BACKGROUND: Since the development of the OMERACT Systemic Lupus Erythematosus (SLE) Core Outcome Set (COS) in 1998, many new SLE domains have been identified and measures developed, creating a need to update the SLE COS. To revisit the 1998 SLE COS and research agenda domains, and generate new candidate domains, we conducted this study of patients with SLE and collaborators.
OBJECTIVE: (1) To evaluate existing candidate SLE domains for inclusion in the SLE COS. (2) To generate additional candidate SLE domains for COS consideration. (3) To engage SLE collaborators, including patients, in developing the updated SLE COS.
METHODS: The OMERACT SLE Working Group\'s steering committee developed a survey to assess the importance of candidate SLE domains and generate additional domains for consideration towards the SLE COS. Patients with SLE followed at the University of Toronto Lupus Clinic (patient group) and members of the OMERACT SLE Working Group (collaborator group) were invited to complete the survey between August 2022 and February 2023.
RESULTS: A total of 175 patients were invited and 100 completed the survey. Of 178 collaborators invited, 145 completed the survey. Patients tended to prioritize life-impact domains while collaborators prioritized clinical domains. Both patients and collaborators recommended additional domains to those included in the 1998 SLE COS and research agenda.
CONCLUSIONS: The domain inclusion and importance results demonstrate that patients and collaborators prioritize different domains, so capturing the perspectives of both groups is essential to ensure a holistic assessment of SLE. The results of the study identify domains that already have a high level of agreement for potential inclusion in the SLE COS, domains that require further explanation, and novel domains that warrant consideration.

Background: The administration of omega-3 polyunsaturated fatty acid supplements is recommended as an adjuvant therapy for adults diagnosed with major depressive disorder. The evaluation of replicated data in combination treatment with omega-3 has been extensively conducted in adults over the past decade. However, the generalizability of these findings to pediatric groups is still uncertain. The objectives of this evaluation were twofold: (1) to evaluate the effectiveness of omega-3 and associated combination therapies in reducing the severity of depressive symptoms, and (2) to include remission rates (i.e., reduction of more than 50% in depression symptoms) as a measure of therapeutic efficacy. Methods: We conducted a literature search on PubMed/EMBASE from inception to October 2023. Data analyses were conducted using Stata (version 17.0). Results: We identified a total of 3168 articles. After eligibility screening of identified studies, nine studies (n = 561 participants) were included in our analysis herein. Pairwise comparisons revealed no significant improvement in depression symptoms for any intervention versus placebo. However, a clustered ranking plot identified omega-3 plus inositol as the most effective treatment for pediatric depression (77.3% efficacy). Omega-3 paired with psychoeducational psychotherapy significantly lowered the remission rate compared to placebo (standardized mean difference = 0.44, 95% confidence interval: 0.00-0.87, p = 0.048), resulting in a 91.5% remission rate, making it the most effective treatment in the study. Conclusions: Taken together, this network meta-analysis presents compelling evidence supporting the antidepressant effects of omega-3 in pediatric groups with depression. Future research should aim to investigate omega-3 as monotherapy for young individuals with depression, as well as investigate the efficacy of omega-3 in comparison to psychosocial interventions for affected individuals.

The gallium-doped hafnium oxide (Ga-HfO2) films with different Ga doping concentrations were prepared by adjusting the HfO2/Ga2O3 atomic layer deposition cycle ratio for high-speed and low-voltage operation in HfO2-based ferroelectric memory. The Ga-HfO2 ferroelectric films reveal a finely modulated coercive field (Ec) from 1.1 (HfO2/Ga2O3 = 32:1) to an exceptionally low 0.6 MV/cm (HfO2/Ga2O3 = 11:1). This modulation arises from the competition between domain nucleation and propagation speed during polarization switching, influenced by the intrinsic domain density and phase dispersion in the film with specific Ga doping concentrations. Higher Ec samples exhibit a nucleation-dominant switching mechanism, while lower Ec samples undergo a transition from a nucleation-dominant to a propagation-dominant reversal mechanism as the electric field increases. This work introduces Ga as a viable dopant for low Ec and offers insights into material design strategies for HfO2-based ferroelectric memory applications.

A switching-type power converter providing an accurate and stable switching output voltage against line/load variations and power supply ripple is mostly complicated in system-on-chip power management integrated circuits (PMICs) within a limited occupation area. Here we fabricated domain wall (DW) nanodevices using an X-cut LiNbO3 thin film on silicon. The domain switching event occurs after a delay time predicted by Merz\'s law under the applied voltage. But the output current is irrespective of the applied voltage and can be adjusted by conducting wall width as well as input resistance in the circuit. The regulating currents appear repetitively across the volatile interfacial domains between the nanodevice and electrode under intermittently applied voltages. A wall-current-limited domain switching model is developed to explain the phenomenon. The multifunctional DW nanodevices with smaller occupation areas can serve as compact low-dropout regulators in PMICs, time-domain delayers in energy-efficient neural network systems, and on-chip electrostatic discharge protection besides nonvolatile memories and selectors.

BACKGROUND: Adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1 (APPL1) plays a crucial role in regulating insulin signaling and glucose metabolism. Mutations in the APPL1 gene have been associated with the development of maturity-onset diabetes of the young type 14 (MODY14). Currently, only two mutations [c.1655T>A (p.Leu552*) and c.281G>A p.(Asp94Asn)] have been identified in association with this disease. Given the limited understanding of MODY14, it is imperative to identify additional cases and carry out comprehensive research on MODY14 and APPL1 mutations.
OBJECTIVE: To assess the pathogenicity of APPL1 gene mutations in diabetic patients and to characterize the functional role of the APPL1 domain.
METHODS: Patients exhibiting clinical signs and a medical history suggestive of MODY were screened for the study. Whole exome sequencing was performed on the patients as well as their family members. The pathogenicity of the identified APPL1 variants was predicted on the basis of bioinformatics analysis. In addition, the pathogenicity of the novel APPL1 variant was preliminarily evaluated through in vitro functional experiments. Finally, the impact of these variants on APPL1 protein expression and the insulin pathway were assessed, and the potential mechanism underlying the interaction between the APPL1 protein and the insulin receptor was further explored.
RESULTS: A total of five novel mutations were identified, including four missense mutations (Asp632Tyr, Arg633His, Arg532Gln, and Ile642Met) and one intronic mutation (1153-16A>T). Pathogenicity prediction analysis revealed that the Arg532Gln was pathogenic across all predictions. The Asp632Tyr and Arg633His variants also had pathogenicity based on MutationTaster. In addition, multiple alignment of amino acid sequences showed that the Arg532Gln, Asp632Tyr, and Arg633His variants were conserved across different species. Moreover, in in vitro functional experiments, both the c.1894G>T (at Asp632Tyr) and c.1595G>A (at Arg532Gln) mutations were found to downregulate the expression of APPL1 on both protein and mRNA levels, indicating their pathogenic nature. Therefore, based on the patient\'s clinical and family history, combined with the results from bioinformatics analysis and functional experiment, the c.1894G>T (at Asp632Tyr) and c.1595G>A (at Arg532Gln) mutations were classified as pathogenic mutations. Importantly, all these mutations were located within the phosphotyrosine-binding domain of APPL1, which plays a critical role in the insulin sensitization effect.
CONCLUSIONS: This study provided new insights into the pathogenicity of APPL1 gene mutations in diabetes and revealed a potential target for the diagnosis and treatment of the disease.

Receptor-like kinases (RLKs) are the most numerous signal transduction components in plants and play important roles in determining how different plants adapt to their ecological environments. Research on RLKs has focused mainly on a small number of typical RLK members in a few model plants. There is an urgent need to study the composition, distribution, and evolution of RLKs at the holistic level to increase our understanding of how RLKs assist in the ecological adaptations of different plant species. In this study, we collected the genome assemblies of 528 plant species and constructed an RLK dataset. Using this dataset, we identified and characterized 524 948 RLK family members. Each member underwent systematic topological classification and was assigned a gene ID based on a unified nomenclature system. Furthermore, we identified two novel extracellular domains in some RLKs, designated Xiao and Xiang. Evolutionary analysis of the RLK family revealed that the RLCK-XVII and RLCK-XII-2 classes were present exclusively in dicots, suggesting that diversification of RLKs between monocots and dicots may have led to differences in downstream cytoplasmic responses. We also used an interaction proteome to help empower data mining for inference of new RLK functions from a global perspective, with the ultimate goal of understanding how RLKs shape the adaptation of different plants to the environments/ecosystems. The assembled RLK dataset, together with annotations and analytical tools, forms an integrated foundation of multiomics data that is publicly accessible via the metaRLK web portal (http://metaRLK.biocloud.top).

Both Bactrocera minax and Bactrocera dorsalis are phytophagous insects, and their larvae are latent feeders, which cause great damage and economic losses to agriculture production and trade. This study aimed to provide a scientific reference for researching and developing the feasible countermeasures against these two pests. Based on the distribution data of B. minax and B. dorsalis in China, obtained from the Chinese herbaria, investigation and literature. Four niche models (Garp, Bioclim, Domain, and Maxent) were used to analyze the key environmental factors affecting the distribution of both pests and to build prediction models of the potential distribution in Sichuan Basin. Combined with two statistical standards, area under the receiver operating characteristic curve (AUC) and Kappa, the validity of prediction models were analyzed and compared. The results show that: the average AUC values of the four models are all above 0.90, and the average Kappa values are all above 0.75, indicating that the four models are suitable for predicting the potential distribution area of B. minax and B. dorsalis. The annual range of temperature, the mean temperature in the driest quarter, the mean temperature in the warmest quarter, the annual precipitation, and the precipitation in driest month are the key environmental factors affecting the distribution of B. minax, while the mean diurnal temperature range, the mean temperature in the driest quarter, the seasonal temperature variations and the precipitation in driest month affect the potential distribution of B. dorsalis. The suitable areas for B. minax are mainly concentrated in the eastern of Sichuan Basin, while the suitable areas for B. dorsalis are concentrated in the southeastern. Except for the Bioclim model, the highly-suitable area for both pests predicted by the other three models are all greater than 15.94 × 104 km2 and the moderately-suitable areas are greater than 13.57 × 104 km2. In conclusion, the suitable areas for both pests in Sichuan Basin are quite wide. Therefore, the relevant authorities should be given strengthened monitoring of both pests, especially in areas with high incursion rates.

BACKGROUND: Information on the association between physical activity (PA) and the risk of chronic kidney disease (CKD) is limited. We aimed to explore the associations of total, domain-specific, and intensity-specific PA with CKD and its subtypes in China.
METHODS: The study included 475,376 adults from the China Kadoorie Biobank aged 30-79 years during 2004-2008 at baseline. An interviewer-administered questionnaire was used to collect the information about PA, which was quantified as metabolic equivalent of task hours per day (MET-h/day) and categorized into 4 groups based on quartiles. Cox regression was used to analyze the association between PA and CKD risk.
RESULTS: During a median follow-up of 12.1 years, 5415 incident CKD cases were documented, including 1159 incident diabetic kidney disease (DKD) cases and 362 incident hypertensive nephropathy (HTN) cases. Total PA was inversely associated with CKD risk, with an adjusted hazard ratio (HR, 95% confidence interval (95%CI)) of 0.83 (0.75-0.92) for incident CKD in the highest quartile of total PA as compared with participants in the lowest quartile. Similar results were observed for risk of DKD and HTN, and the corresponding HRs (95%CIs) were 0.75 (0.58-0.97) for DKD risk and 0.56 (0.37-0.85) for HTN risk. Increased nonoccupational PA, low-intensity PA, and moderate-to-vigorous-intensity PA were significantly associated with a decreased risk of CKD, with HRs (95%CIs) of 0.80 (0.73-0.88), 0.85 (0.77-0.94), and 0.85 (0.76-0.95) in the highest quartile, respectively.
CONCLUSIONS: PA, including nonoccupational PA, low-intensity PA, and moderate-to-vigorous-intensity PA, was inversely associated with the risk of CKD, including DKD, HTN, and other CKD, and such associations were dose dependent.

UNASSIGNED: Pectins are a class of acidic polysaccharides with complex structures. Different pectin molecules are composed of different domains, which have an important impact on their biological activity.
UNASSIGNED: This study aimed to determine the structural features and the antioxidant activities of the pectic polysaccharides isolated from Veronica peregrina L.
UNASSIGNED: The polysaccharide was isolated from Veronica peregrina L by water extraction and fractionated by ion exchange chromatography and gel permeation chromatography. The structure features of the pectic polysaccharides were determined by Fourier transforminfrared spectroscopy (FT-IR) and Nuclear magnetic resonance (NMR). The antioxidant activities was evaluated by the DPPH, OH and ABTS radical scavenging ability.
UNASSIGNED: WVPP-A2b and WVPP-A3b, with molecular weights of 48.7 × 104 and 77.6 × 104  kDa, respectively, contained homogalacturonan (HG), rhamnogalacturonan I (RG-I), and rhamnogalacturonan II (RG-II) domains with a mass ratio of 2.08:2.64:1.00 and 3.87:4.65:1:00, respectively. The RG-I domain contained an arabinogalactan II backbone and arabinans consisting of t-Araf, (1→5)-α-Araf, and (1→3,5)-α-Araf. WVPP-A3b also contained short chains consisting of the [t-Araf-(1→5)-α-Araf-(1→] structural unit. WVPP-A3b showed stronger ability to scavenge DPPH, hydroxyl, and ABTS radicals, which was potentially associated with its high content of galacturonic acid and presence of the HG domain.
UNASSIGNED: The results provide information for enhancing knowledge of the structureactivity relationship of pectic polysaccharides from V. peregrina and their potential application in the healthcare food field.

Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.