Research Progress on Ammonia Sensors Based on Ti3C2Tx MXene at Room Temperature: A Review.

Ammonia (NH3) potentially harms human health, the ecosystem, industrial and agricultural production, and other fields. Therefore, the detection of NH3 has broad prospects and important significance. Ti3C2Tx is a common MXene material that is great for detecting NH3 at room temperature because it has a two-dimensional layered structure, a large specific surface area, is easy to functionalize on the surface, is sensitive to gases at room temperature, and is very selective for NH3. This review provides a detailed description of the preparation process as well as recent advances in the development of gas-sensing materials based on Ti3C2Tx MXene for room-temperature NH3 detection. It also analyzes the advantages and disadvantages of various preparation and synthesis methods for Ti3C2Tx MXene's performance. Since the gas-sensitive performance of pure Ti3C2Tx MXene regarding NH3 can be further improved, this review discusses additional composite materials, including metal oxides, conductive polymers, and two-dimensional materials that can be used to improve the sensitivity of pure Ti3C2Tx MXene to NH3. Furthermore, the present state of research on the NH3 sensitivity mechanism of Ti3C2Tx MXene-based sensors is summarized in this study. Finally, this paper analyzes the challenges and future prospects of Ti3C2Tx MXene-based gas-sensitive materials for room-temperature NH3 detection.

Clinical Characteristics and Prognostic Predictors of Pneumocystis Jirovecii Pneumonia in Patients with and without Chronic Pulmonary Disease: A Retrospective Cohort Study.

Objective: Pneumocystis jirovecii pneumonia (PJP) is a severe respiratory infection caused by Pneumocystis jirovecii in immunocompromised hosts. The role of P. jirovecii colonization in the development or progression of various pulmonary diseases has been reported. Our aim was to explore serial change in serum biomarkers and the independent risk factors for mortality in patients with and without chronic pulmonary diseases who developed PJP. Methods: We performed a retrospective study to select patients with Pneumocystis jirovecii pneumonia between January 1, 2012, and December 31, 2021. Information regarding demographics, clinical characteristics, underlying diseases, laboratory tests, treatment, and outcomes was collected. Univariate and multivariate logistic regression analyses were used to identify independent predictors of in-hospital mortality. Results: A total of 167 patients diagnosed with PJP were included in the study: 53 in the CPD-PJP group and 114 in the NCPD-PJP group. The number of patients with PJP showed an increasing trend over the 10-year period. A similar trend was observed for in-hospital mortality. Independent risk factors associated with death in the NCPD-PJP group were procalcitonin level (adjusted OR 1.08, 95% CI 1.01-1.16, P=0.01), pneumothorax (adjusted OR 0.07, 95% CI 0.01-0.38, P=0.002), neutrophil count (adjusted OR 1.27, 95% CI 1.05-1.53, P=0.01) at 14 days, and hemoglobin level (adjusted OR 0.94, 95% CI 0.91-0.98; P=0.002) at 14 days after admission. The risk factor associated with death in the CPD-PJP group was neutrophil count (adjusted OR 1.19, 95% CI 0.99-1.43; P=0.05) at 14 days after admission. Conclusion: The risk factors for death were different between patients with PJP with and without chronic pulmonary disease. Early identification of these factors in patients with PJP and other underlying diseases may improve prognosis.

The effect of PDK1 in maintaining immune cell development and function.

3-phosphoinositide-dependent protein kinase 1 (PDK1) exhibits a substantial influence on immune cell development by establishing a vital connection between PI3K and downstream mTOR signaling cascades. However, it remains unclear whether PDK1 signaling affects the homeostasis and functionality of immune cells. To explore the impact of PDK1 on different immune cells within immune organs, transgenic mouse strains with lymphocyte-specific PDK1 knockout (PDK1fl/fl CD2-Cre) were generated. Unlike wild-type (WT) mice, lymphocyte-specific PDK1 knockout (KO) mice exhibited thymic atrophy, elevated percentages of CD8+ T cells and neutrophils, and reduced proportions of γδ T cells, B cells, and NK cells in the spleen. Functional analysis revealed elevated release of IFN-γ and IL-17A by T cells in PDK1 KO mice, contrasting with diminished levels observed in γδ T cells and Treg cells. Furthermore, the activation, cytotoxicity, and migratory potential of γδ T cells in PDK1 KO mice are heightened, indicating a potential association with the regulation of the mTOR signaling pathway. To conclude, the findings of this research demonstrated that specific knockout of PDK1 in lymphocytes hindered T cell development in the thymus and exhibited a substantial influence on immune cell homeostasis in the spleen and lymph nodes.

A new particle filter algorithm filtering motion artifact noise for clean electrocardiogram signals in wearable health monitoring system.

With the evolution of wearable systems, more and more people tend to wear wearable devices for health monitoring during sports. However, a large amount of motion artifact noise is introduced at this time, which is difficult to filter out due to its stochasticity. The amplitude and characteristics of motion artifact noise vary with changes in motion intensity. In order to filter out the motion artifact noise, the paperproposes a new particle algorithm, which can detect the intensity of the motion artifact for adaptive filtering, especiallysuitable for wearable health monitoring systems. In this algorithm, variational mode decomposition was first introduced to analyze the noisy electrocardiogram (ECG) signal in order to find the clean components. Then, the Laguerre estimation technique was applied to obtain an accurate ECG polar model. Taking this model as the state equation, a particle filter algorithm was defined to filter out the motion artifact noise. In the particle filter algorithm, we defined a parameter γ whose values were obtained from the six-axis data of motion sensor MPU6050 in our wearable device. This parameter γ could reflect the current noise levels and adaptively update the particle weights. Finally, some exercise experiments proved that the parameter γ could map the motion artifacts in real time and also demonstrated the superiority of the algorithm in terms of signal-to-noise ratio improvement and error reduction compared to other algorithms. The new particle filter algorithm proposed in this paper combines the six-axis data (three-axis accelerometer and three-axis gyroscope) with the ECG signal to effectively eliminate a large amount of motion artifact noise, thus solving the problem of excess noise from wearable devices when people are exercising, allowing them to accurately obtain real-time ECG health information.