CD 4+, CD 8+ and CD 19+cell surface antigen and abnormal mitochondria ultrastructure of peripheral blood P-type atypical lymphocytes in patients with schizophrenia.

OBJECTIVE: P-type atypical lymphocytes may play important roles in the aetiology and therapy of schizophrenia. However, there is merely a direct immunological characterisation of it. The aim of this study is to explore the surface antigens of these cells and their comparative ultrastructure in schizophrenia. METHODS: We recruited 25 age-and gender-matched patients with unmedicated schizophrenia, other mental diseases and healthy individuals. Peripheral venous blood was smeared and stained. CD4+, CD8+ and CD19+ cell surface antigen- positive lymphocytes were purified using magnetic beads and prepared for light microscopy and electron microscopy. RESULTS: The percentages of P-type atypical lymphocytes (34.53% ± 9.92%) were significantly higher (p < 0.0001) in schizophrenia than that of other mental diseases (9.79% ± 3.45%). These cells could present CD4+, CD8+ and CD19+ surface antigens. Their relative ultrastructure differed from that of normal lymphocytes, especially in mitochondria, which showed abundant, aggregated and quite irregular mitochondria; for example, slight dilation of the foci, swelling, degeneration, and even cavity. CONCLUSIONS: P-type atypical lymphocytes could be found among CD4+, CD8+, and CD19 + lymphocytes with schizophrenia. Their abnormal ultrastructure of mitochondria implied that energy metabolism might play an important role in the aetiology of schizophrenia.

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.