Redox-Responsive Nanopesticides Based on Natural Polymers for Environmentally Safe Delivery of Pesticides with Enhanced Foliar Dispersion and Washout Resistance.

Based on the modified cross-linking of the degradable natural polymers chitosan oligosaccharides (COS) and gelatin (GEL) via introduction of a functional bridge 3,3'-dithiodipropionic acid, this study constructed an environmentally responsive dinotefuran (DNF) delivery system (DNF@COS-SS-GEL). The introduction of the disulfide bond (-S-S-) endowed DNF@COS-SS-GEL with redox-responsive properties, allowing for the rapid release of pesticides when stimulated by glutathione (GSH) in the simulated insect. Compared with commercial DNF suspension concentrate (DNF-SC), DNF@COS-SS-GEL showed superior wet spreading and retention performance on cabbage leaves with a reduced contact angle (57°) at 180 s and 4-fold increased retention capacity after rainfall washout. Nanoencapsulation effectively improved the UV-photostability with only a 31.4% decomposition rate of DNF@COS-SS-GEL at 96 h. The small scale and large specific surface area resulted in excellent uptake and transportation properties in plants as well as higher bioactivity against Plutella xylostella larvae. This study will help promote sustainable agricultural development by reducing environmental pollution through improved pesticide utilization.

Unveiling the hidden: a deep learning approach to unraveling subzone-specific changes in peripapillary atrophy in type 2 diabetes.

Purpose: This study aimed to evaluate the optical coherence tomography angiography (OCTA) changes in subzones of peripapillary atrophy (PPA) among type 2 diabetic patients (T2DM) with or without diabetic retinopathy (DR) using well-designed deep learning models. Methods: A multi-task joint deep-learning model was trained and validated on 2,820 images to automate the determination and quantification of the microstructure and corresponding microcirculation of beta zone and gamma zone PPA. This model was then applied in the cross-sectional study encompassing 44 eyes affected by non-proliferative diabetic retinopathy (NPDR) and 46 eyes without DR (NDR). OCTA was utilized to image the peripapillary area in four layers: superficial capillary plexus (SCP), deep capillary plexus (DCP), choroidal capillary (CC) and middle-to-large choroidal vessel (MLCV). Results: The patients in both groups were matched for age, sex, BMI, and axial length. The width and area of the gamma zone were significantly smaller in NPDR group compared to the NDR group. Multiple linear regression analysis revealed a negative association between the diagnosis of DR and the width and area of the gamma zone. The gamma zone exhibited higher SCP, DCP and MLCV density than the beta zone, while the beta zone showed higher CC density than the gamma zone. In comparison to the NDR group, the MLCV density of gamma zone was significantly lower in NPDR group, and this density was positively correlated with the width and area of the gamma zone. Discussion: DR-induced peripapillary vascular changes primarily occur in gamma zone PPA. After eliminating the influence of axial length, our study demonstrated a negative correlation between DR and the gamma zone PPA. Longitudinal studies are required to further elucidate the role of the gamma zone in the development and progression of DR.

Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery.

Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer's acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.

Sporolactobacillus-a new functional genus with potential applications.

Sporolactobacillus is a genus of lactic acid bacteria, which can be widely found in soil. According to NCBI, only 20 strains of the genus Sporolactobacillus have been identified through phenotypic and genotypic analysis, indicating their relatively low numbers compared to other lactic acid bacteria. Currently, there is a growing interest in isolating and studying Sporolactobacillus, particularly focusing on its physiological characteristics and conducting in vitro experiments. This paper provides a review of the sources and physiological characteristics of Sporolactobacillus, along with genotype analysis, carbohydrate metabolism traits, and potential antibacterial properties. It also delves into basic physiological characteristics, lactic acid production, and applications, offering insights for the future utilization of Sporolactobacillus and laying a foundation for exploring its potential applications.

Predicting the oil content of individual corn kernels combining NIR-HSI and multi-stage parameter optimization techniques.

Predicting the oil content of individual corn kernels using hyperspectral imaging and ML offers the advantages of being rapid and non-destructive. However, traditional methods rely on expert experience for setting parameters. In response to these limitations, this study has designed an innovative multi-stage grid search technique, tailored to the characteristics of spectral data. Initially, the study automatically screening the best model from up to 504 algorithm combinations. Subsequently, multi-stage grid search is utilized for improving precision. We collected 270 kernel samples from different parts of the ear from 15 high oil and regular corn materials, with oil contents ranging from 1.4% to 13.1%. Experimental results show that the combinations SG + NONE+KS + PLSR(R2: 0.8570) and MA + LAR+Random+MLR(R2: 0.8523) performed optimally. After parameter optimization, their R2 values increased to 0.9045 and 0.8730, respectively. Additionally, the ACNNR model achieved an R2 of 0.8878 and an RMSE of 0.2243. The improved algorithm significantly outperforms traditional methods and ACNNR model in prediction accuracy and adaptability, offering an effective method for field applications.

LincRNA-p21/AIF-1/CMPK2/NLRP3 pathway promoted inflammation, autophagy and apoptosis of human tubular epithelial cell induced by urate via exosomes.

Urate nephropathy, a common complication of hyperuricemia, has garnered increasing attention worldwide. However, the exact pathogenesis of this condition remains unclear. Currently, inflammation is widely accepted as the key factor in urate nephropathy. Therefore, the aim of this study was to elucidate the interaction of lincRNA-p21/AIF-1/CMPK2/NLRP3 via exosomes in urate nephropathy. This study evaluated the effect of lincRNA-p21/AIF-1/CMPK2/NLRP3 using clinical data collected from patients with urate nephropathy and human renal tubular epithelial cells (HK2) cultured with different concentrations of urate. In clinical research section, the level of lincRNA-p21/AIF-1 in exosomes of urine in patients with hyperuricemia or urate nephropathy was found to be increased, particularly in patients with urate nephropathy. In vitro study section, the level of exosomes, inflammation, autophagy, and apoptosis was increased in HK2 cells induced by urate. Additionally, the expression of lincRNA-p21, AIF-1, CMPK2, and NLRP3 was upregulated in exosomes and HK2 cells. Furthermore, manipulating the activity of lincRNA-p21, AIF-1, CMPK2, and NLRP3 through overexpression or interference vectors regulated the level of inflammation, autophagy, and apoptosis in HK2 cells. In conclusion, the pathway of lincRNA-p21/AIF-1/CMPK2/NLRP3 contributed to inflammation, autophagy, and apoptosis of human renal tubular epithelial cell induced by urate via exosomes. Additionally, the specific exosomes in urine might serve as novel biomarkers for urate nephropathy.

Enhanced antifouling and antibacterial performances of novel UV-curable polysiloxane/microcapsules/Ag composite coatings for marine applications.

Marine biological fouling is a widespread phenomenon encountered by various oceanic ships and naval vessels, resulting in enormous economic losses. Herein, novel 4,5-dichloro-2-octyl-isothiazolone@sodium alginate/chitosan microcapsules (DCOIT@ALG/CS) were prepared through composite gel method using DCOIT as core materials, ALG and CS as shells, and CaCl2 as the cross-linking agent. The formed microcapsules (MCs) with Ag nanoparticles (AgNPs) were then filled in UV-curable polysiloxane (UV-PDMS), followed by UV irradiation to yield UV-PDMS/microcapsules/AgNPs (UV-PDMS/MCs/Ag) composite coatings. The constructed micro-nano dual-scale surface using the MCs and AgNPs improved the antifouling and antibacterial properties of UV-PDMS/MCs/Ag coatings. The as-obtained UV-PDMS/MCs/Ag coatings exhibited a static contact angle of about 160°, shear strength of 2.24 MPa, tensile strength of 3.32 MPa and elongation at break of 212%. The synergistic bacteriostatic effects of DCOIT and AgNPs in UV-PDMS/MCs/Ag coatings resulted in a bactericidal rate of 200 µg ml-1 towards Escherichia coli and Staphylococcus aureus with saturation at 100% within 10 min. In sum, the proposed composite coatings look promising for future marine transportation, pipeline networks and undersea facilities.

Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma.

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation.

Immunogen characterization reveals an intrinsic hindrance in eliciting neutralizing antibodies against JN.1 variant.

The immune evasion of emerging SARS-CoV-2 variants significantly undermines current vaccination efforts, calling for an updated vaccine composition. To identify optimal booster candidates against circulating JN.1, a panel of variant spikes were characterized. The omicron spikes exhibited reduced plasma membrane expression, accompanied by lower cell-cell fusion but increased viral entry. Regimens with DNA prime-DNA boost or DNA prime-adenoviral vectored vaccine boost by intramuscular immunization elicited neutralizing antibody (NAbs) and T cell responses against all variants except BA.2.86 and JN.1. Intranasal immunization induced high IgA and NAb titers in bronchoalveolar lavage against all variants except BA.2.86 and JN.1. T cell responses were generally comparable for all immunogens tested. JN.1 completely escaped NAbs in one immunized cohort, and breakthrough infections marginally boosted antibody titers. Overall, this study indicates intrinsic difficulty in eliciting NAbs against the JN.1 strain, whereas vaccines based on XBB and EG.5.1 are relatively superior in generating cross-reactive NAbs.

3D Morphological Feature Quantification and Analysis of Corn Leaves.

Marked variations in the 3-dimensional (3D) shape of corn leaves can be discerned as a function of various influences, including genetics, environmental factors, and the management of cultivation processes. However, the causes of these variations remain unclear, primarily due to the absence of quantitative methods to describe the 3D spatial morphology of leaves. To address this issue, this study acquired 3D digitized data of ear-position leaves from 478 corn inbred lines during the grain-filling stage. We propose quantitative calculation methods for 13 3D leaf shape features, such as the leaf length, 3D leaf area, leaf inclination angle, blade-included angle, blade self-twisting, blade planarity, and margin amplitude. Correlation analysis, cluster analysis, and heritability analysis were conducted among the 13 leaf traits. Leaf morphology differences among subpopulations of the inbred lines were also analyzed. The results revealed that the 3D leaf traits are capable of revealing the morphological differences among different leaf surfaces, and the genetic analysis revealed that 84.62% of the 3D phenotypic traits of ear-position leaves had a heritability greater than 0.3. However, the majority of 3D leaf shape traits were strongly affected by environmental conditions. Overall, this study quantitatively investigated 3D leaf shape in corn, providing a reliable basis for further research on the genetic regulation of corn leaf morphology and advancing the understanding of the complex interplay among crop genetics, phenotypes, and the environment.

Phenotype identification and genome-wide association study of ear-internode vascular bundles in maize (Zea mays).

The vascular bundle in the ear-internode of maize is a key conduit for transporting photosynthetic materials between "source" and "sink", making it critically important to examine its micro-phenotypes and genetic architecture to identify advantageous characteristics and cultivate high-yielding and high-quality varieties. Unfortunately, the limited observation methods and scope of study precludes any comprehensive and systematic investigations into the microscopic phenotypes and genetic mechanisms of vascular bundle in maize ear-internode. In this study, 47 phenotypic traits were extracted in 495 maize inbred lines using micro computed tomography (Micro-CT) scanning technology and a deep learning-based phenotype acquisition method for stem vascular bundle, which included stem slice-related, epidermis zone-related, periphery zone-related, inner zone-related and vascular bundles-related traits. Phenotypic analysis indicated that there was extensive phenotypic variation of vascular bundle traits in ear-internode, especially that in the inner zone. Of these, 30 phenotypic traits with heritability greater than 0.70 were conducted for GWAS, and a total of 4,225 significant SNPs and 416 candidate genes with detailed functional annotations were identified. Furthermore, 20 genes were highly expressed in stem-related tissues, especially in maize internodes. Functional analysis of candidate genes indicated that the pathways obtained for candidate genes of different trait groups were distinct, mainly involved in vitamin synthesis and metabolism, transport of substances, carbohydrate derivative catabolic process, protein transport and localization, and anatomical structure development. The results of this study will help to further understand the phenotypic traits of stem vascular bundles and provide a reference for revealing the genetic mechanism of maize ear-internode vascular bundles.

Interfacial modulation of nicotinamide additive enables 9700 h Zn metal batteries.

Aqueous zinc-ion batteries (AZIBs) have recently been paid great attention due to their robust safety features, high theoretical capacity, and eco-friendliness, yet their practical application is hindered by the serious dendrite formation and side reactions of Zn metal anode during cycling. Herein, a low-cost small molecule, nicotinamide (NIC), is proposed as an electrolyte additive to effectively regulate the Zn interface, achieving a highly reversible and stable zinc anode without dendrites. NIC molecules not only modify the Zn2+ solvation structure but also preferentially adsorb on the Zn surface than solvated H2O to protect the Zn anode and provide numerous nucleation sites for Zn2+ to homogenize Zn deposition. Consequently, the addition of 1 wt% NIC enables Zn||Zn symmetric cells an ultra-long lifespan of over 9700 h at 1 mA cm-2, which expands nearly 808 times compared to that without NIC. The advantages of NIC additives are further demonstrated in NaVO||Zn full cells, which exhibit exceptional capacity retention of 90.3 % after 1000 cycles with a high Coulombic efficiency of 99.9 % at 1 A/g, while the cell operates for only 42 cycles without NIC additive. This strategy presents a promising approach to solving the anode problem, fostering advancements in practical AZIBs.

Exploring the role of the immune microenvironment in hepatocellular carcinoma: Implications for immunotherapy and drug resistance.

Hepatocellular carcinoma (HCC), the most common type of liver tumor, is a leading cause of cancer-related deaths, and the incidence of liver cancer is still increasing worldwide. Curative hepatectomy or liver transplantation is only indicated for a small population of patients with early-stage HCC. However, most patients with HCC are not candidates for radical resection due to disease progression, leading to the choice of the conventional tyrosine kinase inhibitor drug sorafenib as first-line treatment. In the past few years, immunotherapy, mainly immune checkpoint inhibitors (ICIs), has revolutionized the clinical strategy for HCC. Combination therapy with ICIs has proven more effective than sorafenib, and clinical trials have been conducted to apply these therapies to patients. Despite significant progress in immunotherapy, the molecular mechanisms behind it remain unclear, and immune resistance is often challenging to overcome. Several studies have pointed out that the complex intercellular communication network in the immune microenvironment of HCC regulates tumor escape and drug resistance to immune response. This underscores the urgent need to analyze the immune microenvironment of HCC. This review describes the immunosuppressive cell populations in the immune microenvironment of HCC, as well as the related clinical trials, aiming to provide insights for the next generation of precision immunotherapy.

A digital in situ bone augmentation workflow for reconstructing the alveolar ridge outline using a modified shell technique.

The traditional shell technique is a practical method for augmenting horizontal and vertical alveolar bone defects. However, it has drawbacks, including increased morbidity in the donor site and imprecise harvesting of bone grafts. Instead of using a second surgical site, root areas at the defect site could be the in situ donor site. A digitally designed bone harvest guide was used for an in situ bone augmentation workflow, and the modified shell technique was planned and executed in the root area. This technique offered a controllable procedure which might enhance bone augmentation.

The Immunomodulatory Function of Assembled Composite Nanopolypeptide Containing Bursal-Derived BP7 (CNPB7) in Promoting the Mucosal Immune Response within Poultry Immunization.

Mucosal immunity is the main defense line against respiratory disease pathogens. Newcastle disease and avian infectious bronchitis are common respiratory diseases in poultry. However, the mucosal immune response is not sufficiently activated and thus fails to achieve the ideal immune protection. Therefore, it is important to develop a suitable mucosal immune adjuvant to enhance the immune response of live vaccines. Here, the bursal-derived peptide BP7, ß-glucan, and hyaluronic acid were selected as the adjuvant to be assembled into the composite nanopolypeptide adjuvant (CNPB7) with ultrasonic dispersion technology. The results showed that after optimizing assembly conditions, the optimal average particle size of nanoparticle CNPB7 was 514.9 nm and PDI was 0.298. To evaluate the non-specific immune responses of nanoparticle CNPB7, the chickens were immunized only with nanoparticle CNPB7. It was confirmed that nanoparticle CNPB7 enhanced the expression of CD3, CD4, CD80, and CD86 factors in the spleen lymphocyte from the chicken immunized with nanoparticle CNPB7. To investigate the mucosal immune response of nanoparticle CNPB7, the chickens were orally immunized with Newcastle disease virus (NDV)-infectious bronchitis virus (IBV) dual vaccines and CNPB7. The results proved that the levels of immunoglobulin SIgA, IL-4, IFN-γ, and IL-13 in the mucus samples from the respiratory and digestive tract in chicken immunized with nanoparticle CNPB7 and vaccines were significantly increased, compared to that of vaccine control. Finally, it was observed that nanoparticle CNPB7 promoted specific increased antibody productions against NDV and IBV in the immunized chicken. These results proved that the assembled nanoparticle CNPB7 could enhance the vaccination efficacy in chicken, which provided the experimental basis for the development of new adjuvants, and offered technical support for preventing virus transmission of avian diseases.

A review of bacteriophage and their application in domestic animals in a post-antibiotic era.

Bacteriophages (phages for short) are the most abundant biological entities on Earth and are natural enemies of bacteria. Genomics and molecular biology have identified subtle and complex relationships among phages, bacteria and their animal hosts. This review covers composition, diversity and factors affecting gut phage, their lifecycle in the body, and interactions with bacteria and hosts. In addition, research regarding phage in poultry, aquaculture and livestock are summarized, and application of phages in antibiotic substitution, phage therapy and food safety are reviewed.

Rapid assessment of heavy metal accumulation capability of Sedum alfredii using hyperspectral imaging and deep learning.

Hyperaccumulators are the material basis and key to the phytoremediation of heavy metal contaminated soils. Conventional methods for screening hyperaccumulators are highly dependent on the time- and labor-consuming sampling and chemical analysis. In this study, a novel spectral approach assisted with multi-task deep learning was proposed to streamline accumulating ecotype screening, heavy metal stress discrimination, and heavy metals quantification in plants. The significant Cd/Zn co-hyperaccumulator Sedum alfredii and its non-accumulating ecotype were stressed by Cd, Zn, and Pb. Spectral images of leaves were rapidly acquired by hyperspectral imaging. The self-designed deep learning architecture was composed of a shallow network (ENet) for accumulating ecotype identification, and a multi-task network (HMNet) for heavy metal stress type and accumulation prediction simultaneously. To further assess the robustness of the networks, they were compared with conventional machine learning models (i.e., partial least squares (PLS) and support vector machine (SVM)) on a series of evaluation metrics of classification, multi-label classification, and regression. S. alfredii with heavy metals accumulation capability was identified by ENet with 100 % accuracy. HMNet reduced overfitting and outperformed machine learning models with the average exact match ratio (EMR) of heavy metal stress discrimination increased by 7.46 %, and residual prediction deviations (RPD) of heavy metal concentrations prediction increased by 53.59 %. The method succeeded in rapidly and accurately discriminating heavy metal stress with EMRs over 91 % and accuracies over 96 %, and in predicting heavy metals accumulation with an average RPD of 3.29 for Zn, 2.57 for Cd, and 2.53 for Pb, indicating the satisfactory practicability and potential for sensing heavy metals accumulation. This study provides a relatively novel spectral method to facilitate hyperaccumulator screening and heavy metals accumulation prediction in the phytoremediation process.

Analysis of the multiple drivers of vegetation cover evolution in the Taihangshan-Yanshan region.

The Taihangshan-Yanshan region (TYR) is an important ecological barrier area for Beijing-Tianjin-Hebei, and the effectiveness of its ecological restoration and protection is of great significance to the ecological security pattern of North China. Based on the FVC data from 2000 to 2021, residual analysis, parametric optimal geodetector technique (OPGD) and multi-scale geographically weighted regression analysis (MGWR) were used to clarify the the multivariate driving mechanism of the evolution of FVC in the TYR. Results show that: (1) FVC changes in the TYR show a slowly fluctuating upward trend, with an average growth rate of 0.02/10a, and a spatial pattern of "high in the northwest and low in the southeast"; more than half of the FVC increased during the 22-year period. (2) The results of residual analysis showed that the effects of temperature and precipitation on FVC were very limited, and a considerable proportion (80.80% and 76.78%) of the improved and degraded areas were influenced by other factors. (3) The results of OPGD showed that the main influencing factors of the spatial differentiation of FVC included evapotranspiration, surface temperature, land use type, nighttime light intensity, soil type, and vegetation type (q > 0.2); The explanatory rates of the two-factor interactions were greater than those of the single factor, which showed either nonlinear enhancement or bifactorial enhancement, among which, the interaction of evapotranspiration with mean air and surface temperature has the strongest effect on the spatial and temporal evolution of FVC (q = 0.75). Surface temperature between 4.98 and 10.4 °C, evapotranspiration between 638 and 762 mm/a, and nighttime light between 1.96 and 7.78 lm/m2 favoured an increase in vegetation cover, and vegetation developed on lysimetric soils was more inclined to be of high cover. (4) The correlation between each variable and FVC showed different performance, GDP, elevation, slope and FVC showed significant positive correlation in most regions, while population size, urban population proportion, GDP proportion of primary and secondary industries, and nighttime light intensity all showed negative correlation with FVC to different degrees. The results can provide data for formulating regional environmental protection and restoration policies.

Programmable Chemical Reactions Enable Ultrastrong Soft Pneumatic Actuation.

Soft pneumatic actuation is widely used in wearable devices, soft robots, artificial muscles, and surgery machines. However, generating high-pressure gases in a soft, controllable, and portable way remains a substantial challenge. Here, a class of programmable chemical reactions that can be used to controllably generate gases with a maximum pressure output of nearly 6 MPa is reported. It is proposed to realize the programmability of the chemical reaction process using thermoelectric material with programmable electric current and employing preprogrammed reversible chemical reactants. The programmable chemical reactions as soft pneumatic actuation can be operated independently as miniature gas sources (∼20-100 g) or combined with arbitrary physical structures to make self-contained machines, capable of generating unprecedented pressures of nearly 6 MPa or forces of about 18 kN in a controllable, portable, and silent manner. Striking demonstrations of breaking a brick, a marble, and concrete blocks, raising a sightseeing car, and successful applications in artificial muscles and soft assistive wearables illustrate tremendous application prospects of soft pneumatic actuation via programmable chemical reactions. The study establishes a new paradigm toward ultrastrong soft pneumatic actuation.

Life changes and symptoms of depression and anxiety among Chinese children and adolescents before, during, and after the COVID-19 pandemic lockdown: a combination of cross-sectional, longitudinal, and clustering studies.

The measures to prevent COVID-19 pandemic had caused significant life changes, which may have caused stress on the mental health of children and adolescents. We aimed to evaluate the short- and long-term effects of life changes on children's mental health in a large Chinese cohort. Survey-based life changes during COVID-19 lockdown were measured among 7,829 Chinese students at Grade 1-9, including social contacts, lifestyles and family financial status. Clustering analysis was applied to identify potential patterns of these changes. Depressive and anxiety symptoms were measured using the Center for Epidemiologic Studies Depression Scale and Screen for Child Anxiety Related Emotional Disorders. Logistic regression models were used to investigate the associations between these changes, their patterns and the presence of depression/anxiety symptoms using both cross-sectional and longitudinal designs. We found that the prevalence of depression and anxiety symptoms decreased during pandemic (34.6-32.6%). However, during and shortly after lockdown, students who reported negative impacts on their study, social and outside activities, and diet had increased risks of depressive/anxiety symptoms. Decreased electronic time and sugar-sweetened consumption, as well as family income decline and unemployment, were also associated with higher risks of these symptoms. Additionally, students with changed sleep time had increased depressive symptoms. These associations attenuated or disappeared one year later. Similar patterns were observed in clustering analysis, while only the group with severe impact on family financial status showed a sustained increase in depression symptoms. In summary, restrictive measures that changed children and adolescents' daily life during COVID-19 lockdown showed negative effects on their mental health, with some commonalities and distinctions patterns in the manifestation of depression and anxiety symptoms.