We conducted a systematic review with meta-analysis of randomized controlled trials that evaluated the effect of diabetes mobile phone applications. A total of 1550 participants from 21 studies were included. For type 1 diabetes, a significant 0.49% reduction in HbA1c was seen (95% CI, 0.04-0.94; I = 84%), with unexplained heterogeneity and a low GRADE of evidence. For type 2 diabetes, using diabetes apps was associated with a mean reduction of 0.57% (95% CI, 0.32-0.82; I = 77%). The results had severe heterogeneity that was explained by the frequency of HCP feedback. In studies with no HCP feedback, low frequency and high frequency HCP feedback, the mean reduction is 0.24% (95% CI, 0.02-0.49; I = 0%), 0.33% (95% CI, 0.07-0.59; I = 47%) and 1.12% (95% CI, 0.91-1.32; I = 0%), respectively, with a high GRADE of evidence. There is evidence that diabetes apps improve glycaemic control in type 1 diabetes patients. A reduction of 0.57% in HbA1c was found in type 2 diabetes patients. However, HCP functionality is important to achieve clinical effectiveness. Future studies are needed to explore the cost-effectiveness of diabetes apps and the optimal intensity of HCP feedback.

Background Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.

There is strong evidence to suggest that obesity-related proteins play a key role in pathways that are related to breast cancer. In this study, we aimed to establish a robust obesity-related protein score (ORPS) that could be used to assess breast cancer risk. Based on evidence from high-quality systematic reviews and population studies, we selected nine such proteins that are stable in vitro, and measured their circulating concentrations by ELISA in a case–control study conducted in Chengdu, Sichuan, China, with 279 breast cancer cases and 260 healthy controls. Two obesity-related protein scores (ORPS) were calculated using a three-step method, with linear-weighted summation, and the one with a larger area under the curve was chosen for further evaluation. As a result, ORPS (PS5pre or PS4post) was positively correlated with breast cancer risk (premenopausal: OR≤63 VS >63 3.696, 95% CI 2.025–6.747; postmenopausal: OR≤38 VS >38 7.100, 95% CI 3.134–16.084), and represented a better risk predictor among obese women compared to non-obese in pre- and postmenopausal women. Among different molecular subtypes, ORPS was positively correlated with Luminal breast cancer, with additionally positive association with triple-negative breast cancer in premenopausal women. The ORPS might be a potential marker of breast cancer risk among Chinese women.

Background: Drug use safety in children is a global public health problem. The potentially inappropriate prescription screening tools are expected to reduce adverse drug reactions and promote rational drug use.Objectives: To systematically evaluate children’s potentially inappropriate prescription screening tools and validation studies on these tools.Methods: We systematically searched six databases PubMed, Embase, Cochrane Library, CNKI, VIP and Wanfang Data. Two reviewers independently selected articles by the eligible criteria and extracted data. Then we evaluated the coverage of diseases or drugs in these tools and the consistency of items between tools.Results: Five children’s potentially inappropriate prescription screening tools were identified, most tools were formed by Delphi expert consensus and focused on respiratory system drugs, anti-infective drugs, and gastrointestinal drugs. The coincidence rates of items between the POPI and the POPI Int, the POPI and the POPI United Kingdom, the POPI United Kingdom and the POPI int, and the POPI United Kingdom and the PIPc were 82.0, 55.1, 51.0 and 2.2% respectively, and the KIDs List did not overlap other four tools. Only the POPI tool developed by French experts was comprehensively validated by studies and most tools have not been validated.Conclusion: The development of screening tools for potentially inappropriate prescribing in children is a neglected field and most tools lack studies to validate clinical applicability. More researchers need to form their national potentially inappropriate prescription screening tools for children based on the best available clinical evidence and the actual clinical situation in their countries.

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Background Azithromycin (AZI) is increasingly used for childhood asthma despite limited and inconsistent data. We aimed to evaluate the efficacy and safety of AZI in childhood asthma. Methods We searched seven databases to include randomized controlled trials (RCTs) of AZI in the treatment of childhood asthma. Four reviewers independently screened the records. Risk of Bias 2 was used to assess the quality of RCTs. Risk ratios with 95% confidence interval (CI) from dichotomous outcomes, and mean difference (MD) with 95% CI from continuous outcomes were pooled. Results We included 19 eligible reports from 17 studies. The prevalence of exacerbations in AZI + budesonide (BUD) + β2 agonist (BA) group was lower than BUD + BA group (four [13%] vs. 19 [63%], p < 0.05) in 6– 14 years old children with chronic persistent asthma. AZI plus antiasthma drugs (AADs) could improve the posttreatment childhood asthma control test score (MD = 2.97; 95% CI, 2.39–3.54) compared to AADs alone in children with chronic persistent asthma. AZI plus AADs could improve posttreatment forced expiratory volume in 1 s of predicted value/forced vital capacity % (MD = 10.24%; 95% CI, 6.44%–14.03%) and posttreatment peak expiratory flow % of predicted value (MD = 7.00%; 95% CI, 2.53%–11.47%) compared to AADs alone in children with chronic persistent asthma. The most common adverse reactions of AZI combined with other drugs were gastrointestinal reactions. Conclusions AZI may be beneficial in improving some clinical symptoms and lung functions in older asthma children (over 6 years old) with persistent asthma. But it still requires further research.

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Background: Evidence suggests controversial results based on the antibacterial and anti-inflammatory effects of azithromycin (AZI) in the treatment of childhood asthma. This study was to further evaluate the efficacy and safety of AZI in childhood asthma. Methods: We searched PubMed, Embase (via Ovid), Cochrane Library, China National Knowledge Infrastructure, Chinese Scientific Journals database, WANFANG, and Chinese Biomedical Literature database from inception to November 11, 2020. Randomized controlled trials (RCTs) of AZI versus placebo or one positive control drug, AZI plus anti-asthma drugs (AADs) versus the same AADs, and AZI plus AADs versus placebo or one positive control drug plus the same AADs were included. Primary outcomes were number of exacerbations (NoE); score of clinical tools to assess asthma control after treatment; number of days to relieve symptoms with β2 agonist (DBA); post-treatment lung function indicators, including FEV1% of predicted value (pFEV1%), FVC% of predicted value (pFVC%), FEV1/FVC% of predicted value (pFEV1/FVC%), and PEF% of predicted value (pPEF%). Secondary outcomes were post-treatment fractional exhaled nitric oxide (FENO); post-treatment eosinophil counts in sputum (sEOS) or blood (bEOS); author self-reported outcomes related to asthma (AROs); and adverse events (AEs). Results: 61 eligible reports from 59 studies were finally included. AZI plus AADs shows no statistically significant difference in NoE (RR = 0.49; 95% CI, 0.07 – 3.26; P = 0.05) and sEOS (MD = -1.13%; 95% CI, -3.54% – 1.29%; P = 0.36) compared to AADs alone. The post-treatment C-ACT score was improved after AZI plus salmeterol and fluticasone (SF) treatment compared to SF alone (MD = 2.97; 95% CI, 2.39 – 3.54; P < 0.001). Results from three studies which could not be meta-analyzed showed that AZI may reduce DBA compared to placebo. AZI combined with AADs could improve post-treatment pFEV1% (AZI + glucocorticoid (GC) vs GC: MD = 6.92%; 95% CI, 1.47% – 12.37%; P = 0.01. AZI + leukotriene receptor antagonist (LTRA) vs LTRA: MD = 24.88%; 95% CI, 21.47% – 28.29%; P < 0.001. AZI + GC + BA vs GC + BA: MD = 12.40%; 95% CI, 9.72% – 15.08%; P < 0.001), pFEV1/FVC% (AZI + GC vs GC: MD = 10.24%; 95% CI, 6.44% – 14.03%; P < 0.001. AZI + GC + BA vs GC + BA: MD = 9.05%; 95% CI, 5.66% – 12.44%; P < 0.001. AZI + BA vs LTRA + BA: MD = 14.48%; 95% CI, 11.84% – 17.12%; P < 0.001), and pPEF% (MD = 7.00%, 95% CI, 2.53% – 11.47%; P = 0.002), but not improve pFVC% (MD = -10.37; 95% CI, -20.86% – 0.12%; P = 0.05), compared to AADs alone. Post-treatment bEOS was significantly higher in the AZI group than in the traditional Chinese medicine compound granules group (MD = 0.07×109/L; 95% CI, 0.05×109 – 0.09×109; P < 0.001). No statistically significant difference in bEOS after treatment with AZI plus montelukast (MON) and loratadine (LOR) compared to MON and LOR (MD = 0.03×109/L; 95% CI, -0.06×109 – 0.12×109; P = 0.50). Meanwhile, AZI combined with AADs did not increase AEs (RR = 0.76; 95% CI, 0.51 – 1.13; P = 0.17). Conclusions: AZI was beneficial in improving some clinical symptoms and lung functions in childhood asthma. AZI did not increase AEs when combined with AADs.