Tumor necrosis factor alpha (TNF-α) is considered to play an important role in the pathogenesis in IgA nephropathy (IgAN). The correlations between serum TNF-α and disease severity in patients with IgAN remain controversial. Concentrations of serum TNF-α of 147 patients with IgAN and 126 healthy subjects were measured by chemiluminescence immunoassay. Correlations with clinicopathological features of patients with IgAN were evaluated. Serum levels of TNF-α [9.20 (7.70–10.60) pg/mL vs. 6.04 (5.11–7.23) pg/mL, P < 0.0001] were higher in patients with IgAN than that in healthy subjects. Receiver operating characteristic curve analysis revealed that TNF-α had better discrimination between patients with IgAN and healthy controls than estimated glomerular filtration rate [TNF-α: (AUC, 0.87; 95% CI, 0.83–0.91; P < 0.0001) vs. estimated glomerular filtration rate: (AUC, 0.76; 95% CI, 0.71–0.82; P < 0.0001), P = 0.007]. Multivariate linear regression analyses showed that serum levels of TNF-α were positively correlated with 24-h urine protein excretion (r = 0.33, P = 0.04), urinary protein to serum creatinine ratio (r = 0.33, P = 0.03), serum creatinine (r = 0.46, P < 0.0001) and Cystatin C (r = 0.59, P < 0.0001) in IgAN and negatively correlated with estimated glomerular filtration rate (r = − 0.49, P < 0.0001) after adjustment for sex, systolic blood pressure and diastolic blood pressure. Patients with higher mesangial hypercellularity or tubular atrophy/interstitial fibrosis score according to Oxford classification showed higher serum levels of TNF-α. Our data showed that serum levels of TNF-α detected by chemiluminescence immunoassay was a potential biomarker for evaluating the disease severity in IgAN.

Scarcity of annotated image data sets of thin blood smears makes expert-level differentiation among Plasmodium species challenging. Here, we aimed to establish a deep learning algorithm for identifying and classifying malaria parasites in thin blood smears and evaluate its performance and clinical prospect. You Only Look Once v7 was used as the backbone network for training the artificial intelligence algorithm model. The training, validation, and test sets for each malaria parasite category were randomly selected. A comprehensive analysis was performed on 12 708 thin blood smear images of various infective stages of 12 546 malaria parasites, including P falciparum, P vivax, P malariae, P ovale, P knowlesi, and P cynomolgi. Peripheral blood samples were obtained from 380 patients diagnosed with malaria. Additionally, blood samples from monkeys diagnosed with malaria were used to analyze P cynomolgi. The accuracy for detecting Plasmodium-infected blood cells was assessed through various evaluation metrics. The total time to identify 1116 malaria parasites was 13 seconds, with an average analysis time of 0.01 seconds for each parasite in the test set. The average precision was 0.902, with a recall and precision of infected erythrocytes of 96.0% and 94.9%, respectively. Sensitivity and specificity exceeded 96.8% and 99.3%, with an area under the receiver operating characteristic curve >0.999. The highest sensitivity (97.8%) and specificity (99.8%) were observed for trophozoites and merozoites. The algorithm can help facilitate the clinical and morphologic examination of malaria parasites.

Background: Prehypertension and prediabetes are major risk factors of cardiovascular disease, and their combined presence may result in more serious cardiovascular outcomes than expected with either prehypertension or prediabetes alone. The aim of the present study was to evaluate the prevalence of coexisting prehypertension and prediabetes, and the associated risk profiles in a Chinese population. Methods: A cross-sectional survey in a representative sample of 3,595 men and 4,593 women aged 18 years and older was performed between 2008 and 2010. Prehypertension and prediabetes were diagnosed using the guidelines from the Seventh Report of the Joint National Committee on prevention, detection, and treatment of high blood pressure and American Diabetes Association, respectively. Prehypertension was defined as a systolic blood pressure of 120-139 mmHg and/or diastolic blood pressure of 80-89 mmHg, and prediabetes was defined as a fasting blood glucose of 5.6-6.9 mmol/L. Results: The prevalence of coexisting prehypertension and prediabetes was 11.0%. Men had a higher prevalence of coexisting prehypertension and prediabetes than women (14.2% vs. 8.4%; P < 0.0001). This prevalence increased with age and body mass index, and was the lowest among Mongolian-Chinese (5.1%). A multivariate analysis showed that γ-glutamyltransferase and uric acid were significantly and positively correlated with body mass index, waist circumference, blood pressure, triglycerides, and total cholesterol, and negatively correlated with high density lipoprotein cholesterol in subjects with prehypertension and prediabetes. Conclusions: There is a large proportion of Chinese adults with coexisting prehypertension and prediabetes. Thus, there is a need for more efforts that implement public health programs that target the earlier stages of hypertension and diabetes.

Objective Airway inflammation plays an important role in obstructive sleep apnea (OSA); exhaled nitric oxide is regarded as a noninvasive marker of airway inflammation. The aim of this study was to evaluate fractional exhaled nitric oxide (FeNO) and nasal nitric oxide (nNO) in patients with OSA. Methods Seventy-five patients with OSA and 30 health controls were enrolled in this study. FeNO and nNO were measured before and after sleep. Nasal lavage was performed in 31 non-smoking individuals immediately after NO measurement in the morning. The sample of nasal lavage was taken for cell classification and analyzing interleukin 6 (IL-6) and interleukin 8 (IL-8). Results Both FeNO and nNO were significantly higher in OSA (before sleep FeNO 21.08 ± 8.79 ppb vs.16.90 ± 6.86 ppb, p = 0.022; after sleep FeNO 25.57 ± 15.58 ppb vs.18.07 ± 6.25 ppb, p = 0.003; before sleep nNO 487.03 ± 115.83 ppb vs. 413.37 ± 73.10 ppb, p = 0.001; after sleep nNO 550.07 ± 130.24 ppb vs. 460.43 ± 109.77 ppb, p < 0.001). Furthermore, in non-smoking OSA, nNO levels were positively correlated with apnea hypopnea index (AHI) and average decrease of pulse arterial oxygen saturation (SpO₂); after sleep, nNO was also positively associated to recording time with SpO₂ < 90% and negatively associated to minimum SpO₂. Both before and after sleep nNO levels were positively correlated with the percentage of neutrophils in nasal lavage (r = 0.528, p = 0.014; r = 0.702, p < 0.001, respectively). Additionally, before sleep nNO was also positively associated with IL-6 (r = 0.586, p = 0.005) and IL-8 (r = 0.520, p = 0.016) concentration. Conclusion This study sustains the presence of airway inflammation in OSA patients with the increase of FeNO and nNO. The data suggests nNO might have greater value than FeNO since it positively correlated with OSA severity, and nNO is a potential bio-marker of nasal inflammation in non-smoking OSA patients.

Background: Serum γ-glutamyltransferase (GGT) and uric acid (UA) levels are elevated in patients with diabetes or cardiovascular disease. Prediabetes, characterized by impaired glucose tolerance, is an important risk factor for overt diabetes as well as cardiovascular disease. Therefore, the aim of this study was to explore the relationship between GGT, UA and prediabetes in a Chinese population, and provide a scientific basis for the early prevention and treatment of diabetes. Methods: We performed a cross-sectional population-based study in a cohort of 2694 subjects (1211 men and 1483 women, aged 35–86 years). Questionnaires and physical examinations were performed using standardized procedures. Fasting blood was collected to measure glucose and other biochemical parameters. The subjects were divided into two groups with either normal fasting glucose (NFG) or impaired fasting glucose (IFG), according to international diagnostic criteria. Logistic regression analysis was performed to estimate odds ratios (OR) and 95% confidence intervals. Results: Compared with the NFG group, the IFG group had significantly higher blood pressure but lower high-density lipoprotein–cholesterol in women. Body mass index, waist circumference, triglyceride, glucose, GGT, and UA levels were significantly higher in males and females in the IFG group than those in the NFG group. Logistic regression analysis revealed that the OR for prediabetes increased with increasing serum GGT quartiles and UA quartiles. GGT and UA were positively associated with prediabetes in men and women, independent of age, ethnicity, smoking, alcohol consumption, blood pressure, physical labor, and other confounders. Conclusions: We found that serum GGT and UA levels were positively associated with prediabetes in men and women living in areas inhabited by Chinese ethnic minorities. As elevated GGT and UA levels were associated with significantly increased risk of prediabetes, they may be used as sensitive biological markers of prediabetes.