Design, synthesis, and bioevaluation of SOS1 PROTACs derived from pyrido[2,3-d]pyrimidin-7-one-based SOS1 inhibitor.

Oncogenic KRAS mutations drive an approximately 25 % of all human cancers. Son of Sevenless 1 (SOS1), a critical guanine nucleotide exchange factor, catalyzes the activation of KRAS. Targeting SOS1 degradation has engaged as a promising therapeutic strategy for KRAS-mutant cancers. Herein, we designed and synthesized a series of novel CRBN-recruiting SOS1 PROTACs using the pyrido[2,3-d]pyrimidin-7-one-based SOS1 inhibitor as the warhead. One representative compound 11o effectively induced the degradation of SOS1 in three different KRAS-mutant cancer cell lines with DC50 values ranging from 1.85 to 7.53 nM. Mechanism studies demonstrated that 11o-induced SOS1 degradation was dependent on CRBN and proteasome. Moreover, 11o inhibited the phosphorylation of ERK and displayed potent anti-proliferative activities against SW620, A549 and DLD-1 cells. Further optimization of 11o may provide us promising SOS1 degraders with favorable drug-like properties for developing new chemotherapies targeting KRAS-driven cancers.

Evaluation of a walking school bus program: a cluster randomized controlled trial.

BACKGROUND: The purpose of this study was to investigate the effects of a walking school bus intervention on children's active commuting to school. METHODS: We conducted a randomized controlled trial (RCT) in Houston, Texas (Year 1) and Seattle, Washington (Years 2-4) from 2012 to 2016. The study had a two-arm, cluster randomized design comparing the intervention (walking school bus and education materials) to the control (education materials) over one school year October/November - May/June). Twenty-two schools that served lower income families participated. Outcomes included percentage of days students' active commuting to school (primary, measured via survey) and moderate-to-vigorous physical activity (MVPA, measured via accelerometry). Follow-up took place in May or June. We used linear mixed-effects models to estimate the association between the intervention and outcomes of interest. RESULTS: Total sample was 418 students [Mage=9.2 (SD = 0.9) years; 46% female], 197 (47%) in the intervention group. The intervention group showed a significant increase compared with the control group over time in percentage of days active commuting (ß = 9.04; 95% CI: 1.10, 16.98; p = 0.015) and MVPA minutes/day (ß = 4.31; 95% CI: 0.70, 7.91; p = 0.02). CONCLUSIONS: These findings support implementation of walking school bus programs that are inclusive of school-age children from lower income families to support active commuting to school and improve physical activity. TRAIL REGISTRATION: This RCT is registered at clinicaltrials.gov (NCT01626807).

Antibiotic impregnated catheters and intrathecal antibiotics for CSF shunt infection prevention in children undergoing low-risk CSF shunt surgery.

BACKGROUND: Cerebrospinal fluid (CSF) shunts allow children with hydrocephalus to survive and avoid brain injury (J Neurosurg 107:345-57, 2007; Childs Nerv Syst 12:192-9, 1996). The Hydrocephalus Clinical Research Network implemented non-randomized quality improvement protocols that were shown to decrease infection rates compared to pre-operative prophylactic intravenous antibiotics alone (standard care): initially with intrathecal (IT) antibiotics between 2007-2009 (J Neurosurg Pediatr 8:22-9, 2011), followed by antibiotic impregnated catheters (AIC) in 2012-2013 (J Neurosurg Pediatr 17:391-6, 2016). No large scale studies have compared infection prevention between the techniques in children. Our objectives were to compare the risk of infection following the use of IT antibiotics, AIC, and standard care during low-risk CSF shunt surgery (i.e., initial CSF shunt placement and revisions) in children. METHODS: A retrospective observational cohort study at 6 tertiary care children's hospitals was conducted using Pediatric Health Information System + (PHIS +) data augmented with manual chart review. The study population included children ≤ 18 years who underwent initial shunt placement between 01/2007 and 12/2012. Infection and subsequent CSF shunt surgery data were collected through 12/2015. Propensity score adjustment for regression analysis was developed based on site, procedure type, and year; surgeon was treated as a random effect. RESULTS: A total of 1723 children underwent initial shunt placement between 2007-2012, with 1371 subsequent shunt revisions and 138 shunt infections. Propensity adjusted regression demonstrated no statistically significant difference in odds of shunt infection between IT antibiotics (OR 1.22, 95% CI 0.82-1.81, p = 0.3) and AICs (OR 0.91, 95% CI 0.56-1.49, p = 0.7) compared to standard care. CONCLUSION: In a large, observational multicenter cohort, IT antibiotics and AICs do not confer a statistically significant risk reduction compared to standard care for pediatric patients undergoing low-risk (i.e., initial or revision) shunt surgeries.

SFTSV nucleoprotein mediates DNA sensor cGAS degradation to suppress cGAS-dependent antiviral responses.

Cyclic GMP-AMP synthase (cGAS) is an important DNA pattern recognition receptor that senses double-stranded DNA derived from invading pathogens or self DNA in cytoplasm, leading to an antiviral interferon response. A tick-borne Bunyavirus, severe fever with thrombocytopenia syndrome virus (SFTSV), is an RNA virus that causes a severe emerging viral hemorrhagic fever in Asia with a high case fatality rate of up to 30%. However, it is unclear whether cGAS interacts with SFTSV infection. In this study, we found that SFTSV infection upregulated cGAS RNA transcription and protein expression, indicating that cGAS is an important innate immune response against SFTSV infection. The mechanism of cGAS recognizing SFTSV is by cGAS interacting with misplaced mitochondrial DNA in the cytoplasm. Depletion of mitochondrial DNA significantly inhibited cGAS activation under SFTSV infection. Strikingly, we found that SFTSV nucleoprotein (N) induced cGAS degradation in a dose-dependent manner. Mechanically, N interacted with the 161-382 domain of cGAS and linked the cGAS to LC3. The cGAS-N-LC3 trimer was targeted to N-induced autophagy, and the cGAS was degraded in autolysosome. Taken together, our study discovered a novel antagonistic mechanism of RNA viruses, SFTSV is able to suppress the cGAS-dependent antiviral innate immune responses through N-hijacking cGAS into N-induced autophagy. Our results indicated that SFTSV N is an important virulence factor of SFTSV in mediating host antiviral immune responses. IMPORTANCE: Severe fever with thrombocytopenia syndrome virus (SFTSV) is a tick-borne RNA virus that is widespread in East and Southeast Asian countries with a high fatality rate of up to 30%. Up to now, many cytoplasmic pattern recognition receptors, such as RIG-I, MDA5, and SAFA, have been reported to recognize SFTSV genomic RNA and trigger interferon-dependent antiviral responses. However, current knowledge is not clear whether SFTSV can be recognized by DNA sensor cyclic GMP-AMP synthase (cGAS). Our study demonstrated that cGAS could recognize SFTSV infection via ectopic mitochondrial DNA, and the activated cGAS-stimulator of interferon genes signaling pathway could significantly inhibit SFTSV replication. Importantly, we further uncovered a novel mechanism of SFTSV to inhibit innate immune responses by the degradation of cGAS. cGAS was degraded in N-induced autophagy. Collectively, this study illustrated a novel virulence factor of SFTSV to suppress innate immune responses through autophagy-dependent cGAS degradation.

Reducing IV Antibiotic Duration for Neonatal UTI Using a Clinical Standard Pathway.

OBJECTIVES: Urinary tract infections (UTIs) are the most common bacterial infections in young infants and are traditionally treated with longer intravenous (IV) antibiotic courses. A growing body of evidence supports shorter IV antibiotic courses for young infants. Our primary aim was to decrease the IV antibiotic treatment to 3 days over 2 years for neonates aged 0 to 28 days who have been hospitalized with UTIs. METHODS: Using quality improvement methods, our primary intervention was to implement a revised clinical pathway recommending 3 (previously 7) days of IV antibiotics. Our primary outcome measure was IV antibiotic duration, and the secondary outcomes were length of stay (LOS) and costs. The balancing measure was readmission within 30 days of discharge. Neonates were identified by using International Classification of Diseases diagnosis codes and excluded if they were admitted to the ICU or had a LOS >30 days. We used statistical process control to analyze outcome measures for 4 years before (baseline) and 2 years after the pathway revision (intervention) in February 2020. RESULTS: A total of 93 neonates were hospitalized with UTIs in the baseline period and 41 were hospitalized in the intervention period. We found special cause variation, with a significant decrease in mean IV antibiotic duration from 4.7 (baseline) to 3.1 days (intervention) and a decrease in mean LOS from 5.4 to 3.6 days. Costs did not differ between the baseline and intervention periods. There were 7 readmissions during the baseline period, and 0 during the intervention period. CONCLUSIONS: The implementation of a revised clinical pathway significantly reduced IV antibiotic treatment duration and hospital LOS for neonatal UTIs without an increase in hospital readmissions.

PON3::LCN1 and HTN3::MSANTD3 Gene Fusions With NR4A3/NR4A2 Expression in Salivary Acinic Cell Carcinoma.

Acinic cell carcinoma of the salivary gland (AciCC) is a low-grade carcinoma characterized by the overexpression of the transcription factor nuclear receptor subfamily 4 group A member 3 (NR4A3). AciCC has been the subject of a few molecular research projects. This study delves into AciCC's molecular landscape to identify additional alterations and explore their clinical implications. RNA sequencing and immunohistochemical staining for markers NR4A3/NR4A2, DOG-1, S100, and mammaglobin were utilized on 41 AciCCs and 11 secretory carcinoma (SC) samples. NR4A3 was evident in 35 AciCCs, while the residual 6 were NR4A3-negative and NR4A2-positive; SC samples were consistently NR4A3-negative. A novel fusion, PON3 exon 1- LCN1 exon 5, was detected in 9/41 (21.9%) AciCCs, exhibiting a classical histologic pattern with serous cell components growing in solid sheets alongside the intercalated duct-like component. Clinical follow-up of 39 patients over a median of 59 months revealed diverse prognostic outcomes: 34 patients exhibited no disease evidence, whereas the remaining 5 experienced poorer prognosis, involving local recurrence, lymph node, and distant metastasis, and disease-associated death, 4 of which harbored the PON3::LCN1 fusion. In addition, the HTN3::MSANTD3 fusion was recurrently identified in 7/41 AciCC cases. SC patients lacked both fusions. Immunohistochemistry uncovered differential expression of DOG-1, S100, and mammaglobin across samples, providing nuanced insights into their roles in AciCC. This study accentuates PON3::LCN1 and HTN3::MSANTD3 fusions as recurrent molecular events in AciCC, offering potential diagnostic and prognostic utility and propelling further research into targeted therapeutic strategies.

Enrooted-type metal-support interaction boosting oxygen evolution reaction in acidic media.

Supported metal catalysts with appropriate metal-support interactions (MSIs) hold a great promise for heterogeneous catalysis. However, ensuring tight immobilization of metal clusters/nanoparticles on the support while maximizing the exposure of surface active sites remains a huge challenge. Herein, we report an Ir/WO3 catalyst with a new enrooted-type MSI in which Ir clusters are, unprecedentedly, atomically enrooted into the WO3 lattice. The enrooted Ir atoms decrease the electron density of the constructed interface compared to the adhered (root-free) type, thereby achieving appropriate adsorption toward oxygen intermediates, ultimately leading to high activity and stability for oxygen evolution in acidic media. Importantly, this work provides a new enrooted-type supported metal catalyst, which endows suitable MSI and maximizes the exposure of surface active sites in contrast to the conventional adhered, embedded, and encapsulated types.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Deep learning algorithm-based multimodal MRI radiomics and pathomics data improve prediction of bone metastases in primary prostate cancer.

PURPOSE: Bone metastasis is a significant contributor to morbidity and mortality in advanced prostate cancer, and early diagnosis is challenging due to its insidious onset. The use of machine learning to obtain prognostic information from pathological images has been highlighted. However, there is a limited understanding of the potential of early prediction of bone metastasis through the feature combination method from various sources. This study presents a method of integrating multimodal data to enhance the feasibility of early diagnosis of bone metastasis in prostate cancer. METHODS AND MATERIALS: Overall, 211 patients diagnosed with prostate cancer (PCa) at Gansu Provincial Hospital between January 2017 and February 2023 were included in this study. The patients were randomized (8:2) into a training group (n = 169) and a validation group (n = 42). The region of interest (ROI) were segmented from the three magnetic resonance imaging (MRI) sequences (T2WI, DWI, and ADC), and pathological features were extracted from tissue sections (hematoxylin and eosin [H&E] staining, 10 × 20). A deep learning (DL) model using ResNet 50 was employed to extract deep transfer learning (DTL) features. The least absolute shrinkage and selection operator (LASSO) regression method was utilized for feature selection, feature construction, and reducing feature dimensions. Different machine learning classifiers were used to build predictive models. The performance of the models was evaluated using receiver operating characteristic curves. The net clinical benefit was assessed using decision curve analysis (DCA). The goodness of fit was evaluated using calibration curves. A joint model nomogram was eventually developed by combining clinically independent risk factors. RESULTS: The best prediction models based on DTL and pathomics features showed area under the curve (AUC) values of 0.89 (95% confidence interval [CI], 0.799-0.989) and 0.85 (95% CI, 0.714-0.989), respectively. The AUC for the best prediction model based on radiomics features and combining radiomics features, DTL features, and pathomics features were 0.86 (95% CI, 0.735-0.979) and 0.93 (95% CI, 0.854-1.000), respectively. Based on DCA and calibration curves, the model demonstrated good net clinical benefit and fit. CONCLUSION: Multimodal radiomics and pathomics serve as valuable predictors of the risk of bone metastases in patients with primary PCa.

Reopening schools safely and educating youth (ROSSEY) study: Protocol for a community-based, cluster randomized controlled trial.

INTRODUCTION: ROSSEY is a community-academic partnership aiming to develop and test a COVID-19 risk communication intervention for elementary school students and families in Yakima County, Washington. We describe the ROSSEY study protocol that will be implemented in the Yakima School District. METHODS: Aim 1 is to identify the community's social, ethical, and behavioral needs and resources for students to return to school and maintain onsite learning. We will conduct semi-structured interviews with students and school employees and focus groups with parents. Aim 2 is to evaluate the effectiveness of risk communication on students' school attendance. We will conduct a cluster randomized control trial. We will enroll 14 Yakima School District elementary schools with 900 student participants and randomize the schools into the COVID-19 risk communication intervention or control group. Aim 3 will assess implementation of the risk communication intervention and schools' COVID-19 mitigation strategies. We will use the RE-AIM framework to guide this work, which will entail conducting semi-structured interviews with students and school employees and focus groups with parents. DISCUSSION: Implementation of science-based risk communication can educate the community on the benefits and safety of COVID-19 testing and vaccination. Risk communication may also inform families about the role of COVID-19 testing and vaccines as part of mitigation strategies to allow for safe in-person learning. Schools have extraordinary influence to promote children's health through policy and practice change. Study findings will provide evidence to facilitate policy decisions and best practices at schools that facilitate adoption of COVID-19 risk communication. TRIAL REGISTRATION: ClinicalTrials.govNCT04859699. Registered on April 26, 2021.

Development and validation of a federated learning framework for detection of subphenotypes of multisystem inflammatory syndrome in children.

Background: Multisystem inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection. The highly diverse clinical features of MIS-C necessities characterizing its features by subphenotypes for improved recognition and treatment. However, jointly identifying subphenotypes in multi-site settings can be challenging. We propose a distributed multi-site latent class analysis (dMLCA) approach to jointly learn MIS-C subphenotypes using data across multiple institutions. Methods: We used data from the electronic health records (EHR) systems across nine U.S. children's hospitals. Among the 3,549,894 patients, we extracted 864 patients < 21 years of age who had received a diagnosis of MIS-C during an inpatient stay or up to one day before admission. Using MIS-C conditions, laboratory results, and procedure information as input features for the patients, we applied our dMLCA algorithm and identified three MIS-C subphenotypes. As validation, we characterized and compared more granular features across subphenotypes. To evaluate the specificity of the identified subphenotypes, we further compared them with the general subphenotypes identified in the COVID-19 infected patients. Findings: Subphenotype 1 (46.1%) represents patients with a mild manifestation of MIS-C not requiring intensive care, with minimal cardiac involvement. Subphenotype 2 (25.3%) is associated with a high risk of shock, cardiac and renal involvement, and an intermediate risk of respiratory symptoms. Subphenotype 3 (28.6%) represents patients requiring intensive care, with a high risk of shock and cardiac involvement, accompanied by a high risk of >4 organ system being impacted. Importantly, for hospital-specific clinical decision-making, our algorithm also revealed a substantial heterogeneity in relative proportions of these three subtypes across hospitals. Properly accounting for such heterogeneity can lead to accurate characterization of the subphenotypes at the patient-level. Interpretation: Our identified three MIS-C subphenotypes have profound implications for personalized treatment strategies, potentially influencing clinical outcomes. Further, the proposed algorithm facilitates federated subphenotyping while accounting for the heterogeneity across hospitals.

Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data.

The diagnosis of severe COVID-19 lung infection is important because it carries a higher risk for the patient and requires prompt treatment with oxygen therapy and hospitalization while those with less severe lung infection often stay on observation. Also, severe infections are more likely to have long-standing residual changes in their lungs and may need follow-up imaging. We have developed deep learning neural network models for classifying severe vs. non-severe lung infections in COVID-19 patients on chest radiographs (CXR). A deep learning U-Net model was developed to segment the lungs. Inception-v1 and Inception-v4 models were trained for the classification of severe vs. non-severe COVID-19 infection. Four CXR datasets from multi-country and multi-institutional sources were used to develop and evaluate the models. The combined dataset consisted of 5748 cases and 6193 CXR images with physicians' severity ratings as reference standard. The area under the receiver operating characteristic curve (AUC) was used to evaluate model performance. We studied the reproducibility of classification performance using the different combinations of training and validation data sets. We also evaluated the generalizability of the trained deep learning models using both independent internal and external test sets. The Inception-v1 based models achieved AUC ranging between 0.81 ± 0.02 and 0.84 ± 0.0, while the Inception-v4 models achieved AUC in the range of 0.85 ± 0.06 and 0.89 ± 0.01, on the independent test sets, respectively. These results demonstrate the promise of using deep learning models in differentiating COVID-19 patients with severe from non-severe lung infection on chest radiographs.

Computed tomography-based nomogram of Siewert type II/III adenocarcinoma of esophagogastric junction to predict response to docetaxel, oxaliplatin and S-1.

BACKGROUND: Neoadjuvant chemotherapy (NAC) has become the standard care for advanced adenocarcinoma of esophagogastric junction (AEG), although a part of the patients cannot benefit from NAC. There are no models based on baseline computed tomography (CT) to predict response of Siewert type II or III AEG to NAC with docetaxel, oxaliplatin and S-1 (DOS). AIM: To develop a CT-based nomogram to predict response of Siewert type II/III AEG to NAC with DOS. METHODS: One hundred and twenty-eight consecutive patients with confirmed Siewert type II/III AEG underwent CT before and after three cycles of NAC with DOS, and were randomly and consecutively assigned to the training cohort (TC) (n = 94) and the validation cohort (VC) (n = 34). Therapeutic effect was assessed by disease-control rate and progressive disease according to the Response Evaluation Criteria in Solid Tumors (version 1.1) criteria. Possible prognostic factors associated with responses after DOS treatment including Siewert classification, gross tumor volume (GTV), and cT and cN stages were evaluated using pretherapeutic CT data in addition to sex and age. Univariate and multivariate analyses of CT and clinical features in the TC were performed to determine independent factors associated with response to DOS. A nomogram was established based on independent factors to predict the response. The predictive performance of the nomogram was evaluated by Concordance index (C-index), calibration and receiver operating characteristics curve in the TC and VC. RESULTS: Univariate analysis showed that Siewert type (52/55 vs 29/39, P = 0.005), pretherapeutic cT stage (57/62 vs 24/32, P = 0.028), GTV (47.3 ± 27.4 vs 73.2 ± 54.3, P = 0.040) were significantly associated with response to DOS in the TC. Multivariate analysis of the TC also showed that the pretherapeutic cT stage, GTV and Siewert type were independent predictive factors related to response to DOS (odds ratio = 4.631, 1.027 and 7.639, respectively; all P < 0.05). The nomogram developed with these independent factors showed an excellent performance to predict response to DOS in the TC and VC (C-index: 0.838 and 0.824), with area under the receiver operating characteristic curve of 0.838 and 0.824, respectively. The calibration curves showed that the practical and predicted response to DOS effectively coincided. CONCLUSION: A novel nomogram developed with pretherapeutic cT stage, GTV and Siewert type predicted the response of Siewert type II/III AEG to NAC with DOS.

Retracted: Resveratrol Suppresses Human Nasopharyngeal Carcinoma Cell Growth Via Inhibiting Differentiation Antagonizing Non-Protein Coding RNA (DANCR) Expression.

The authors have requested retraction due to the identification of errors in the data. Reference: Jiafeng Zhang, Xiaojie Jin, Chuan Zhou, Hui Zhao, Ping He, Yalin Hao, Qiongna Dong. Resveratrol Suppresses Human Nasopharyngeal Carcinoma Cell Growth Via Inhibiting Differentiation Antagonizing Non-Protein Coding RNA (DANCR) Expression. Med Sci Monit, 2020; 26: e923622. DOI: 10.12659/MSM.923622.

Local-environment-guided selection of atomic structures for the development of machine-learning potentials.

Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs.

Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.

The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is an increasing interest about utilizing artificial intelligence techniques to improve the efficiency of disorder diagnosis in recent years. However, these methods rely only on neuroimaging data for disorder diagnosis and do not explore the pathogenic mechanism behind the disorder or provide an interpretable result toward the diagnosis decision. Furthermore, the scarcity of medical data limits the performance of existing methods. As the hot application of graph neural networks (GNNs) in molecular graphs and drug discovery due to its strong graph-structured data learning ability, whether GNNs can also play a huge role in the field of brain disorder analysis. Thus, in this work, we innovatively model brain neuroimaging data into graph-structured data and propose knowledge distillation (KD) guided brain subgraph neural networks to extract discriminative subgraphs between patient and healthy brain graphs to explain which brain regions and abnormal functional connectivities cause the disorder. Specifically, we introduce the KD technique to transfer the knowledge of pretrained teacher model to guide brain subgraph neural networks training and alleviate the problem of insufficient training data. And these discriminative subgraphs are conducive to learn better brain graph-level representations for disorder prediction. We conduct abundant experiments on two functional magnetic resonance imaging datasets, i.e., Parkinson's disease (PD) and attention-deficit/hyperactivity disorder (ADHD), and experimental results well demonstrate the superiority of our method over other brain graph analysis methods for disorder prediction accuracy. The interpretable experimental results given by our method are consistent with corresponding medical research, which is encouraging to provide a potential for deeper brain disorder study.

Machine learning for data-driven design of high-safety lithium metal anode.

Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open-sourced databases and calculating their microscopic properties. We then detail procedures for developing a machine learning model for predicting the ionic diffusion barrier and preparing the inputs for prediction. This protocol enables a cost-effective workflow to identify promising self-assembled monolayers with exceptional performance. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).1.

Clinical outcomes and risk factor analysis of dental implants inserted with lateral maxillary sinus floor augmentation: A 3- to 8-year retrospective study.

AIM: To evaluate the 3- to 8-year outcomes of dental implants placed with lateral sinus floor augmentation (LSFA) and to identify factors affecting implant survival. MATERIALS AND METHODS: This retrospective study was performed by screening all implants placed with LSFA procedures, which were conducted between January 2012 and December 2016. Subantral bone gain (SABG) and apical bone height (ABH) were assessed using panoramic radiographs. The cumulative survival rate of implants was analysed using life-table analysis and Kaplan-Meier survival curves. The influential risk factors affecting survival were assessed using univariate log-rank tests and multivariable mixture cure rate model. Implant complications were recorded. RESULTS: Based on the established criteria, a total of 449 patients (760 implants) were included in this study. In the 3- to 8-year follow-up (mean ± SD, 5.81 ± 1.33 years), 15 implants in 14 patients failed, with a CRS of 96.81% on an implant basis and 95.07% on a patient basis. A history of periodontitis and poor compliance with supportive periodontal treatment was associated with a significantly higher risk of implant failure at both implant and patient levels. Significant decreases in ABH occurred during each yearly interval except for 3 years. A similar trend has been observed for SABG at 1, 2, 6 and 8 years. The total complication rate was 31.84% on implant basis, with peri-implant mucositis (21.58%) being the most frequent biologic complication and porcelain cracking (5.00%) being the most common technical complication. CONCLUSIONS: Implant with LSFA is a reliable treatment option in atrophic maxilla. A history of periodontitis without regular supportive periodontal treatment was identified as a predictor for implant failure. Slight but significant shrinkage of vertically augmented bone can be observed after implant placement.

Utilization trends in cerebrospinal fluid shunt infection prevention techniques in the United States from 2007 to 2015.

OBJECTIVE: The objective of this study was to describe trends in the utilization of infection prevention techniques (standard care, intrathecal [IT] antibiotics, antibiotic-impregnated catheters [AICs], and combination of IT antibiotics and AICs) among participating hospitals over time. METHODS: This retrospective cohort study at six large children's hospitals between 2007 and 2015 included children ≤ 18 years of age who underwent initial shunt placement between 2007 and 2012. Pediatric Health Information System + (PHIS+) data were augmented with chart review data for all shunt surgeries that occurred prior to the first shunt infection. The Pearson chi-square test was used to test for differences in outcomes. RESULTS: In total, 1723 eligible children had initial shunt placement between 2007 and 2012, with 3094 shunt surgeries through 2015. Differences were noted between hospitals in gestational age, etiology of hydrocephalus, and race and ethnicity, but not sex, weight at surgery, and previous surgeries. Utilization of infection prevention techniques varied across participating hospitals. Hydrocephalus Clinical Research Network hospitals used more IT antibiotics in 2007-2011; after 2012, increasing adoption of AICs was observed in most hospitals. CONCLUSIONS: A consistent trend of decreasing IT antibiotic use and increased AIC utilization was observed after 2012, except for hospital B, which consistently used AICs.