Introduction of metals as biomaterials has been known for a long time. In the early development, sufficient strength and suitable mechanical properties were the main considerations for metal implants. With the development of new generations of biomaterials, the concepts of bioactive and biodegradable materials were proposed. Biological function design is very import for metal implants in biomedical applications. Three crucial design criteria are summarized for developing metal implants: (1) mechanical properties that mimic the host tissues; (2) sufficient bioactivities to form bio-bonding between implants and surrounding tissues; and (3) a degradation rate that matches tissue regeneration and biodegradability. This article reviews the development of metal implants and their applications in biomedical engineering. Development trends and future perspectives of metallic biomaterials are also discussed.

As machine learning is becoming the fashion in disease prediction while no prediction model has performed very efficiently and accurately on predicting pregnancy diseases up to now, it’s necessary to compare several common machine learning methods’ performance on pregnancy diseases prediction and select out the best one. The data of two common pregnancy complications, pregnancy-induced hypertension (PIH) and Intrahepatic cholestasis of pregnancy (ICP), based on various maternal characteristics measured in patients’ routine blood examination in 10–19 weeks of gestation are considered to be suitable to be learned. This is a retrospective study of 320 healthy pregnancies in 10–19 weeks, with 149 patients who subsequently developed PIH and 250 patients who subsequently developed ICP. Nine machine learning methods were used to predict PIH and ICP and their performance was compared via 8 evaluation indexes. Finally, the light Gradient Boosting Machine (lightGBM) is considered to be the best method to predict gestational diseases.

Our objective was to develop a first 19 weeks risk prediction model with several potential gestational diabetes mellitus (GDM) predictors including hepatic and renal and coagulation function measur...

Airway stent insertion is important for patients with airway stenosis. Currently, the most widely used airway stents in clinical procedures are silicone and metallic stents, which offer patients effective treatment. However, these stents composed of permanent materials need to be removed, subjecting patients to invasive manipulation once more. As a result, there is a growing demand for biodegradable airway stents. Biodegradable materials for airway stents are now available in two types: biodegradable polymers and biodegradable alloys. Polymers that include poly (l-lactic acid), poly (D, l-lactide-co-glycolide), polycaprolactone, and polydioxanone are the ultimate metabolites which are generally carbon dioxide and water. Magnesium alloys are the most often utilized metal biodegradable materials for airway stents. The stent’s mechanical properties and rate of degradation vary as a result of the different materials, cutting techniques, and structural configurations. We summarized the information above from recent studies on biodegradable airway stents conducted in both animals and humans. There is great potential for clinical applications for biodegradable airway stents. They avoid damage to the trachea during removal and reduce complications to some extent. However, several significant technical difficulties slow down the development of biodegradable airway stents. The efficacy and safety of different biodegradable airway stents still need to be investigated and proved.

Background: Maintenance hemodialysis (MHD) patients often suffer from sarcopenia, which is strongly associated with their long-term mortality. The diagnosis and treatment of sarcopenia, especially possible sarcopenia for MHD patients are of great importance. This study aims to use machine learning and medical data to develop two simple sarcopenia identification assistant tools for MHD patients and focuses on sex specificity. Methods: Data were retrospectively collected from patients undergoing MHD and included patients’ basic information, body measurement results and laboratory findings. The 2019 consensus update by Asian working group for sarcopenia was used to assess whether a MHD patient had sarcopenia. Finally, 140 male (58 with possible sarcopenia or sarcopenia) and 102 female (65 with possible sarcopenia or sarcopenia) patients’ data were collected. Participants were divided into sarcopenia and control groups for each sex to develop binary classifiers. After statistical analysis and feature selection, stratified shuffle split and Synthetic Minority Oversampling Technique were conducted and voting classifiers were developed. Results: After eliminating handgrip strength, 6-m walk, and skeletal muscle index, the best three features for sarcopenia identification of male patients are age, fasting blood glucose, and parathyroid hormone. Meanwhile, age, arm without vascular access, total bilirubin, and post-dialysis creatinine are the best four features for females. After abandoning models with overfitting or bad performance, voting classifiers achieved good sarcopenia classification performance for both sexes (For males: sensitivity: 77.50% ± 11.21%, specificity: 83.13% ± 9.70%, F1 score: 77.32% ± 5.36%, the area under the receiver operating characteristic curves (AUC): 87.40% ± 4.41%. For females: sensitivity: 76.15% ± 13.95%, specificity: 71.25% ± 15.86%, F1 score: 78.04% ± 8.85%, AUC: 77.69% ± 7.92%). Conclusions: Two simple sex-specific sarcopenia identification tools for MHD patients were developed. They performed well on the case finding of sarcopenia, especially possible sarcopenia.