PGCNMDA: learning node representations along paths with graph convolutional network for predicting miRNA-disease associations.

Identifying miRNA-disease associations (MDAs) is crucial for improving the diagnosis and treatment of various diseases. However, biological experiments can be time-consuming and expensive. To overcome these challenges, computational approaches have been developed, with Graph Convolutional Network (GCN) showing promising results in MDA prediction. The success of GCN-based methods relies on learning a meaningful spatial operator to extract effective node feature representations. To enhance the inference of MDAs, we propose a novel method called PGCNMDA, which employs graph convolutional networks with a learning graph spatial operator from paths. This approach enables the generation of meaningful spatial convolutions from paths in GCN, leading to improved prediction performance. On HMDD v2.0, PGCNMDA obtains a mean AUC of 0.9229 and an AUPRC of 0.9206 under 5-fold cross-validation (5-CV), and a mean AUC of 0.9235 and an AUPRC of 0.9212 under 10-fold cross-validation (10-CV), respectively. Additionally, the AUC of PGCNMDA also reaches 0.9238 under global leave-one-out cross-validation (GLOOCV). On HMDD v3.2, PGCNMDA obtains a mean AUC of 0.9413 and an AUPRC of 0.9417 under 5-CV, and a mean AUC of 0.9419 and an AUPRC of 0.9425 under 10-CV, respectively. Furthermore, the AUC of PGCNMDA also reaches 0.9415 under GLOOCV. The results show that PGCNMDA is superior to other compared methods. In addition, the case studies on pancreatic neoplasms, thyroid neoplasms and leukemia show that 50, 50 and 48 of the top 50 predicted miRNAs linked to these diseases are confirmed, respectively. It further validates the effectiveness and feasibility of PGCNMDA in practical applications.

Outer Sphere Electron Transfer Enabling High-Voltage Aqueous Electrolytes.

Aqueous electrolytes with a low voltage window (1.23 V) and prone side reactions, such as hydrogen evolution reaction and cathode dissolution, compromise the advantages of high safety and low cost of aqueous metal-ion batteries. Herein, introducing catechol (CAT) into the aqueous electrolyte, an outer sphere electron transfer mechanism is initiated to inhibit the water reactivity, achieving an electrochemical window of 3.24 V. In a typical Zn-ion battery, the outer sphere electrons jump from CAT to Zn2+-H2O at a geometrically favorable situation and between the solvation molecules without breaking or forming chemical bonds as that of the inner sphere electron transfers. The excited state π-π stacking further leads to the outer sphere electron transfer occurring at the electrolyte/electrode interface. This high-voltage electrolyte allows achieving an operating voltage two times higher than that of the usual aqueous electrolytes and provides almost the highest energy density and power density for the V2O5-based aqueous Zn-ion full batteries. The Zn//Zn symmetric battery delivers a 4000 h lifespan, and the Zn//V2O5 full battery achieves a ∼380 W h kg-1 energy density and a 92% capacity retention after 3000 cycles at 1 A g-1 and a 2.4 V output voltage. This outer sphere electron transfer strategy paves the way for designing high-voltage aqueous electrolytes.

Measuring the Boundary Gapless State and Criticality via Disorder Operator.

The disorder operator is often designed to reveal the conformal field theory (CFT) information in quantum many-body systems. By using large-scale quantum Monte Carlo simulation, we study the scaling behavior of disorder operators on the boundary in the two-dimensional Heisenberg model on the square-octagon lattice with gapless topological edge state. In the Affleck-Kennedy-Lieb-Tasaki phase, the disorder operator is shown to hold the perimeter scaling with a logarithmic term associated with the Luttinger liquid parameter K. This effective Luttinger liquid parameter K reflects the low-energy physics and CFT for (1+1)D boundary. At bulk critical point, the effective K is suppressed but it keeps finite value, indicating the coupling between the gapless edge state and bulk fluctuation. The logarithmic term numerically captures this coupling picture, which reveals the (1+1)D SU(2)_{1} CFT and (2+1)D O(3) CFT at boundary criticality. Our Letter paves a new way to study the exotic boundary state and boundary criticality.

Impact of deep muscle invasion on nodal status and survival in patients with pT2 esophageal squamous cancer.

BACKGROUND: Whether T2 esophageal squamous cell carcinoma should be subclassified remains controversial. We aimed to investigate the impact of the depth of muscularis propria invasion on nodal status and survival outcomes. METHODS: We identified patients with pT2 esophageal squamous cell carcinoma who underwent primary surgery from January 2009 to June 2017. Clinical data were extracted from prospectively maintained databases. Tumor muscularis propria invasion was stratified into superficial or deep. Binary logistic regression was used to determine risk factors for lymph node metastases. The impact of the depth of muscularis propria invasion on survival was investigated using Kaplan‒Meier analysis and a Cox proportional hazard regression model. RESULTS: A total of 750 patients from three institutes were investigated. The depth of muscularis propria invasion (odds ratio [OR]: 3.95, 95% confidence interval [CI]: 2.46-6.35; p < 0.001) was correlated with lymph node metastases using logistic regression. T substage (hazard ratio [HR]: 1.37, 95% CI: 1.05-1.79; p < 0.001) and N status (HR: 1.51, 95% CI: 1.05-2.17; p < 0.001) were independent risk factors in multivariate Cox regression analysis. The deep muscle invasion was associated with worse overall survival (HR: 1.52, 95% CI: 1.19-1.94; p = 0.001) than superficial, specifically in T2N0 patients (HR: 1.38, 95% CI: 1.08-1.94; p = 0.035). CONCLUSIONS: We found that deep muscle invasion was associated with significantly worse outcomes and recommended the substaging of pT2 esophageal squamous cell carcinoma in routine pathological examination.

Identifying Human miRNA Target Sites via Learning the Interaction Patterns between miRNA and mRNA Segments.

miRNAs (microRNAs) target specific mRNA (messenger RNA) sites to regulate their translation expression. Although miRNA targeting can rely on seed region base pairing, animal miRNAs, including human miRNAs, typically cooperate with several cofactors, leading to various noncanonical pairing rules. Therefore, identifying the binding sites of animal miRNAs remains challenging. Because experiments for mapping miRNA targets are costly, computational methods are preferred for extracting potential miRNA-mRNA fragment binding pairs first. However, existing prediction tools can have significant false positives due to the prevalent noncanonical miRNA binding behaviors and the information-biased training negative sets that were used while constructing these tools. To overcome these obstacles, we first prepared an information-balanced miRNA binding pair ground-truth data set. A miRNA-mRNA interaction-aware model was then designed to help identify miRNA binding events. On the test set, our model (auROC = 94.4%) outperformed existing models by at least 2.8% in auROC. Furthermore, we showed that this model can suggest potential binding patterns for miRNA-mRNA sequence interacting pairs. Finally, we made the prepared data sets and the designed model available at http://cosbi2.ee.ncku.edu.tw/mirna_binding/download.

Design and synthesis 1H-Pyrrolo[2,3-b]pyridine derivatives as FLT3 inhibitors for the treatment of Acute myeloid Leukemia.

Acute myeloid leukemia (AML) is the most common type of blood cancer and has been strongly correlated with the overexpression of Fms-like tyrosine kinase 3 (FLT3), a member of the class III receptor tyrosine kinase family. With the emergence of FLT3 internal tandem duplication alteration (ITD) and tyrosine kinase domain (TKD) mutations, the development of FLT3 small molecule inhibitors has become an effective medicinal chemistry strategy for AML. Herein, we have designed and synthesized two series of 1H-pyrrolo[2,3-b]pyridine derivatives CM1-CM24, as FLT3 inhibitors based on F14, which we previously reported, that can target the hydrophobic FLT3 back pocket. Among these derivates, CM5 showed significant inhibition of FLT3 and FLT3-ITD, with inhibitory percentages of 57.72 % and 53.77 % respectively at the concentration of 1 µΜ. Furthermore, CM5 demonstrated potent inhibition against FLT3-dependent human AML cell lines MOLM-13 and MV4-11 (both harboring FLT3-ITD mutant), with IC50 values of 0.75 µM and 0.64 µM respectively. In our cellular mechanistic studies, CM5 also effectively induces apoptosis by arresting cell cycle progression in the G0/G1 phase. In addition, the amide and urea linker function were discussed in detail based on computational simulations studies. CM5 will serve as a novel lead compound for further structural modification and development of FLT3 inhibitors specifically targeting AML with FLT3-ITD mutations.

Improved Mobility-Stretchability Properties of Diketopyrrolopyrrole-Based Conjugated Polymers with Diastereomeric Conjugation Break Spacers.

Stretchable conjugated polymers with conjugation break spacers (CBSs) synthesized via random terpolymerization have gained considerable attention because of their efficacy in modulating mobility and stretchability. This study incorporates a series of dianhydrohexitol diastereomers of isosorbide (ISB) and isomannide (IMN) units into the diketopyrrolopyrrole-based backbone as CBSs. It is found that the distorted CBS (IMN) improves the mobility-stretchability properties of the polymer with a highly coplanar backbone, whereas the extended CBS (ISB) enhances those of the polymer with a noncoplanar backbone. Additionally, the different configurations of ISB and IMN sufficiently affect the solid-state packing, aggregation capabilities, crystallographic parameters, and mobility-stretchability properties of the polymer. The IMN-based polymers exhibit the highest mobility of 1.69 cm2 V-1 s-1 and crystallinity retentions of (85.7, 78.6)% under 20% and 60% strains, outperforming their ISB-based or unmodified counterparts. The improvement is correlated with a robust aggregation capability. Furthermore, the CBS content affects aggregation behavior, notably affecting mobility. This result indicates that incorporating CBSs into the polymer can enhance backbone flexibility via movement and rotation of the CBS without affecting the crystalline regions.

Investigating the Mobility-Compressibility Properties of Conjugated Polymers by the Contact Film Transfer Method with Prestrain.

Up to now, researches on the mobility-stretchability of semiconducting polymers are extensively investigated, but little attention was  paid to their morphology and field-effect transistor characteristics under compressive strains, which is equally crucial in wearable electronic applications. In this work, a contact film transfer method is applied to evaluate the mobility-compressibility properties of conjugated polymers. A series of isoindigo-bithiophene conjugated polymers with symmetric carbosilane side chains (P(SiSi)), siloxane-terminated alkyl side chains (P(SiOSiO)), and combined asymmetric side chains (P(SiOSi)) are investigated. Accordingly, a compressed elastomer slab is used to transfer and compress the polymer films by releasing prestrain, and the morphology and mobility evolutions of these polymers are tracked. It is found that P(SiOSi) outperforms the other symmetric polymers including P(Si─Si) and P(SiO─SiO), having the ability to dissipate strain with its shortened lamellar spacing and orthogonal chain alignment. Notably, the mechanical durability of P(SiOSi) is also enhanced after consecutive compress-release cycles. In addition, the contact film transfer technique is demonstrated to be applicable to investigate the compressibility of different semiconducting polymers. These results demonstrate a comprehensive approach to understand the mobility-compressibility properties of semiconducting polymers under tensile and compressive strains.

Biochemical and protein nutritional potential of mulberry (Morus alba L.) leaf: partial substitution improves the nutrition of conventional protein.

BACKGROUND: With the requirements of environmental, cost and economic sustainability, new sources of alternative proteins in the livestock industry are receiving increasing attention. Mulberry (Morus alba L.) leaves are a unique feed resource because of their high protein content and large availability. Therefore, mining sustainable protein suitable for the animal husbandry industry in sericulture resources could achieve a win-win situation. RESULTS: The protein content in mulberry leaves is 232.10-386.16 g kg-1 , and the mean value of crude fat content is 43.76 ± 8.48 g kg-1 , which has the advantages of protein content and energy. In addition, the average content of phytic acid in mulberry leaves is only 1.88 ± 0.56 g kg-1 , which means that it is not inhibited in terms of nutrient absorption. Meanwhile, the digestibility of protein was Bean pulp > Sample 8 ≈ Alfalfa ≈ Sample 13 ≈ Cottonseed meal > Fish meal, and the ß-turn and particle size of mulberry leaf protein are more conducive to digestion in vitro. Furthermore, the protein of Sample 13 had the richest essential amino acids (252.00 g kg-1 ) and the highest essential amino acid index (EAAI), which was superior to conventional feed protein. In addition, the partial substitution of mulberry leaf protein (15%) significantly increased the EAAI value of conventional feed protein. However, to balance nutrition, it is necessary to combine mulberry leaf protein with other proteins to further broaden its application field. CONCLUSION: Mulberry leaves are a new source of feed protein, which helps to alleviate the two major problems of mulberry resource surplus and feed protein resource shortage. © 2023 Society of Chemical Industry.

Joint modeling and multiple comparisons with the best of data from a SMART with survival outcomes.

A dynamic treatment regimen (DTR) is a sequence of decision rules that can alter treatments or doses based on outcomes from prior treatment. In the case of two lines of treatment, a DTR specifies first-line treatment, and second-line treatment for responders and treatment for non-responders to the first-line treatment. A sequential, multiple assignment, randomized trial (SMART) is one such type of trial that has been designed to assess DTRs. The primary goal of our project is to identify the treatments, covariates, and their interactions result in the best overall survival rate. Many previously proposed methods to analyze data with survival outcomes from a SMART use inverse probability weighting and provide non-parametric estimation of survival rates, but no other information. Other methods have been proposed to identify and estimate the optimal DTR, but inference issues were seldom addressed. We apply a joint modeling approach to provide unbiased survival estimates as a mechanism to quantify baseline and time-varying covariate effects, treatment effects, and their interactions within regimens. The issue of multiple comparisons at specific time points is addressed using multiple comparisons with the best method.

High Curvature PEDOT:PSS Transport Layer Toward Enhanced Perovskite Light-Emitting Diodes.

The rapidly developed metal halide perovskite light-emitting diodes (PeLEDs) are considered as a promising candidate for next-generation display and illumination, but the unbalanced charge transport is still a hard-treat case to restrict its efficiency and operational stability. Here, a high curvature PEDOT:PSS transport layer is demonstrated via the self-assembly island-like structures by the incorporation of alkali metal salts. Benefiting from the dielectric confinement effect of the high curvature surface, the modified CsPbBr3 -based PeLEDs present a 2.1 times peak external quantum efficiency (EQE) from 6.75% to 14.23% and a 3.3 times half lifetime (T50 ) from 3.96 to 13.01 h. Besides, the PeLEDs show high luminance up to 44834 cd m-2 . Evidently, this work may provide a deep insight into the structure-activity relationship between the micro-structures at the PEDOT:PSS/perovskite interface and the performance of PeLEDs, and crack the codes for ameliorating the performance of PeLEDs via interfacial micro-structured regulation.

A survey on predicting microbe-disease associations: biological data and computational methods.

Various microbes have proved to be closely related to the pathogenesis of human diseases. While many computational methods for predicting human microbe-disease associations (MDAs) have been developed, few systematic reviews on these methods have been reported. In this study, we provide a comprehensive overview of the existing methods. Firstly, we introduce the data used in existing MDA prediction methods. Secondly, we classify those methods into different categories by their nature and describe their algorithms and strategies in detail. Next, experimental evaluations are conducted on representative methods using different similarity data and calculation methods to compare their prediction performances. Based on the principles of computational methods and experimental results, we discuss the advantages and disadvantages of those methods and propose suggestions for the improvement of prediction performances. Considering the problems of the MDA prediction at present stage, we discuss future work from three perspectives including data, methods and formulations at the end.

PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features.

MOTIVATION: Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types. RESULTS: In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations. AVAILABILITY AND IMPLEMENTATION: Data and source codes are available at https://github.com/27167199/PDMDA.

Circulating Tumor Cells are an Independent Risk Factor for Poor Prognosis in Patients with Gallbladder Adenocarcinoma.

BACKGROUND: The study aimed to evaluate the prognostic impact of circulating tumor cells (CTCs) in patients with gallbladder adenocarcinoma after resection. MATERIALS AND METHODS: Between January 2018 and January 2021, 101 consecutive patients with gallbladder adenocarcinoma were included. CTCs were detected and enumerated using the CanPatrol® technique. The follow-up period ended in January 2023. The cancer-specific survival (CSS) and disease-free survival (DFS) were calculated using log-rank and Cox regression analyses. RESULTS: CTCs were detected positively in 61.54% (8/13) of the patients in the non-operation group and 13.64% (12/88) in the operation group. In the operation group, the median CSS for CTCs-positive and CTCs-negative patients was 5.0 and 9.5 months (P < 0.001), respectively, and DFS was 2.8 and 5.0 months at stage III (P < 0.001), respectively. In the non-operation group, the median CSS for CTCs-positive and CTCs-negative patients was 3.5 and 6.5 months (P = 0.0031), respectively. The median CSS for CTCs-positive patients in the operation group was similar to that in the non-operation group (P = 0.67). Multivariate analyses showed that positive CTCs was an independent risk factor for poor CSS (HR 0.066, 95% CI 0.021-0.206, P < 0.001) as well as lymph infiltration (HR 0.320, 95% CI 0.110-0.930, P = 0.036), without R0 curative resection (HR 7.520, 95% CI 2.100-26.931, P = 0.002), and without adjuvant chemotherapy (HR 7.730, 95% CI 2.416-24.731, P < 0.001). CONCLUSION: Positive CTCs was an independent predictor of poor prognosis after resection in patients with gallbladder adenocarcinoma. Preoperative detection of CTCs may play an important guiding role in formulating treatment strategies for these patients.

Emergent Glassy Behavior in a Kagome Rydberg Atom Array.

We present large-scale quantum Monte Carlo simulation results on a realistic Hamiltonian of kagome-lattice Rydberg atom arrays. Although the system has no intrinsic disorder, intriguingly, our analyses of static and dynamic properties on large system sizes reveal emergent glassy behavior in a region of parameter space located between two valence bond solid phases. The extent of this glassy region is demarcated using the Edwards-Anderson order parameter, and its phase transitions to the two proximate valence bond solids-as well as the crossover towards a trivial paramagnetic phase-are identified. We demonstrate the intrinsically slow (imaginary) time dynamics deep inside the glassy phase and discuss experimental considerations for detecting such a quantum disordered phase with numerous nearly degenerate local minima. Our proposal paves a new route to the study of real-time glassy phenomena and highlights the potential for quantum simulation of a distinct phase of quantum matter beyond solids and liquids in current-generation Rydberg platforms.

Development of an MRI-Based Comprehensive Model Fusing Clinical, Radiomics and Deep Learning Models for Preoperative Histological Stratification in Intracranial Solitary Fibrous Tumor.

BACKGROUND: Accurate preoperative histological stratification (HS) of intracranial solitary fibrous tumors (ISFTs) can help predict patient outcomes and develop personalized treatment plans. However, the role of a comprehensive model based on clinical, radiomics and deep learning (CRDL) features in preoperative HS of ISFT remains unclear. PURPOSE: To investigate the feasibility of a CRDL model based on magnetic resonance imaging (MRI) in preoperative HS in ISFT. STUDY TYPE: Retrospective. POPULATION: Three hundred and ninety-eight patients from Beijing Tiantan Hospital, Capital Medical University (primary training cohort) and 49 patients from Lanzhou University Second Hospital (external validation cohort) with ISFT based on histopathological findings (237 World Health Organization [WHO] tumor grade 1 or 2, and 210 WHO tumor grade 3). FIELD STRENGTH/SEQUENCE: 3.0 T/T1-weighted imaging (T1) by using spin echo sequence, T2-weighted imaging (T2) by using fast spin echo sequence, and T1-weighted contrast-enhanced imaging (T1C) by using two-dimensional fast spin echo sequence. ASSESSMENT: Area under the receiver operating characteristic curve (AUC) was used to assess the performance of the CRDL model and a clinical model (CM) in preoperative HS in the external validation cohort. The decision curve analysis (DCA) was used to evaluate the clinical net benefit provided by the CRDL model. STATISTICAL TESTS: Cohen's kappa, intra-/inter-class correlation coefficients (ICCs), Chi-square test, Fisher's exact test, Student's t-test, AUC, DCA, calibration curves, DeLong test. A P value <0.05 was considered statistically significant. RESULTS: The CRDL model had significantly better discrimination ability than the CM (AUC [95% confidence interval, CI]: 0.895 [0.807-0.912] vs. 0.810 [0.745-0.874], respectively) in the external validation cohort. The CRDL model can provide a clinical net benefit for preoperative HS at a threshold probability >20%. DATA CONCLUSION: The proposed CRDL model holds promise for preoperative HS in ISFT, which is important for predicting patient outcomes and developing personalized treatment plans. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Enhancing the Performance of Electret-Free Phototransistor Memory by Using All-Conjugated Block Copolymer.

Conjugated polymers are of great interest owing to their potential in stretchable electronics to function under complex deformation conditions. To improve the performance of conjugated polymers, various structural designs have been proposed and these conjugated polymers are specially applied in exotic optoelectronics. In this work, a series of all-conjugated block copolymers (PII2T-b-PNDI2T) comprising poly(isoindigo-bithiophene) (PII2T) and poly(naphthalenediimide-bithiophene) (PNDI2T) are developed with varied compositions and applied to electret-free phototransistor memory. Accordingly, these memory devices present p-type transport capability and electrical-ON/photo-OFF memory behavior. The efficacy of the all-conjugated block copolymer design in improving the memory-photoresponse properties in phototransistor memory is revealed. By optimizing the composition of the block copolymer, the corresponding device achieves a wide memory window of 36 V and a high memory ratio of 7 × 104 . Collectively, the results of this study indicate a new concept for designing electret-free phototransistor memory by using all-conjugated block copolymer heterojunctions to mitigate the phase separation of conjugated polymer blends. Meanwhile, the intrinsic optoelectronic properties of the constituent conjugated polymers can be well-maintained by using an all-conjugated block copolymer design.

Overcoming the barrier: designing novel thermally robust shape memory vitrimers by establishing a new machine learning framework.

Shape memory vitrimers (SMVs) are an emerging class of advanced materials that have garnered significant interest from researchers in the past five to six years. These materials can return to their original shape when exposed to a stimulus, while also healing damage they have sustained. However, achieving both high healing/recycling efficiency and a high glass transition temperature (Tg) in SMVs has been challenging, due to the conflicting requirements between molecular chain mobility and the formation and reaction of dynamic covalent bond exchange. Based on the understanding of chemo-physical properties, this study first leverages machine learning (ML), involving supervised and unsupervised learning approaches, to navigate this complex design space of SMVs. Furthermore, we elaborated the basic mathematical frameworks of ML approaches and comprehensively compared their performances. Based on the best performing model, we designed four types of thermally robust shape memory vitrimers (TRSMVs), which boast high recycling efficiency, elevated Tg, and exemplary shape memory effects, overcoming conventional barriers. One of the discovered samples exhibited outstanding performance with a Tg of 233.5 °C, a recycling efficiency of 84.1%, and a recovery stress of 33 MPa in experiments. It aligns well with ML predictions, showcasing the potential of our ML framework in driving innovative materials design and advancing the field of smart polymers.

Incidence and risk factors associated with negative postoperative behavioral changes in children undergoing painless gastroscopy.

OBJECTIVE: To investigate the incidence of and risk factors associated with negative postoperative behavioral changes (NPOBCs) in children undergoing painless gastroscopy. METHODS: Inclusion criteria: ASA I-II and outpatients aged 6-12 years undergoing painless gastroscopy. EXCLUSION CRITERIA: history of surgery or anesthesia, children with developmental or intellectual abnormalities, refusal to participate, preoperative abdominal pain score > 3 points, history of chronic abdominal pain of > 3 months duration, and serious intraoperative complications. On the 1st, 14th, and 30th day after the gastroscopy, the Post Hospitalization Behavior Questionnaire for Ambulatory Surgery (PHBQ-AS) was used to assess NPOBCs in children. RESULTS: A total of 1,670 children were included in this prospective observational cohort study. The incidence rates of NPOBCs were 14.13%, 4.55%, and 2.14% on the 1st, 14th, and 30th day after gastroscopy, respectively. The risk factors for the first day were female sex (OR 1.34, 95% CI 1.00-1.79), parental anxiety (OR 2.23, 95% CI 1.75-3.12), and severe anxiety in children (OR 2.83, 95% CI 1.96-4.07). The risk factors on the 14th day were parental anxiety (OR 3.71, 95% CI 2.19-6.29), a parental educational level above high school (OR 1.65, 95% CI 1.00-2.70), and severe anxiety in children (OR 11.87, 95% CI 5.85-24.07). The risk factors on the 30th day were female sex (OR 2.99, 95% CI 1.41-6.34), being an only child (OR 4.42, 95% CI 2.18-8.95), a parental educational level above high school (OR 2.66, 95% CI 1.27 NPOBCs 5.56), and severe anxiety in children (OR 6.84, 95% CI 2.84-16.49). CONCLUSION: In children undergoing painless gastroscopy, the incidence rates of NPOBCs on the 1st, 14th, and 30th day were 14.13%, 4.55%, and 2.14%, respectively. The risk factors for NPOBCs were severe anxiety in children, female sex, parental anxiety, and a parental educational level above high school. In particular, severe preoperative anxiety in children was a persistent risk factor for NPOBCs within 30 days.

Analysis of avian influenza A (H3N8) viruses in poultry and their zoonotic potential, China, September 2021 to May 2022.

BackgroundTwo human cases of avian influenza A (H3N8) virus infection were reported in China in 2022.AimTo characterise H3N8 viruses circulating in China in September 2021-May 2022.MethodsWe sampled poultry and poultry-related environments in 25 Chinese provinces. After isolating H3N8 viruses, whole genome sequences were obtained for molecular and phylogenetic analyses. The specificity of H3N8 viruses towards human or avian receptors was assessed in vitro. Their ability to replicate in chicken and mice, and to transmit between guinea pigs was also investigated.ResultsIn total, 98 H3N8 avian influenza virus isolates were retrieved from 38,639 samples; genetic analysis of 31 representative isolates revealed 17 genotypes. Viruses belonging to 10 of these genotypes had six internal genes originating from influenza A (H9N2) viruses. These reassorted viruses could be found in live poultry markets and comprised the strains responsible for the two human infections. A subset of nine H3N8 viruses (including six reassorted) that replicated efficiently in mice bound to both avian-type and human-type receptors in vitro. Three reassorted viruses were shed by chickens for up to 9 days, replicating efficiently in their upper respiratory tract. Five reassorted viruses tested on guinea pigs were transmissible among these by respiratory droplets.ConclusionAvian H3N8 viruses with H9N2 virus internal genes, causing two human infections, occurred in live poultry markets in China. The low pathogenicity of H3N8 viruses in poultry allows their continuous circulation with potential for reassortment. Careful monitoring of spill-over infections in humans is important to strengthen early-warning systems and maintain influenza pandemic preparedness.