Trajectory on postpartum depression of Chinese women and the risk prediction models: A machine-learning based three-wave follow-up research.

BACKGROUND: Our study delves into postpartum depression (PPD) extending observation up to six months postpartum, addressing the gap in long-term follow-ups and uncover critical intervention points. METHOD: Through a continuous three-wave cohort study involving 3174 of 10,730 invited postpartum women, we utilized machine learning to predict PPD risk, incorporating self-reported surveys and health records from October 2021 to Jan 2023. RESULTS: PPD prevalence slightly decreased from 30.9 % to 29.1 % over six months. The Random Forest model emerged as the most effective, identifying key predictors of PPD at different stages. The top three factors at first month were newborn's birth weight, maternal weight before delivery and before pregnancy. The EPDS scores of last time, newborn's birth weight and maternal weight before pregnancy and before delivery were main predictors for EPDS scores at third and sixth months postpartum. LIMITATION: The study faces limitations such as potential selection bias due to the convenience sampling method and the reliance on self-reported measures, which may introduce reporting bias. Furthermore, the high attrition rate could affect the representativeness of the sample and the generalizability of the findings. CONCLUSION: There is a slight decrease in PPD rates over six months, yet the prevalence remains high. This underscores the need for early and ongoing mental health support for new mothers. Our study highlights the efficacy of machine learning in enhancing PPD risk assessment and tailoring intervention strategies, paving the way for more personalized healthcare approaches in postpartum care.

Solid electrochemiluminescence sensor by immobilization luminol in Zn-Co-ZIF CNFs for sensitive detection of procymidone in vegetables.

A solid-state electrochemiluminescence (ECL) sensor was fabricated by immobilizing luminol, a classical luminescent reagent, on a Zn-Co-ZIF carbon fiber-modified electrode for the rapid and sensitive detection of procymidone (PCM) in vegetable samples. The sensor was created by sequentially modifying the glassy carbon electrode with Zn-Co-ZIF carbon fiber (Zn-Co-ZIF CNFs), Pt@Au NPs, and luminol. Zn-Co-ZIF CNFs, prepared through electrospinning and high-temperature pyrolysis, possessed a large specific surface area and porosity, making it suitable as carrier and electron transfer accelerator in the system. Pt@Au NPs demonstrated excellent catalytic activity, effectively enhancing the generation of active substances. The ECL signal was significantly amplified by the combination of Zn-Co-ZIF CNFs and Pt@Au NPs, which can subsequently be diminished by procymidone. The ECL intensity decreased proportionally with the addition of procymidone, displaying a linear relationship within the concentration range 1.0 × 10-13 to 1.0 × 10-6 mol L-1 (R2 = 0.993). The sensor exhibited a detection limit of 3.3 × 10-14 mol L-1 (S/N = 3) and demonstrated outstanding reproducibility and stability, making it well-suited for the detection of procymidone in vegetable samples.

An ultrasensitive solid-state electrochemiluminescence sensor based on Ni-MOF@Ru(bpy)32+ and Au NPs@TiO2 for determination of permethrin.

The novel TiO2 and Ni-MOF materials were synthesized and utilized for the detection of permethrin (PET). A highly sensitive solid-state electrochemiluminescence (ECL) sensor was developed based on Ni-MOF@Ru(bpy)32+ and Au NPs@TiO2. In this sensing platform, Ru(bpy)32+-Tripropyl Amine (TPrA) was used as a luminescent signal, Ni-MOF acted as a carrier to carry more luminescent reagents Ru(bpy)32+. Au NPs acted as promoters facilitated electron transport and TiO2 could further enhance the luminescence intensity of the system by synergistical interaction with Au NPs. The possible mechanisms of signal amplification were investigated. The ECL intensity decreased significantly with increasing PET concentration, enabling the determination of PET amount through the observation of the change in ECL signal intensity (ΔI). Under optimal experimental conditions, the linear range of PET concentration from 1.0 × 10-11 mol L-1 to 1.0 × 10-6 mol L-1, with a detection limit of 3.3 × 10-12 mol L-1 (3S/N). This method was successfully applied to determine PET in various vegetable samples.

TM-Score predicts immunotherapy efficacy and improves the performance of the machine learning prognostic model in gastric cancer.

Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The application of tumor mutational burden (TMB) in predicting immunotherapy efficacy in GC patients is limited and controversial, emphasizing the importance of optimizing TMB-based patient selection. By combining TMB and major histocompatibility complex (MHC) related hub genes, we established a novel TM-Score. This score showed superior performance for immunotherapeutic selection (AUC = 0.808) compared to TMB, MSI status, and EBV status. Additionally, it predicted the prognosis of GC patients. Subsequently, a machine learning model adjusted by the TM-Score further improved the accuracy of survival prediction (AUC > 0.8). Meanwhile, we found that GC patients with low TM-Score had a higher mutation frequency, higher expression of HLA genes and immune checkpoint genes, and higher infiltration of CD8+ T cells, CD4+ helper T cells, and M1 macrophages. This suggests that TM-Score is significantly associated with tumor immunogenicity and tumor immune environment. Notably, based on the RNA-seq and scRNA-seq, it was found that AKAP5, a key component gene of TM-Score, is involved in anti-tumor immunity by promoting the infiltration of CD4+ T cells, NK cells, and myeloid cells. Additionally, siAKAP5 significantly reduced MHC-II mRNA expression in the GC cell line. In addition, our immunohistochemistry assays confirmed a positive correlation between AKAP5 and human leukocyte antigen (HLA) expression. Furthermore, AKAP5 levels were higher in patients with longer survival and those who responded to immunotherapy in GC, indicating its potential value in predicting prognosis and immunotherapy outcomes. In conclusion, TM-Score, as an optimization of TMB, is a more precise biomarker for predicting the immunotherapy efficacy of the GC population. Additionally, AKAP5 shows promise as a therapeutic target for GC.

Bioinformatics and system biology approaches to determine the connection of SARS-CoV-2 infection and intrahepatic cholangiocarcinoma.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causal agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide, which poses a severe threat to human health. COVID-19 is a systemic ailment affecting various tissues and organs, including the lungs and liver. Intrahepatic cholangiocarcinoma (ICC) is one of the most common liver cancer, and cancer patients are particularly at high risk of SARS-CoV-2 infection. Nonetheless, few studies have investigated the impact of COVID-19 on ICC patients. METHODS: With the methods of systems biology and bioinformatics, this study explored the link between COVID-19 and ICC, and searched for potential therapeutic drugs. RESULTS: This study identified a total of 70 common differentially expressed genes (DEGs) shared by both diseases, shedding light on their shared functionalities. Enrichment analysis pinpointed metabolism and immunity as the primary areas influenced by these common genes. Subsequently, through protein-protein interaction (PPI) network analysis, we identified SCD, ACSL5, ACAT2, HSD17B4, ALDOA, ACSS1, ACADSB, CYP51A1, PSAT1, and HKDC1 as hub genes. Additionally, 44 transcription factors (TFs) and 112 microRNAs (miRNAs) were forecasted to regulate the hub genes. Most importantly, several drug candidates (Periodate-oxidized adenosine, Desipramine, Quercetin, Perfluoroheptanoic acid, Tetrandrine, Pentadecafluorooctanoic acid, Benzo[a]pyrene, SARIN, Dorzolamide, 8-Bromo-cAMP) may prove effective in treating ICC and COVID-19. CONCLUSION: This study is expected to provide valuable references and potential drugs for future research and treatment of COVID-19 and ICC.

AtVQ25 promotes salicylic acid-related leaf senescence by fine-tuning the self-repression of AtWRKY53.

Most mechanistic details of chronologically ordered regulation of leaf senescence are unknown. Regulatory networks centered on AtWRKY53 are crucial for orchestrating and integrating various senescence-related signals. Notably, AtWRKY53 binds to its own promoter and represses transcription of AtWRKY53, but the biological significance and mechanism underlying this self-repression remain unclear. In this study, we identified the VQ motif-containing protein AtVQ25 as a cooperator of AtWRKY53. The expression level of AtVQ25 peaked at mature stage and was specifically repressed after the onset of leaf senescence. AtVQ25-overexpressing plants and atvq25 mutants displayed precocious and delayed leaf senescence, respectively. Importantly, we identified AtWRKY53 as an interacting partner of AtVQ25. We determined that interaction between AtVQ25 and AtWRKY53 prevented AtWRKY53 from binding to W-box elements on the AtWRKY53 promoter and thus counteracted the self-repression of AtWRKY53. In addition, our RNA-sequencing data revealed that the AtVQ25-AtWRKY53 module is related to the salicylic acid (SA) pathway. Precocious leaf senescence and SA-induced leaf senescence in AtVQ25-overexpressing lines were inhibited by an SA pathway mutant, atsid2, and NahG transgenic plants; AtVQ25-overexpressing/atwrky53 plants were also insensitive to SA-induced leaf senescence. Collectively, we demonstrated that AtVQ25 directly attenuates the self-repression of AtWRKY53 during the onset of leaf senescence, which is substantially helpful for understanding the timing of leaf senescence onset modulated by AtWRKY53.

A molecule-imprinted electrochemiluminescence sensor based on CdS@MWCNTs for ultrasensitive detection of fenpropathrin.

A molecularly-imprinted electrochemiluminescence sensor was constructed for the determination of fenpropathrin (FPT) by molecular imprinting technology. In this sensing platform, the introduction of CdS@MWCNTs significantly enhanced the initial ECL signal of the luminol-O2 system. Specifically, MWCNTs was used as a carrier to adsorb more CdS, in which CdS acted as a co-reaction promoter for luminescence. Molecularly imprinted polymer (MIP) containing specific recognition sites of FPT was used as the material for selective recognition. With increasing amount of FPT the ECL signal decreased. Under the optimum conditions, the ECL response was linearly related to the logarithm of FPT concentration. The developed ECL sensor allowed for sensitive determination of FPT and exhibited a wide linear range from 1.0 × 10- 10 mol L- 1 to 1.0 × 10- 6 mol L- 1. The limit of detection was 3.3 × 10- 11 mol L- 1 (S/N = 3). It can be used for the detection of FPT in vegetable samples.

Ultrasensitive solid-state electrochemiluminescence sensor based on lotus root shaped carbon fiber, CdSe QDs and Fe3O4 synergically amplify Ru(bpy)32+ luminophore signal for detection of cyfluthrin.

An efficient and innovative electrochemiluminescence (ECL) sensor was developed for trace detection of cyfluthrin. The sensor utilized materials such as lotus root shaped carbon fiber (Co CNFs), cadmium selenide quantum dots (CdSe QDs), and Fe3O4 to amplify Ru(bpy)32+ signals. Co CNFs, with its large specific surface area and porosity, served the purpose of not only enhancing the stability of the sensor by fixing CdSe QDs and Ru(bpy)32+ on the Co CNFs/GCE, but also facilitating electron transfer. CdSe QDs was involved in the luminescence reaction and collaborated with Ru(bpy)32+ to enhance the sensor's sensitivity, while Fe3O4 promoted electron transfer in the system due to its large surface area. The solid-state ECL sensor achieved satisfactory signal under the synergistic action of these components. The ECL signal of the sensor was quenched by cyfluthrin, and a favorable linear relationship was observed between the sensor and cyfluthrin in the concentration range 1 × 10-12 to 1 × 10-6 M. The detection limit of the sensor was 3.3 × 10-13 M (S/N = 3). The utilization of lotus root shaped carbon fiber, CdSe QDs, and Fe3O4 in the Ru(bpy)32+ system demonstrated a synergistic effect for cyfluthrin detection, presenting a new approach for the rapid determination analysis of pesticide residues in foods.

Enhancement of PRMT6 binding to a novel germline GATA1 mutation associated with congenital anemia.

Mutations in the master hematopoietic transcription factor GATA1 are often associated with functional defects in erythropoiesis and megakaryopoiesis. In this study, we identified a novel GATA1 germline mutation (c.1162delGG, p.Leu387Leufs*62) in a patient with congenital anemia and occasional thrombocytopenia. The C-terminal GATA1, a rarely studied mutational region, undergoes frameshifting translation as a consequence of this double-base deletion mutation. To investigate the specific function and pathogenic mechanism of this mutant, in vitro mutant models of stable re-expression cells were generated. The mutation was subsequently validated to cause diminished transcriptional activity of GATA1 and defective differentiation of erythroid and megakaryocytes. Using proximity labeling and mass spectrometry, we identified selective alterations in the proximal protein networks of the mutant, revealing decreased binding to a set of normal GATA1-interaction proteins, including the essential co-factor FOG1. Notably, our findings further demonstrated enhanced recruitment of the protein arginine methyltransferase PRMT6, which mediates histone modification at H3R2me2a and represses transcription activity. We also found an enhanced binding of this mutant GATA1/PRMT6 complex to the transcriptional regulatory elements of GATA1's target genes. Moreover, treatment of the PRMT6 inhibitor MS023 could partially rescue the inhibited transcriptional and impaired erythroid differentiation caused by the GATA1 mutation. Taken together, our results provide molecular insights into erythropoiesis in which mutation leads to partial loss of GATA1 function and the broader role of PRMT6 and its inhibitor MS023 in congenital anemia, highlighting PRMT6 binding as a negative factor of GATA1 transcriptional activity in aberrant hematopoiesis.

Assessment of small strain modulus in soil using advanced computational models.

Small-strain shear modulus ([Formula: see text]) of soils is a crucial dynamic parameter that significantly impacts seismic site response analysis and foundation design. [Formula: see text] is susceptible to multiple factors, including soil uniformity coefficient ([Formula: see text]), void ratio (e), mean particle size ([Formula: see text]), and confining stress ([Formula: see text]). This study aims to establish a [Formula: see text] database and suggests three advanced computational models for [Formula: see text] prediction. Nine performance indicators, including four new indices, are employed to calculate and analyze the model's performance. The XGBoost model outperforms the other two models, with all three models achieving [Formula: see text] values exceeding 0.9, RMSE values below 30, MAE values below 25, VAF values surpassing 80%, and ARE values below 50%. Compared to the empirical formula-based traditional prediction models, the model proposed in this study exhibits better performance in IOS, IOA, a20-index, and PI metrics values. The model has higher prediction accuracy and better generalization ability.