Recent Advances in Heterogeneous Catalytic Hydrogenation of CO2 to Methane

With the accelerating industrialization, urbanization process, and continuously upgrading of consumption structures, the CO2 from combustion of coal, oil, natural gas, and other hydrocarbon fuels is unbelievably increased over the past decade. As an important carbon resource, CO2 gained more and more attention because of its converting properties to lower hydrocarbon, such as methane, methanol, and formic acid. Among them, CO2 methanation is considered to be an extremely efficient method due to its high CO2 conversion and CH4 selectivity. However, the CO2 methanation process requires high reaction temperatures (300–400°C), which limits the theoretical yield of methane. Thus, it is desirable to find a new strategy for the efficient conversion of CO2 to methane at relatively low reaction temperature, and the key issue is using the catalysts in the process. The advances in the noble metal catalysts, Ni-based catalysts, and Co-based catalysts, for catalytic hydrogenation CO2 to methane are reviewed in this paper, and the effects of the supports and the addition of second metal on CO2 methanation as well as the reaction mechanisms are focused

Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms

BackgroundRadiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However, the specific mechanisms that contribute to this resistance remain unclear. Currently, molecular targeted therapy, including mitochondrial genes, has emerged as a new approach in treating different types of cancers, gaining significant attention as an area of research in addressing the challenge of radiotherapy resistance in cancer.MethodsThe present study employed a rigorous screening methodology within the TCGA database to identify a cohort of patients diagnosed with CC who had received radiotherapy treatment. The control group consisted of individuals who demonstrated disease stability or progression after undergoing radiotherapy. In contrast, the treatment group consisted of patients who experienced complete or partial remission following radiotherapy. Following this, we identified and examined the differentially expressed genes (DEGs) in the two cohorts. Subsequently, we conducted additional analyses to refine the set of excluded DEGs by employing the least absolute shrinkage and selection operator regression and random forest techniques. Additionally, a comprehensive analysis was conducted in order to evaluate the potential correlation between the expression of core genes and the extent of immune cell infiltration in patients diagnosed with CC. The mitochondrial-associated genes were obtained from the MITOCARTA 3.0. Finally, the verification of increased expression of the mitochondrial gene TMEM38A in individuals with CC exhibiting sensitivity to radiotherapy was conducted using reverse transcription quantitative polymerase chain reaction and immunohistochemistry assays.ResultsThis process ultimately led to the identification of 7 crucial genes, viz., GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2, which were strongly associated with radiotherapy sensitivity. The enrichment analysis has unveiled a significant association between these 7 crucial genes and prominent signaling pathways, such as the p53 signaling pathway, KRAS signaling pathway, and PI3K/AKT/MTOR pathway. By utilizing these 7 core genes, an unsupervised clustering analysis was conducted on patients with CC, resulting in the categorization of patients into three distinct molecular subtypes. In addition, a predictive model for the sensitivity of CC radiotherapy was developed using a neural network approach, utilizing the expression levels of these 7 core genes. Moreover, the CellMiner database was utilized to predict drugs that are closely linked to these 7 core genes, which could potentially act as crucial agents in overcoming radiotherapy resistance in CC.ConclusionTo summarize, the genes GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2 were found to be closely correlated with the sensitivity of CC to radiotherapy. Notably, TMEM38A, a mitochondrial gene, exhibited the highest degree of correlation, indicating its potential as a crucial biomarker for the modulation of radiotherapy sensitivity in CC

Efficacy and safety of SGLT-2i in overweight/obese, non-diabetic individuals: a meta-analysis of randomized controlled trials

Introduction: Sodium-glucose cotransporter 2 inhibitor (SGLT-2i) has been shown to decrease blood glucose levels in type 2 diabetes mellitus (T2DM) patients and potentially yield additional benefits in weight loss. This meta-analysis aimed to investigate the efficacy and safety of giving SGLT-2i to overweight/obese, non-diabetic individuals. Material and methods: The search was underpinned by PubMed, Cochrane Central Register of Controlled Trials, Web of Science, and Springer to identify English-language papers on randomized controlled trials (RCTs) on the use of SGLT-2i in overweight/obese, nondiabetic individuals published in and before March 2021, to study its effectiveness and safety. Results were evaluated by weighted mean difference (WMD), standardized mean difference (SMD), risk ratio (RR), and 95% confidence interval (CI). Results: We reviewed 13 papers and compared the SGLT-2i group with the control group (other drugs and placebo) and found that SGLT-2i reduced weight (WMD = –1.33, p = 0.002) and waist circumference (WMD = –1.94, p = 0.03) in overweight/obese, non-diabetic individuals. The use of SGLT-2i is more effective than other interventions in terms of weight loss ≥ 5% (RR = 2.04, p < 0.001), but not in terms of weight loss ≥ 10% (RR = 1.83, p = 0.22). In addition, there were no significant changes in other metabolic parameters, like fasting plasma glucose (FPG), lipids, blood pressure, etc. SGLT-2i increased the risk of infections in urinary tract (RR = 1.91, p = 0.009) andreproductive system (RR = 4.09, p < 0.001). Conclusions: SGLT-2i is a promising candidate to reduce weight and waist circumference to a limited extent in overweight/obese, nondiabetic individuals. Generally, it is safe and effective. However, it potentially increased the risk of urogenital infections, which cannot be ignored

Mn Modified Ni/Bentonite for CO₂ Methanation

To enhance the low-temperature catalytic activity and stability of Ni/bentonite catalyst, Ni-Mn/bentonite catalyst was prepared by introducing Mn into Ni/bentonite catalyst and was used for CO2 methanation. The results indicated that the addition of Mn enhanced the interaction between the NiO and the bentonite carrier, increased the dispersion of the active component Ni and decreased the grain size of the active component Ni, increased the specific surface area and pore volume of the Ni/bentonite catalyst, and decreased the average pore size, which suppressed the aggregation of Ni particles grown during the CO2 methanation process. At the same time, the Mn addition increased the amount of oxygen vacancies on the Ni/bentonite catalyst surface, which promoted the activation of CO2 in the methanation reaction, increasing the low-temperature activity and stability of the Ni/bentonite catalyst. Under the reaction condition of atmospheric pressure, 270 °C, V(H2):V(CO2) = 4, and feed gas space velocity of 3600 mL·gcat−1·h−1, the CO2 conversion on the Ni-Mn/bentonite catalyst with 2wt% Mn was 85.2%, and the selectivity of CH4 was 99.8%. On the other hand, when Mn was not added, the CO2 conversion reached 84.7% and the reaction temperature only raised to 300 °C. During a 150-h stability test, the CO2 conversion of Ni-2wt%Mn/bentonite catalyst decreased by 2.2%, while the CO2 conversion of the Ni/bentonite catalyst decreased by 6.4%

Image_1_Exploring a novel seven-gene marker and mitochondrial gene TMEM38A for predicting cervical cancer radiotherapy sensitivity using machine learning algorithms.png

BackgroundRadiotherapy plays a crucial role in the management of Cervical cancer (CC), as the development of resistance by cancer cells to radiotherapeutic interventions is a significant factor contributing to treatment failure in patients. However, the specific mechanisms that contribute to this resistance remain unclear. Currently, molecular targeted therapy, including mitochondrial genes, has emerged as a new approach in treating different types of cancers, gaining significant attention as an area of research in addressing the challenge of radiotherapy resistance in cancer.MethodsThe present study employed a rigorous screening methodology within the TCGA database to identify a cohort of patients diagnosed with CC who had received radiotherapy treatment. The control group consisted of individuals who demonstrated disease stability or progression after undergoing radiotherapy. In contrast, the treatment group consisted of patients who experienced complete or partial remission following radiotherapy. Following this, we identified and examined the differentially expressed genes (DEGs) in the two cohorts. Subsequently, we conducted additional analyses to refine the set of excluded DEGs by employing the least absolute shrinkage and selection operator regression and random forest techniques. Additionally, a comprehensive analysis was conducted in order to evaluate the potential correlation between the expression of core genes and the extent of immune cell infiltration in patients diagnosed with CC. The mitochondrial-associated genes were obtained from the MITOCARTA 3.0. Finally, the verification of increased expression of the mitochondrial gene TMEM38A in individuals with CC exhibiting sensitivity to radiotherapy was conducted using reverse transcription quantitative polymerase chain reaction and immunohistochemistry assays.ResultsThis process ultimately led to the identification of 7 crucial genes, viz., GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2, which were strongly associated with radiotherapy sensitivity. The enrichment analysis has unveiled a significant association between these 7 crucial genes and prominent signaling pathways, such as the p53 signaling pathway, KRAS signaling pathway, and PI3K/AKT/MTOR pathway. By utilizing these 7 core genes, an unsupervised clustering analysis was conducted on patients with CC, resulting in the categorization of patients into three distinct molecular subtypes. In addition, a predictive model for the sensitivity of CC radiotherapy was developed using a neural network approach, utilizing the expression levels of these 7 core genes. Moreover, the CellMiner database was utilized to predict drugs that are closely linked to these 7 core genes, which could potentially act as crucial agents in overcoming radiotherapy resistance in CC.ConclusionTo summarize, the genes GJA3, TMEM38A, ID4, CDHR1, SLC10A4, KCNG1, and HMGCS2 were found to be closely correlated with the sensitivity of CC to radiotherapy. Notably, TMEM38A, a mitochondrial gene, exhibited the highest degree of correlation, indicating its potential as a crucial biomarker for the modulation of radiotherapy sensitivity in CC.</p