Development and validation of a prognostic model for cervical cancer by combination of machine learning and high-throughput sequencing.

BACKGROUND: Cervical cancer holds the highest morbidity and mortality rates among female reproductive tract tumors. However, the curative outcomes for patients with persistent, recurrent, or metastatic cervical cancer remain unsatisfactory. There is a lack of comprehensive prognostic indicators for cervical cancer. This study aims to develop a model that evaluates the prognosis of cervical cancer in combination of high-throughput sequencing and various machine learning algorithms. METHODS: In this study, we combined two single-cell RNA sequencing (scRNA-seq) projects and TCGA data for cervical cancer to obtain shared differentially expressed genes (DEGs). A LASSO regression and several learners were applied for signature feature selection. Six machine learning algorithms including Linear Discriminant Analysis, Naive Bayes, K Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting were utilized to construct a prognostic model for cervical cancer. External validation was conducted using the CGCI-HTMCP-CC dataset, and the accuracy of the model was assessed through ROC curve analysis. RESULTS: The results demonstrated the successful construction of a prognostic model based on DEGs from bulk- and scRNA-seq data. Ten genes CXCL8, DLC1, GRN, MPLKIP, PRDX1, RUNX1, SNX3, TFRC, UBE2V2, and UQCRC1 were screened by feature selection and applied for model construction. Random Forest exhibited the best performance in predicting the risk of cervical cancer. Patients in the high-risk group presented worse overall survival compared to those in the low-risk group. CONCLUSION: Conclusively, our model based on DEGs from bulk-seq and scRNA-seq data effectively evaluates the prognosis of cervical cancer and provides valuable insights for comprehensive clinical management.

Traditional methods and biosensors for detecting disinfection by-products in water: A review.

In recent years, pollution caused by disinfection by-products (DBPs) has become a global concern. Initially, there were fewer contaminants, and the mechanism of their generation was unclear; however, the number of contaminants has increased exponentially as a result of rapid industrialization and numerous economic activities (e.q., during the outbreak of COVID-19 a surge in the use of chlorinated disinfectants was observed). DBP toxicity results in various adverse health effects and organ failure in humans. In addition, it profoundly affects other forms of life, including animals, plants, and microorganisms. This review comprehensively discusses the pre-treatment methods of traditional and emerging DBPs and the technologies applied for their detection. Additionally, this paper provides a detailed discussion of the principles, applicability, and characteristics of traditional large-scale instrumentation methods (such as gas/liquid/ion chromatography coupled with mass spectrometry) for detecting DBPs based on their respective detection techniques. At the same time, the design, functionality, classification, and characteristics of rapid detection technologies (such as biosensors) are also detailed and analyzed.

Nanohybrid of antimonene@Ti3C2Tx-based electrochemical aptasensor for lead detection.

Lead ions (Pb2+), as one of many common heavy metallic environmental pollutants, can cause serious side-effects and result in chronic poisoning to people's health, so it is highly significant to monitor Pb2+ efficiently and sensitively. Here, we proposed an antimonene@Ti3C2Tx nanohybrid-based electrochemical aptamer sensor (aptasensor) for high sensitive Pb2+ determination. The sensing platform of nanohybrid was synthesized by ultrasonication, possessing the advantages of both antimonene and Ti3C2Tx, which not only can vastly enlarge the sensing signal of the proposed aptasensor, but also greatly simplified its manufacturing flow, because antimonene can strongly interact with aptamer through noncovalently bound. The surface morphology and microarchitecture of the nanohybrid were perused by several methods such as scanning electron microscope (SEM), energy-dispersive X-ray mapping spectroscopy (EDS), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and atomic force microscope (AFM). Under optimal empirical conditions, the proposed aptasensor exhibited a wide linear correlation of the current signals with the logarithm of CPb2+ (Log CPb2+) over the span from 1 × 10-12 to 1 × 10-7 M and provided a trace discernment limit of 3.3 × 10-13 M. Moreover, the constructed aptasensor displayed superior repeatability, great consistency, eminent selectivity, and beneficial reproducibility, implying its extreme potential application for water quality control and the environmental monitoring of Pb2+.

In situ synthesis of label-free electrochemical aptasensor-based sandwich-like AuNPs/PPy/Ti3C2Tx for ultrasensitive detection of lead ions as hazardous pollutants in environmental fluids.

The monitoring of hazardous pollutants in environmental fluids is one of main stretaegy in investigation of water and soil quality. Metal ions are one of main and dangerius materials in water sampels and one of the main causes of environmental problems. Therefore, many of environmental researchers focused on fabrication of highly sensitive sensor to ion hazardous pollutants environmental fluids. The encapsulation of 2D MXenes with other stable materials has proven to be an effective method for enhancing their stability and electrochemical properties. In this work, a sandwich-like nanocomposite structure, AuNPs/PPy/Ti3C2Tx, was designed and synthesized via a facile method of one-step layer-by-layer self-assembly. The morphology and structure of the prepared nanocomposites are characterized with various methods such as scanning electron microscope (SEM), transmission electron microscope (TEM), X-ray photoelectron spectroscopy (XPS) and X-ray diffraction (XRD). Ti3C2Tx as a substrate played a significant role in the synthesis and alignment of PPy and AuNPs growth. The nanocomposites have maximized the benefits of the inorganic AuNPs and organic PPy materials, enhancing their stability and electrochemical performance. Meanwhile, AuNPs have given the nanocomposite the ability to form covalent bonds with biomaterials via the Au-S bond. Thus, a novel electrochemical aptasensor was developed based on AuNPs/PPy/Ti3C2Tx for the sensitive and selective detection of Pb2+. It demonstrated a wide linear range from 5 × 10-14 to 1 × 10-8 M with a low LOD of 1 × 10-14 M (S/N = 3). Additionally, the developed aptasensor exhibited excellent selectivity and stability and successfully used to sensing of Pb2+ in environmental fluids such as NongFu Spring and tap water.

On the fossil and non-fossil fuel sources of carbonaceous aerosol with radiocarbon and AMS-PMF methods during winter hazy days in a rural area of North China plain.

Regional transport is a key source of carbonaceous aerosol in many Chinese megacities including Beijing. The sources of carbonaceous aerosol in urban areas have been studied extensively but are poorly known in upwind rural areas. This work aims to quantify the contributions of fossil and non-fossil fuel emissions to carbonaceous aerosols at a rural site in North China Plain in winter 2016. We integrated online high resolution-time of flight-aerosol mass spectrometer (HR-TOF-AMS) observations and radiocarbon (14C) measurements of fine particles with Positive Matrix Factorization (PMF) analysis as well as Extended Gelencsér (EG) method. We found that fine particle concentration is much higher at the rural site than in Beijing during the campaign (Dec 7, 2016 to Jan 8, 2017). PMF analysis of the AMS data showed that coal-combustion related organic aerosol (CCOA + Oxidized CCOA) and more oxidized oxygenated organic aerosol (MO-OOA) contributed 48% and 30% of organic matter to non-refractory PM1 (NR-PM1) mass. About 2/3 of the OC and EC were from fossil-fuel combustion. The EG method, combining AMS-PMF and 14C data, showed that primary and secondary OC from fossil fuel contribute 35% and 22% to total carbon (TC), coal combustion emission dominates the fossil fuel sources, and biomass burning accounted for 21% of carbonaceous aerosol. In summary, our results confirm that fossil fuel combustion was the dominant source of carbonaceous aerosol during heavy pollution events in the rural areas. Significant emissions of solid fuel carbonaceous aerosols at rural areas can affect air quality in downwind cities such as Beijing and Tianjin, highlighting the benefits of energy transition from solid fuels to cleaner energy in rural areas.

Drivers of the rapid rise and daily-based accumulation in PM1.

Submicron particle matter (PM1) that rapidly reaches exceedingly high levels in several or more hours in the North China Plain (NCP) has been threating~400 million individuals' health for decades. The precise cause of the rapid rise in PM1 remains uncertain. Based on sophisticated measurements in PM1 characterizations and corresponding boundary-layer (BL) meteorology in the NCP, it demonstrates that this rising is mainly driven by BL meteorological variability. Large increases in near-ground inversions and decreases in vertical heat/momentum fluxes during the day-night transition result in a significant reduction in mixing space. The PM1 that is vertically distributed before accumulates at the near-ground and then experiences a rapid rise. Besides meteorological variability, a part of the rise in organics is ascribed to an increase of coal combustion at midnight. The daily-based accumulation of PM1 is attributed to day-to-day vertical meteorological variability, particularly diminishing mixing layer height exacerbated by aerosol-radiation feedback. Resolved by a multiple linear regression model, BL meteorological variability can explain 71% variances of PM1. In contrast, secondary chemical reactions facilitate the daily-based accumulation of PM1 rather than the rapid rise. Our results show that BL meteorological variability plays a dominant role in PM1 rising and day-to-day accumulation, which is crucial for understanding the mechanism of heavy pollution formation.

Significant changes in the chemical compositions and sources of PM2.5 in Wuhan since the city lockdown as COVID-19.

Wuhan was the first city to adopt the lockdown measures to prevent COVID-19 spreading, which improved the air quality accordingly. This study investigated the variations in chemical compositions, source contributions, and regional transport of fine particles (PM2.5) during January 23-February 22 of 2020, compared with the same period in 2019. The average mass concentration of PM2.5 decreased from 72.9 µg m-3 (2019) to 45.9 µg m-3 (2020), by 27.0 µg m-3. It was predominantly contributed by the emission reduction (92.0%), retrieved from a random forest tree approach. The main chemical species of PM2.5 all decreased with the reductions ranging from 0.85 µg m-3 (chloride) to 9.86 µg m-3 (nitrate) (p < 0.01). Positive matrix factorization model indicated that the mass contributions of seven PM2.5 sources all decreased. However, their contribution percentages varied from -11.0% (industrial processes) to 8.70% (secondary inorganic aerosol). Source contributions of PM2.5 transported from potential geographical regions showed reductions with mean values ranging from 0.22 to 4.36 µg m-3. However, increased contributions of firework burning, secondary inorganic aerosol, road dust, and vehicle emissions from transboundary transport were observed. This study highlighted the complex and nonlinear response of chemical compositions and sources of PM2.5 to air pollution control measures, suggesting the importance of regional-joint control.

A highly-sensitive VB2 electrochemical sensor based on one-step co-electrodeposited molecularly imprinted WS2-PEDOT film supported on graphene oxide-SWCNTs nanocomposite.

A highly-sensitive electrochemical sensor based on single walled carbon nanotubes (SWCNTs) nanocomposite electro-catalyst supported molecularly imprinted poly(3,4-ethylenedioxythiophene) (PEDOT) film modified with two-dimensional layered tungsten sulfide (WS2) nanosheet for the detection of vitamin B2 (VB2) was successfully developed. Molecularly imprinted WS2-PEDOT film was prepared by the electrochemical co-polymerization of functional monomer 3,4-ethylenedioxythiophene in the presence of template molecule VB2 and WS2 nanosheet. SWCNTs nanocomposite modified electrode was obtained by drop-coating SWCNTs dispersion containing graphene oxide (GO) onto GCE surface. WS2 was used for improving the denseness and smoothness in the morphology and structure of PEDOT. GO was employed for enhancing the electro-catalytic capacity of SWCNTs and the interaction with template molecule via functional groups. The imprinted sensor was characterized, and its performance and parameters were investigated and optimized using linear sweep voltammetry. Under optimized conditions, the prepared imprinted sensor displayed a good linear response to VB2 in wide concentration ranges from 0.002 to 0.9 µM with a low detection limit of 0.7 nM, and successfully applied to electrochemically detect VB2 in drug samples with good reproducibility, repeatability and storage stability. The new design for the facile fabrication of mimic electrode will open up a new horizon in the development of highly-sensitive biomimic/imprinted sensors.

Highly selective "turn-on" fluorescent sensing of fluoride ion based on a conjugated polymer thin film-Fe3+ complex.

We designed a new fluorescent conjugated polymer thin film sensor via direct electropolymerization of the corresponding electroactive monomer M onto the surface of ITO electrode, and the thin film-Fe3+ complex was used for the highly-selective detection of fluoride ion (F-) in water environmental samples. The as-obtained thin film could effectively detect Fe3+ as a selective turn-off fluorescent sensor, and exhibited outstanding reversibility. This film in the presence of Fe3+ showed a highly selective turn-on response toward F- over other anions with a 5-fold enhancement in the fluorescence intensity. F- with a relatively wide concentration range from 10 µM to 3 mM could be determined in a rather simple and sensitive manner with a detection limit of 6.78 µM (0.128 ppm). Analytical applicability of the film-Fe3+ complex for determining the levels of F- in environmental water samples has been successfully demonstrated by fluorescent analysis with satisfactory results. This strategy will provide a new approach for the facile design of new molecular sensing devices and practical application in environments.

[Sources and Characteristics of Regional Background PM1 in North China During the Autumn and Winter Polluted Period].

The campaign of investigating the chemical compositions and particle size distributions of NR-PM1(non-refractory PM1) was conducted by using a High Resolution Time of Flight Aerosol Mass Spectrometer(HR-ToF-AMS) at the Shangdianzi(SDZ) regional atmospheric background site(117.07°E, 40.39°N), northeast of Beijing, from October 17th 2015 to January 27th 2016. The results showed that organics was the main component of PM1, and the proportion of nitrate was higher than that of sulfate in autumn and winter. The mean mass-resolved size distributions for the main components displayed accumulation mode. The wider organic peak shape and larger nitrate peak size indicated that the organics contributed to both small and large particles at the beginning of the particle formation, growth and aging processes, while most of nitrate particles preferred to grow into large particles during the aging process. The ratios of elements between OM and OC, O/C and H/C were calculated as 1.91, 0.58 and 1.58 respectively. The slope of Van Krevelen diagram of organic aerosols during polluted episode was -0.21, whose oxidation state was higher than those of other city sites. Nitrate was the major contributor of NR-PM1 during the polluted period, while organics was significantly higher than that during clean period. The results of back trajectories analysis demonstrated that the air masses were complex during the pollution episode. The northwest wind from central Inner Mongolia and Siberia dominated the clean episodes, which was conducive to the spread of pollutants.