 and its variants continue to threaten public health. Nanobodies that block the attachment of the RBD to host cell angiotensin-converting enzyme 2 (ACE2) represent promising drug candidates. In this study, we reported the identification and structural biological characterization of a nanobody from a RBD-immunized alpaca. The nanobody, termed as 2S-1-19, shows outstanding neutralizing activity against both pseudotyped and authentic SARS-CoV-2 viruses. The crystal structure of 2S-1-19 bound to SARS-CoV-2 RBD reveals an epitope that overlaps with the binding site for ACE2. We also showed that 2S-1-19 reserves promising, though compromised, neutralizing activity against the Delta variant and that the trivalent form of 2S-1-19 remarkably increases its neutralizing capacity. Despite this, neither the monomeric or trimeric 2S-1-19 could neutralize the Omicron BA.1.1 variant, possibility due to the E484A and Q493K mutations found within this virus variant. These data provide insights into immune evasion caused by SARS-CoV-2 variants.

Intestinal Symptoms Among Children aged 2-7 Years with Autism Spectrum Disorder in 13 Cities of China.

BACKGROUND: Autism spectrum disorder (ASD) is a multifactorial, pervasive, neurodevelopmental disorder, of which intestinal symptoms collectively represent one of the most common comorbidities. METHODS: In this study, 1,222 children with ASD and 1,206 typically developing (TD) children aged 2-7 years were enrolled from 13 cities in China. Physical measurement and basic information questionnaires were conducted in ASD and TD children. The Childhood Autism Rating Scale (CARS), Social Responsiveness Scale (SRS), and Autism Behavior Checklist (ABC) were used to evaluate the clinical symptoms of children with ASD. The six-item Gastrointestinal Severity Index (6-GSI) was used to evaluate the prevalence of intestinal symptoms in two groups. RESULTS: The detection rates of constipation, stool odor, and total intestinal symptoms in ASD children were significantly higher than those in TD children (40.098% vs. 25.622%, 17.021% vs. 9.287%, and 53.601% vs. 41.294%, respectively). Autistic children presenting with intestinal comorbidity had significantly higher scores on the ABC, SRS, CARS, and multiple subscales than autistic children without intestinal symptoms, suggesting that intestinal comorbidity may exacerbates the core symptoms of ASD children. CONCLUSION: Intestinal dysfunction was significantly more common in autistic than in TD children. This dysfunction may aggravate the core symptoms of children with ASD.

Developmental prediction modeling based on diffusion tensor imaging uncovering age-dependent heterogeneity in early childhood autistic brain.

OBJECTIVE: There has been increasing evidence for atypical white matter (WM) microstructure in autistic people, but findings have been divergent. The development of autistic people in early childhood is clouded by the concurrently rapid brain growth, which might lead to the inconsistent findings of atypical WM microstructure in autism. Here, we aimed to reveal the developmental nature of autistic children and delineate atypical WM microstructure throughout early childhood while taking developmental considerations into account. METHOD: In this study, diffusion tensor imaging was acquired from two independent cohorts, containing 91 autistic children and 100 typically developing children (TDC), aged 4-7 years. Developmental prediction modeling using support vector regression based on TDC participants was conducted to estimate the WM atypical development index of autistic children. Then, subgroups of autistic children were identified by using the k-means clustering method and were compared to each other on the basis of demographic information, WM atypical development index, and autistic trait by using two-sample t-test. Relationship of the WM atypical development index with age was estimated by using partial correlation. Furthermore, we performed threshold-free cluster enhancement-based two-sample t-test for the group comparison in WM microstructures of each subgroup of autistic children with the rematched subsets of TDC. RESULTS: We clustered autistic children into two subgroups according to WM atypical development index. The two subgroups exhibited distinct developmental stages and age-dependent diversity. WM atypical development index was found negatively associated with age. Moreover, an inverse pattern of atypical WM microstructures and different clinical manifestations in the two stages, with subgroup 1 showing overgrowth with low level of autistic traits and subgroup 2 exhibiting delayed maturation with high level of autistic traits, were revealed. CONCLUSION: This study illustrated age-dependent heterogeneity in early childhood autistic children and delineated developmental stage-specific difference that ranged from an overgrowth pattern to a delayed pattern. Trial registration This study has been registered at ClinicalTrials.gov (Identifier: NCT02807766) on June 21, 2016 ( https://clinicaltrials.gov/ct2/show/NCT02807766 ).

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OBJECTIVES: To explore the association between maternal gestational diabetes mellitus (GDM) exposure and the development of autism spectrum disorder (ASD) in offspring. METHODS: A case-control study was conducted, recruiting 221 children with ASD and 400 healthy children as controls. Questionnaires and interviews were used to collect information on general characteristics of the children, socio-economic characteristics of the family, maternal pregnancy history, and maternal disease exposure during pregnancy. Multivariate logistic regression analysis was used to investigate the association between maternal GDM exposure and the development of ASD in offspring. The potential interaction between offspring gender and maternal GDM exposure on the development of ASD in offspring was explored. RESULTS: The proportion of maternal GDM was significantly higher in the ASD group compared to the control group (16.3% vs 9.4%, P=0.014). After adjusting for variables such as gender, gestational age, mode of delivery, parity, and maternal education level, maternal GDM exposure was a risk factor for ASD in offspring (OR=2.18, 95%CI: 1.04-4.54, P=0.038). On the basis of adjusting the above variables, after further adjusting the variables including prenatal intake of multivitamins, folic acid intake in the first three months of pregnancy, and assisted reproduction the result trend did not change, but no statistical significance was observed (OR=1.94, 95%CI: 0.74-5.11, P=0.183). There was an interaction between maternal GDM exposure and offspring gender on the development of ASD in offspring (P<0.001). Gender stratified analysis showed that only in male offspring of mothers with GDM, the risk of ASD was significantly increased (OR=3.67, 95%CI: 1.16-11.65, P=0.027). CONCLUSIONS: Maternal GDM exposure might increase the risk of ASD in offspring. There is an interaction between GDM exposure and offspring gender in the development of ASD in offspring.

Untargeted metabolomics reveals hepatic metabolic disorder in the BTBR mouse model of autism and the significant role of liver in autism.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, and the etiology is unknown. Metabolic dysfunction is present in patients with ASD. In the current study, untargeted metabolomics was employed to screen the differential metabolites in the liver of BTBR mouse model of autism, and MetaboAnalyst 4.0 was used for metabolic pathway analysis. Mice were killed, and liver samples were collected for untargeted metabolomics analysis and examination of histopathology. Finally, 12 differential metabolites were identified. The intensities of phenylethylamine, 4-Guanidinobutanoic acid, leukotrieneD4, and SM(d18:1/24:1(15Z)) were significantly upregulated (p < .01), and the intensities of estradiol, CMP-N-glycoloylneuraminate, retinoyl ß-glucuronide,4-phosphopantothenoylcysteine, aldophosphamide, taurochenodesoxycholic acid, taurocholic acid, and dephospho-CoA were significantly downregulated (p < .01) in the BTBR group compared with C57 control group, indicating that differences between BTBR and C57 groups were observed in metabolic patterns. Disturbed pathways of the BTBR mice involved lipid metabolism, retinol metabolism, and amino acid and energy metabolism, revealing that bile acid-mediated activation of LXRα might contribute to metabolic dysfunction of lipid and leukotriene D4 produced by the activation of 5-LOX led to hepatic inflammation. Pathological changes in the liver tissue, such as hepatocyte vacuolization, and small amounts of inflammatory and cell necrosis, further supported metabolomic results. Moreover, Spearman's rank correlation revealed that there is a strong relationship between metabolites across liver and cortex, suggesting liver may exert action by connecting peripheral and neural systems. These findings were likely to be of pathological importance or a cause/consequence of autism, and may provide insight into key metabolic dysfunction to target potential therapeutic strategies relating to ASD.

Correlation and predictive ability of sensory characteristics and social interaction in children with autism spectrum disorder.

Background: Individuals with autism spectrum disorder (ASD) often have different social characteristics and particular sensory processing patterns, and these sensory behaviors may affect their social functioning. The objective of our study is to investigate the sensory profiles of children with ASD and their association with social behavior. Specifically, we aim to identify the predictive role of sensory processing in social functioning. Methods: The Short Sensory Profile (SSP) was utilized to analyze sensory differences between ASD children and their peers. The Social Responsiveness Scale (SRS) and other clinical scales were employed to assess the social functioning of children with ASD. Additionally, the predictive ability of sensory perception on social performance was discussed using random forest and support vector machine (SVM) models. Results: The SSP scores of ASD children were lower than those of the control group, and there was a significant negative correlation between SSP scores and clinical scale scores (P < 0.05). The random forest and SVM models, using all the features, showed higher sensitivity, while the random forest model with 7-feature factors had the highest specificity. The area under the receiver operating characteristic (ROC) curve (AUC) for all the models was higher than 0.8. Conclusion: Autistic children in our study have different patterns of sensory processing than their peers, which are significantly related to their patterns of social functioning. Sensory features can serve as a good predictor of social functioning in individuals with ASD.

Association of feeding patterns in infancy with later autism symptoms and neurodevelopment: a national multicentre survey.

BACKGROUND: We aimed to compare differences in infant feeding patterns (breastfeeding and complementary food supplementation) between children with the autism spectrum disorder (ASD) and typically developing (TD) children through a multicentre study. The relationship between these patterns and later core symptoms and neurodevelopment in children with ASD was also investigated. METHODS: We analysed breastfeeding and complementary feeding patterns in 1389 children with ASD and 1190 TD children. The Children Neuropsychological and Behavior Scale-Revision 2016 (CNBS-R2016) was used to assess neurodevelopmental levels. The Autism Behavior Checklist (ABC), Social Responsiveness Scale (SRS), Childhood Autism Rating Scale (CARS), and ASD Warning Behavior Subscale of the CNBS-R2016 were used to assess ASD symptoms. RESULTS: Children with ASD had a shorter breastfeeding duration in infancy (8 (3-12) months vs. 10 (6-14) months, P < 0.001), later introduction of complementary foods (P < 0.001), and poorer acceptance of complementary foods (P < 0.001) than TD children. Total ABC and CARS scores were lower in the group of children with ASD who had been breastfed for 12 months or more than in the group who had been breastfed for less than 6 months. Children with ASD who were given complementary food after 6 months had lower general quotient (GQ), adaptive ability, fine motor and language scores than those who were given complementary food within 4-6 months. Children with ASD with poor acceptance of complementary foods had higher ABC and SRS scores and lower gross motor scores than those who had good acceptance. CONCLUSIONS: Children with ASD have a shorter duration of breastfeeding, a later introduction of complementary foods, and poorer acceptance of complementary foods than TD children. These feeding patterns may be related to the symptoms and growth of children with ASD. The research suggests that continued breastfeeding for longer than 12 months may be beneficial in reducing ASD symptoms and that infants who have difficulty introducing complementary foods should be followed up for neurodevelopment. TRIAL REGISTRATION: The ethics committee of the Children's Hospital of Chongqing Medical University approved the study. Approval Number: (2018) IRB (STUDY) NO. 121, and registered in the Chinese Clinical Trial Registry (Registration number: ChiCTR2000031194, registered on 23/03/2020).

Structural basis of selective cannabinoid CB2 receptor activation.

Cannabinoid CB2 receptor (CB2R) agonists are investigated as therapeutic agents in the clinic. However, their molecular mode-of-action is not fully understood. Here, we report the discovery of LEI-102, a CB2R agonist, used in conjunction with three other CBR ligands (APD371, HU308, and CP55,940) to investigate the selective CB2R activation by binding kinetics, site-directed mutagenesis, and cryo-EM studies. We identify key residues for CB2R activation. Highly lipophilic HU308 and the endocannabinoids, but not the more polar LEI-102, APD371, and CP55,940, reach the binding pocket through a membrane channel in TM1-TM7. Favorable physico-chemical properties of LEI-102 enable oral efficacy in a chemotherapy-induced nephropathy model. This study delineates the molecular mechanism of CB2R activation by selective agonists and highlights the role of lipophilicity in CB2R engagement. This may have implications for GPCR drug design and sheds light on their activation by endogenous ligands.