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Sepsis-related acute lung injury (ALI) remains a major cause of mortality in critically ill patients and lacks specific therapy. Mitochondrial dysfunction is involved in the progression of septic lung injury. Mitochondrial dynamics, mitophagy, and biogenesis converge to constitute the assiduous quality control of mitochondria (MQC). Heme oxygenase-1 (HO-1) protects against sepsis-induced ALI through the modulation of mitochondrial dynamics. However, the causal relationship between HO-1 and the general processes of MQC, and their associated cellular pathways in sepsis-related ALI remain ill-defined. Herein, lipopolysaccharide (LPS)-induced ALI in Sprague-Dawley rats together with LPS-induced oxidative injury in RAW264.7 macrophages were used to investigate whether the PI3K/Akt pathway-mediated induction of HO-1 preserves MQC and alleviates septic lung injury. After pretreatment with hemin, a potent inducer of HO-1, LPS-induced cell apoptosis, enhanced mitochondrial fragmentation, and mitochondrial membrane potential damage were significantly reduced in macrophages. In rats, these effects were accompanied by a higher survival rate, less damage to lung tissue, a 28.5% elevation in lung mitochondria MnSOD activity, and a 39.2% increase in respiratory control ratios. Concomitantly, HO-1 induction preserved the dynamic process of mitochondrial fusion/fission (Mfn2, OPA1, Drp1), promoted mitochondrial biogenesis (NRF1, PGC1α, Tfam), and facilitated the key mediators of mitochondrial mitophagy (Parkin, PINK1) at mRNA and protein levels. Notably, LY294002, a PI3K inhibitor, or knockdown of PI3K by small interfering RNA significantly suppressed Akt phosphorylation, attenuated HO-1 induction, and further reversed these beneficial effects evoked by hemin pretreatment in RAW264.7 cells or rats received LPS, indicating a direct involvement of PI3K/Akt pathway. Taken together, our results indicated that HO-1 activation, through PI3K/Akt pathway, plays a critical role in protecting lung from oxidative injury in the setting of sepsis by regulating MQC. HO-1 may therefore be a therapeutic target for the prevention sepsis-related lung injury.

Cuproptosis, a novel form of copper-mediated regulated cell death, participates in tumor progression. However, the role of cuproptosis-related genes (CRGs) in colorectal cancer (CRC) remains unclear. We aimed to investigate the cuproptosis subtypes and build a predictive model to improve the prognosis of patients with CRC. Gene expression data were downloaded from the TCGA database to identify distinct molecular subtypes using a non-negative matrix factorization algorithm. A robust and efficient prognostic signature was constructed by performing multivariate Cox regression analysis and further validated using the Gene Expression Omnibus cohort. Based on the gene expression matrix of CRC, the abundance of infiltrating immune cells and tumour microenvironment scores were calculated using the CIBERSORT and ESTIMATE algorithms, respectively. The pRRophetic algorithm was used to predict the sensitivity of the patients to different chemotherapy drugs. Two distinct molecular subtypes were identified based on 41 CRGs, with subtype C1 being characterized by an advanced clinical stage and worse overall survival. A prognostic signature was constructed based on the DEGs between the two cuproptosis subtypes, and its predictive ability was validated in an external database. Patients with CRC who belonged to the low-risk group had significantly higher survival rates than those who belonged to the high-risk group. Additionally, it remained a valid prognostic indicator in strata of age, sex, tumor location, and TNM stage, and its significance persisted after the multivariate Cox regression analysis. By further analyzing the prognostic signature, a higher immune score was observed in the low-risk group, which presented a better prognosis. AKT.inhibitor.VIII, doxorubicin, lenalidomide, and tipiparnib were more sensitive in the high-risk score group. A highly accurate nomogram was constructed to improve clinical application of the risk score. Compared with an ideal nomogram, our model, consisting of clinicopathological features, performed well in predicting patient survival. In conclusion, our study provides new ways and perspectives for the prediction of the prognosis of patients with CRC and guide more effective treatment regimens.

The Beijing-Tianjin-Hebei region, with the capital Beijing as the core, carries a profound geo-culture and gathers numerous intangible cultural heritages. Studying the spatial distribution characteristics and influencing factors of intangible cultural heritage can help to propose positive suggestions for protection and development from the perspective of sustainable development. Taking 329 national intangible cultural heritages in Beijing-Tianjin—Hebei as the research objects, and using ArcGIS spatial analysis technology, combined with methods such as kernel density analysis, standard deviation ellipse and geographical detector, to visualize the spatial distribution of intangible cultural heritages and explore the influencing factors of their distribution. The results show that: (1) the distribution of intangible cultural heritage in Beijing-Tianjin-Hebei is not balanced, mainly concentrated in the central and southern regions, and three high-density core circles are formed in Beijing, Tianjin, and Handan-Xingtai in Hebei Province, showing large differences in spatial distribution density; (2) Traditional techniques, traditional arts, dramatic balladry, sports, music, and medicine show the characteristics of aggregated distribution. folkways, dance, and drama show the characteristics of random distribution. Folk literature shows the characteristics of uniform distribution; (3) The Cultural Development Period is an important evolutionary turning point in the spatial and temporal distribution of intangible cultural heritage in Beijing-Tianjin-Hebei, as well as an accelerating period in the development of intangible cultural heritage, and completes the transformation from decentralized distribution to localized aggregation distribution. In The Period of Regional Cultural Integration, the peak value is reached; (4) Economic development and social culture have the greatest influence on the spatial distribution of intangible cultural heritage in Beijing-Tianjin-Hebei, while the influence of physical environment is relatively small.

IntroductionThis study aimed to summarize the evidence describing the relationship between maternal factors during gestation and risk of congenital heart disease (CHD) in offspring.MethodsPubMed, EMBASE, and the Cochrane Library were searched for potentially relevant reports from inception to May 2021. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) calculated by the random-effects model were used to evaluate the association between maternal factors and CHD risk.ResultsThere was a significant association between CHD risk and obesity in pregnancy (OR 1.29, 95% CI 1.22–1.37; P < 0.001), smoking in pregnancy (OR 1.16, 95% CI 1.07–1.25; P < 0.001), maternal diabetes (OR 2.65, 95% CI 2.20–3.19; P < 0.001), and exposure of pregnant women to organic solvents (OR 1.82, 95% CI 1.23–2.70; P = 0.003). No correlations were revealed between CHD susceptibility and advanced maternal age (OR 1.04, 95% CI 0.96–1.12; P = 0.328), underweight (OR 1.02, 95% CI 0.96–1.08; P = 0.519), alcohol intake in pregnancy (OR 1.08, 95% CI 0.95–1.22; P = 0.251), coffee intake (OR 1.18, 95% CI 0.97–1.44; P = 0.105), and exposure to irradiation (OR 1.80, 95% CI 0.85–3.80; P = 0.125).DiscussionMaternal factors including maternal obesity, smoking in pregnancy, maternal diabetes and exposure to organic solvents might predispose the offspring to CHD risk.

A bstract We have constructed all the three-family N = 1 supersymmetric Pati-Salam models from intersecting D6-branes, and obtained 33 independent models in total. But how to realize the string-scale gauge coupling relations in these models is a big challenge. We first discuss how to decouple the exotic particles in these models. In addition, we consider the adjoint chiral mulitplets for SU(4) C and SU(2) L gauge symmetries, the Standard Model (SM) vector-like particles from D6-brane intersections, as well as the vector-like particles from the N = 2 subsector. We show that the gauge coupling relations at string scale can be achieved via two-loop renormalization group equation running for all these supersymmetric Pati-Salam models. Therefore, we propose a concrete way to obtain the string-scale gauge coupling relations for the generic intersecting D-brane models.

We construct new three-family N = 1 supersymmetric Pati–Salam models from intersecting D6-branes with original gauge group U ( 4 ) C × USp ( 2 ) L × U ( 2 ) R on a Type IIA T 6 / ( Z 2 × Z 2 ) orientifold. We find that replacing a U ( 2 ) with a USp ( 2 ) group severely restricts the number of three-generation supersymmetric models such that there are only five inequivalent models. Exchanging the left and right sectors, we obtain five dual models with gauge group U ( 4 ) C × U ( 2 ) L × USp ( 2 ) R . These ten models have different gauge coupling relations at string scale. The highest wrapping number is 4, and one of the models contains no filler O6-planes. Moreover, we discuss in detail the particle spectra, the composite particles through strong coupling dynamics, and the exotic particle decouplings. Also, we study how to realize the string-scale gauge coupling relations in some models.

All the possible three-family N=1 supersymmetric Pati–Salam models constructed with intersecting D6-branes from Type IIA orientifolds on T6/(Z2×Z2) are recently presented in arXiv:2112.09632. Taking models with largest wrapping number 5 and approximate gauge coupling unification at GUT scale as examples, we show string scale gauge coupling unification can be realized through two-loop renormalization group equation running by introducing seven pairs of vector-like particles from N=2 sector. The number of these introduced vector-like particles are fully determined by the brane intersection numbers while there are two D6-brane parallel to each other along one two-torus. We expect this will solve the gauge coupling unification problem in the generic intersecting brane worlds by introducing vector-like particles that naturally included in the N=2 sector.

Therapeutic antibody development requires selection and engineering of molecules with high affinity and other drug-like biophysical properties. Co-optimization of multiple antibody properties remains a difficult and time-consuming process that impedes drug development. Here we evaluate the use of machine learning to simplify antibody co-optimization for a clinical-stage antibody (emibetuzumab) that displays high levels of both on-target (antigen) and off-target (non-specific) binding. We mutate sites in the antibody complementarity-determining regions, sort the antibody libraries for high and low levels of affinity and non-specific binding, and deep sequence the enriched libraries. Interestingly, machine learning models trained on datasets with binary labels enable predictions of continuous metrics that are strongly correlated with antibody affinity and non-specific binding. These models illustrate strong tradeoffs between these two properties, as increases in affinity along the co-optimal (Pareto) frontier require progressive reductions in specificity. Notably, models trained with deep learning features enable prediction of novel antibody mutations that co-optimize affinity and specificity beyond what is possible for the original antibody library. These findings demonstrate the power of machine learning models to greatly expand the exploration of novel antibody sequence space and accelerate the development of highly potent, drug-like antibodies. Optimising antibody properties such as affinity can be detrimental to other key properties. Here the authors use machine learning to simplify the identification of antibodies with co-optimal levels of affinity and specificity for a clinical-stage antibody that displays high levels of on- and off-target binding.

In recent years, it has become common practice for Chinese local governments to inject land assets into financing platform companies and use them as mortgage or credit guarantees to obtain bank loans and issue urban investment bonds, which is known as “land financialization”. This study investigates the impact and mechanism of land financialization on the uncoordinated development of population urbanization and land urbanization in China. Theoretical analysis and empirical analysis results based on the data of prefecture-level cities in China from 2006 to 2015 demonstrate that land financialization by local governments is a significant cause of the uncoordinated development of population urbanization and land urbanization, and the pressure of urban economic development will strengthen this negative impact. Extended analysis further reveals that in areas where population urbanization and land urbanization are uncoordinated, land financialization, while promoting urban spatial expansion, will lower land use efficiency and have an inverted U-shaped influence on economic growth due to a weak agglomeration effect. The above conclusion shows that urbanization driven by debt-based investment is unsustainable. Efforts should be made to establish a financialization system that propels sound urbanization and to build a stable input linkage between land financialization and the supply of urban public service.

Cadmium (Cd) contamination in paddy fields and its subsequent transfer in soil–rice systems are of particular concern. Significant discrepancies exist in the transfer process of Cd pollution sources from soil to rice. Here, we proposed a novel hybrid framework to reveal the priority of controlling Cd pollution sources in soil–rice systems, based on a high-dimensional geographical database. We further defined transfer potential (TP) to describe the ability of Cd from soil to rice (TP r  = Cd r /Cd s ) and activated status (TP a  = Cd a /Cd s ), respectively, to reveal the priority sources of Cd pollution at the regional scale. The mining source has both high levels of TP r and TP a , which should be a controlled priority. Followed by traffic sources with a higher value of TP r , showing the risk to rice rather than the soil. The activated and enriched capacities of soil Cd are unequal in different sources that we attribute to the disparities of Cd transport in soil–rice systems. Cd contamination shows a significant spatial heterogeneity due to the difference in its transport performance. Our findings provide support for designing site-specific and pollution-targeted control priorities for suitable Cd pollution mitigation strategies at the regional scale.