A MALDI-TOF mass spectrometry-based haemoglobin chain quantification method for rapid screen of thalassaemia.

BACKGROUND: Thalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden. Considering its high prevalence in low and middle-income countries, a cheap, accurate and high-throughput screening test of thalassaemia prior to a more expensive confirmatory diagnostic test is urgently needed. METHODS: In this study, we constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains in blood, and for the first time, evaluated its diagnostic efficacy in 674 thalassaemia (including both asymptomatic carriers and symptomatic patients) and control samples collected in three hospitals. Parameters related to haemoglobin imbalance (α-globin, ß-globin, γ-globin, α/ß and α-ß) were used for feature selection before classification model construction with 8 machine learning methods in cohort 1 and further model efficiency validation in cohort 2. RESULTS: The logistic regression model with 5 haemoglobin peak features achieved good classification performance in validation cohort 2 (AUC 0.99, 95% CI 0.98-1, sensitivity 98.7%, specificity 95.5%). Furthermore, the logistic regression model with 6 haemoglobin peak features was also constructed to specifically identify ß-thalassaemia (AUC 0.94, 95% CI 0.91-0.97, sensitivity 96.5%, specificity 87.8% in validation cohort 2). CONCLUSIONS: For the first time, we constructed an inexpensive, accurate and high-throughput classification model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains and demonstrated its great potential in rapid screening of thalassaemia in large populations.Key messagesThalassaemia is one of the most common inherited monogenic diseases worldwide with a heavy global health burden.We constructed a machine learning model based on MALDI-TOF mass spectrometry quantification of haemoglobin chains to screen for thalassaemia.

Rapid Detection of COVID-19 Using MALDI-TOF-Based Serum Peptidome Profiling.

The outbreak of coronavirus disease 2019 (COVID-19) caused by SARS CoV-2 is ongoing and a serious threat to global public health. It is essential to detect the disease quickly and immediately to isolate the infected individuals. Nevertheless, the current widely used PCR and immunoassay-based methods suffer from false negative results and delays in diagnosis. Herein, a high-throughput serum peptidome profiling method based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is developed for efficient detection of COVID-19. We analyzed the serum samples from 146 COVID-19 patients and 152 control cases (including 73 non-COVID-19 patients with similar clinical symptoms, 33 tuberculosis patients, and 46 healthy individuals). After MS data processing and feature selection, eight machine learning methods were used to build classification models. A logistic regression machine learning model with 25 feature peaks achieved the highest accuracy (99%), with sensitivity of 98% and specificity of 100%, for the detection of COVID-19. This result demonstrated a great potential of the method for screening, routine surveillance, and diagnosis of COVID-19 in large populations, which is an important part of the pandemic control.

Study On Radon Concentration Variation During Subway Construction.

The high radon concentration in the underground space of the subway station during construction often endangers the health of workers. Subway station project No. 16 in Beijing, while under construction, was selected as the main measuring point, a year's monitoring data was obtained to analyse the change of radon concentration. It was found that the concentration of radon was basically within the range of 5 ~ 500 Bq/m3 and showing a low level in the morning and a high level at noon, and presents the seasonal rule, compared with other seasons, the summer radiation is stronger. Furthermore, among the different measuring points, the radon concentration of the heading roadway is the highest, and the construction level of the station hall is the lowest. According to the comprehensive analysis, the concentration of radon during the construction of the subway station is mainly affected by the ambient temperature and air mobility.

Expression Profiles and Binding Properties of the Chemosensory Protein PxylCSP11 from the Diamondback Moth, Plutella xylostella (Lepidoptera: Plutellidae).

The diamondback moth, Plutella xylostella L. (Lepidoptera: Plutellidae) is one of the most destructive pests to cruciferous plants worldwide. The oligophagous moth primarily utilizes its host volatiles for foraging and oviposition. Chemosensory proteins (CSPs) are soluble carrier proteins with low molecular weight, which recognize and transport various semiochemicals in insect chemoreception. At present, there is limited information on the recognition of host volatiles by CSPs of P. xylostella. Here, we investigated expression patterns and binding characteristics of PxylCSP11 in P. xylostella. The open reading frame of PxylCSP11 was 369-bp encoding 122 amino acids. PxylCSP11 possessed four conserved cysteines, which was consistent with the typical characteristic of CSPs. PxylCSP11 was highly expressed in antennae, and the expression level of PxylCSP11 in male antennae was higher than that in female antennae. Fluorescence competitive binding assays showed that PxylCSP11 had strong binding abilities to several ligands, including volatiles of cruciferous plants, and (Z)-11-hexadecenyl acetate (Z11-16:Ac), a major sex pheromone of P. xylostella. Our results suggest that PxylCSP11 may play an important role in host recognition and spouse location in P. xylostella.