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Soil respiration (Rs) represents the greatest carbon dioxide flux from terrestrial ecosystems to the atmosphere. However, its environmental drivers are not fully understood, and there are still significant uncertainties in soil respiration model estimates. This study aimed to estimate the spatial distribution pattern and driving mechanism of global soil respiration by constructing a machine learning model method based on ecological big data. First, we constructed ecological big data containing five categories of 27-dimensional environmental factors. We then used four typical machine learning methods to develop the performance of machine learning models under four training strategies and explored the relationship between soil respiration and environmental factors. Finally, we used the RF machine learning algorithm to estimate the global Rs spatial distribution pattern in 2021, driven by multiple dimensions of environmental factors, and derived the annual soil respiration values. The results showed that RF performed better under the four training strategies, with a coefficient of determination R 2  = 0.78216, root mean squared error (RMSE) = 285.8964 gCm −2 y −1 , and mean absolute error (MAE) = 180.4186 gCm −2 y −1 , which was more suitable for the estimation of large-scale soil respiration. In terms of the importance of environmental factors, unlike previous studies, we found that the influence of geographical location was greater than that of MAP. Another new finding was that enhanced vegetation index 2 (EVI2) had a higher contribution to soil respiration estimates than the enhanced vegetation index (EVI) and normalized vegetation index (NDVI). Our results confirm the potential of utilizing ecological big data for spatially large-scale Rs estimations. Ecological big data and machine learning algorithms can be considered to improve the spatial distribution patterns and driver analysis of Rs.

Resistance monitoring in the Asian corn borer, Ostrinia furnacalis, is necessary to accommodate the commercial introduction and stewardship of Bt maize in China. The susceptibility of 56 O. furnacalis field populations, collected between 2015 and 2021 from the corn belt regions of China, to Cry1Ab and Cry1F toxins was determined. Neonate larvae (within 12 h after hatching) were placed on the surface of semi-artificial agar-free diet incorporating a series of concentrations of purified toxins, and mortality was evaluated after 7d. The median lethal concentration (LC50) values of Cry1Ab and Cry1F were 0.05 to 0.37 µg/g (protein/diet) and 0.10 to 1.22 µg/g, respectively. Although interpopulation variation in susceptibility to the toxins was observed, the magnitude of the differences was 5.8-fold and 8.3-fold for Cry1Ab and Cry1F, respectively. These results suggested that the observed susceptibility differences reflect natural geographical variation in response and not variation caused by prior exposure to selection pressures. Therefore, the O. furnacalis populations were apparently still susceptible to Cry1Ab and Cry1F across their range within China. The monitoring data established here will serve as a comparative reference for early warning signs of field-evolved resistance after the cultivation of Bt maize in China.

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, often diagnosed at advanced stages with poor prognosis. CD53, a tetraspanin involved in immune regulation, has an unclear role in LUAD. Five GEO LUAD transcriptomic datasets were integrated and batch-corrected. Differential expression and weighted gene co-expression network analyses identified LUAD-associated genes. Machine learning (Elastic Net) selected core predictive genes. Immune infiltration analysis, single-cell RNA-seq analysis, regulatory and drug-gene network construction, summary-data-based Mendelian randomization (SMR), and colocalization analyses were performed. Public TCGA-LUAD data were further used to evaluate the association between CD53 expression and clinicopathological characteristics. CD53 expression was clinically validated by immunohistochemistry (IHC) and PCR in 30 paired LUAD and adjacent normal tissue samples. Six genes (BTK, CD163, CD53, F13A1, HCLS1, MS4A6A) were consistently downregulated in LUAD, with CD53 as a central immune-related candidate. CD53 expression positively correlated with monocytes and neutrophils, and negatively with naïve B cells, T follicular helper cells, plasma cells, and M0 macrophages. Single-cell RNA-seq analysis showed that CD53 was mainly localized to immune cell populations, particularly T cells, macrophages, and monocytes. SMR prioritized CD53 as a genetically associated candidate gene, whereas colocalization analysis did not support a shared causal variant between CD53 and LUAD. IHC and PCR both confirmed significantly lower CD53 expression in tumor tissues versus adjacent normal tissues, with low expression associated with poor prognosis. Public TCGA-LUAD analysis further showed that the CD53-low group had a higher proportion of deceased patients, and deceased patients exhibited lower CD53 expression than alive patients. Drug-gene network analysis predicted several candidate compounds potentially associated with CD53 and other model genes, providing hypothesis-generating clues for future experimental studies. CD53 is downregulated in bulk LUAD tissues, correlates with immune cell infiltration, and may serve as a candidate immune-microenvironment-associated diagnostic and prognostic biomarker. Its direct tumor-suppressive function remains to be determined in future mechanistic studies. These findings provide a basis for further mechanistic studies and future validation of CD53-related diagnostic, prognostic, and therapeutic hypotheses in LUAD.

The aim of the present study was to assess the expression of sirtuin (Sirt)1 in tendon stem cells (TSCs) and to elucidate its association with osteogenic and adipogenic differentiation of TSCs. Reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and western blot analyses were performed to detect Sirt1 mRNA and protein levels in TSCs, respectively. TSCs were positive for Sirt1 expression, which was elevated by Sirt1 activator SRT1720 in a time- and concentration- dependent manner, and decreased by Sirt1 inhibitor EX527. TSCs were treated with SRT1720 and EX527 for various time periods and resulting changes in osteogenic and adipogenic protein markers were analyzed using alizarin red and oil red O staining. According to RT-qPCR and western blot analyses, the associated factors β-catenin, Runt-related transcription factor 2 (Runx2) and bone morphogenetic protein 2 were elevated following increases of Sirt1 levels, while CCAAT/enhancer binding protein (CEBP)α and peroxisome proliferator-activated receptor (PPAR)γ were decreased. These results suggested that osteogenic differentiation capacity was enhanced, while adipogenic differentiation capacity declined. Further mechanistic study revealed that phosphoinositide-3 kinase (PI3K) and AKT were decreased following activation of Sirt1. In conclusion, the present study suggested that Sirt1 promotes the osteogenic differentiation of TSCs through upregulating β-catenin and Runx2 and inhibits the adipogenic differentiation of TSCs through the PI3K/AKT pathway with downregulation of CEBPα and PPARγ.

The traditional xenograft subcutaneous pancreatic cancer model is notorious for its low incidence of tumor formation, inconsistent results for the chemotherapeutic effects of drug molecules of interest, and a poor predictive capability for the clinical efficacy of novel drugs. These drawbacks are attributed to a variety of factors, including inoculation of heterogeneous tumor cells from patients with different pathological histories, and use of poorly defined Matrigel(®). In this study, we aimed to tissue-engineer a pancreatic cancer model that could readily cultivate a pancreatic tumor derived from highly homogenous CD24(+)CD44(+) pancreatic cancer stem cells delivered by a well defined electrospun scaffold of poly(glycolide-co-trimethylene carbonate) and gelatin. The scaffold supported in vitro tumorigenesis from CD24(+)CD44(+) cancer stem cells for up to 7 days without inducing apoptosis. Moreover, CD24(+)CD44(+) cancer stem cells delivered by the scaffold grew into a native-like mature pancreatic tumor within 8 weeks in vivo and exhibited accelerated tumorigenesis as well as a higher incidence of tumor formation than the traditional model. In the scaffold model, we discovered that oxaliplatin-gemcitabine (OXA-GEM), a chemotherapeutic regimen, induced tumor regression whereas gemcitabine alone only capped tumor growth. The mechanistic study attributed the superior antitumorigenic performance of OXA-GEM to its ability to induce apoptosis of CD24(+)CD44(+) cancer stem cells. Compared with the traditional model, the scaffold model demonstrated a higher incidence of tumor formation and accelerated tumor growth. Use of a tiny population of highly homogenous CD24(+)CD44(+) cancer stem cells delivered by a well defined scaffold greatly reduces the variability associated with the traditional model, which uses a heterogeneous tumor cell population and poorly defined Matrigel. The scaffold model is a robust platform for investigating the antitumorigenesis mechanism of novel chemotherapeutic drugs with a special focus on cancer stem cells.

Background The Mood Disorder Questionnaire (MDQ) is a well-recognized screening tool for bipolar disorder, but its Chinese version needs further validation. This study aims to measure the accuracy of the Chinese version of the MDQ as a screening instrument for bipolar disorder (BPD) in a group of patients with a current major depressive episode. Methods 142 consecutive patients with an initial DSM-IV-TR diagnosis of a major depressive episode were screened for BPD using the Chinese translation of the MDQ and followed up for one year. The final diagnosis, determined by a special committee consisting of three trained senior psychiatrists, was used as a 'gold standard' and ROC was plotted to evaluate the performance of the MDQ. The optimal cut-off was chosen by maximizing the Younden's index. Results Of the 142 patients, 122 (85.9%) finished the one year follow-up. On the basis of a semi-structured clinical interview 48.4% (59/122) received a diagnosis of unipolar depression (UPD), 36.9% (45/122) BPDII and 14.8% (18/122) BPDI. At the end of the one year follow-up,9 moved from UPD to BPD, 2 from BPDII to UPD, 1 from BPDII to BPDI, the overall rate of initial misdiagnosis was 16.4%. MDQ showed a good accuracy for BPD: the optimal cut-off was 4, with a sensitivity of 0.72 and a specificity of 0.73. When BPDII and BPDI were calculated independently, the optimal cut-off for BPDII was 4, with a sensitivity of 0.70 and a specificity of 0.73; while the optimal cut-off for BPDI was 5, with a sensitivity of 0.67 and a specificity of 0.86. Conclusions Our results show that the Chinese version of MDQ is a valid tool for screening BPD in a group of patients with current depressive episode on the Chinese mainland.