Diabetic macular edema (DME) is the most threatening complication of diabetic retinopathy that affects visual function, which is characterized by intractability and recurrent attacks. Currently, the clinical routine treatments for DME mainly include intravitreal injection, grid laser photocoagulation in the macular area, subthreshold micropulse laser, periocular corticosteroid injection, and vitrectomy. Although conventional treatments are effective for some patients, persistent, refractory, and recurrent DME remains a clinical challenge that needs to be urgently addressed. In recent years, clinical studies have found that certain combination therapies are superior to monotherapy, which can not only restore the anatomical structure of the macular area and effectively reduce macular edema but also improve visual function to some extent while reducing the number of treatments and the overall cost. This makes up for the shortcomings of single treatment modalities and is highly anticipated in the clinical setting. However, the application of combination therapy in clinical practice is relatively short, and its safety and long-term effectiveness need further exploration. Currently, new drugs, new formulations, and new therapeutic targets are still under research and development to address different mechanisms of DME occurrence and development, such as anti-vascular endothelial growth factor agents designed to anchor repetitive sequence proteins with stronger inhibition of vascular leakage, multiple growth factor inhibitors, anti-inflammatory agents, and stem cell therapy. With the continuous improvement of the combination application of existing drugs and treatments and the development of new drugs and treatment technologies, personalized treatment for DME will become possible.
ObjectiveTo analyze the risk factors for acute kidney injury (AKI) after off-pump coronary artery bypass grafting (OPCABG). Methods The PubMed, EMbase, The Cochrane Library, Web of Science, Wanfang data, CBM, VIP, CNKI were searched by computer for researches on risk factors associated with the development of AKI after OPCABG from the inception to March 2022. The meta-analysis was performed using RevMan 5.4 software. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of included studies.ResultsA total of 18 researches were included, involving 9 risk factors. The NOS score of all included studies was≥6 points. Meta-analysis results showed that age [OR=1.03, 95%CI (1.01, 1.06), P=0.020], body mass index (BMI) [OR=1.10, 95%CI (1.05, 1.15), P<0.001], history of hypertension [OR=1.45, 95%CI (1.27, 1.66), P<0.001], history of diabetes [OR=1.50, 95%CI (1.33, 1.70), P<0.001], preoperative serum creatinine level [OR=2.05, 95%CI (1.27, 3.32), P=0.003], low left ventricular ejection fraction [OR=4.51, 95%CI (1.39, 14.65), P=0.010], preoperative coronary angiography within a short period of time [OR=2.10, 95%CI (1.52, 2.91), P<0.001], perioperative implantation of intra-aortic balloon pump [OR=3.42, 95%CI (2.26, 5.16), P<0.001], perioperative blood transfusion [OR=2.00, 95%CI (1.51, 2.65), P<0.001] were risk factors for AKI after OPCABG. ConclusionAge, BMI, history of hypertension, history of diabetes, preoperative serum creatinine level, low left ventricular ejection fraction, preoperative coronary angiography within a short period of time, perioperative implantation of intra-aortic balloon pump, perioperative blood transfusion are risk factors for AKI after OPCABG. Medical staff should focus on monitoring the above risk factors and early identifying, in order to prevent or delay the onset of postoperative AKI and promote early recovery of patients.
Wearable devices are used in the new design of the maternal health care system to detect electrocardiogram and oxygen saturation signal while smart terminals are used to achieve assessments and input maternal clinical information. All the results combined with biochemical analysis from hospital are uploaded to cloud server by mobile Internet. Machine learning algorithms are used for data mining of all information of subjects. This system can achieve the assessment and care of maternal physical health as well as mental health. Moreover, the system can send the results and health guidance to smart terminals.
A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.