Fibrous materials have garnered much interest in the field of biomedical engineering due to their high surface-area-to-volume ratio, porosity, and tunability. Specifically, in the field of tissue engineering, fiber meshes have been used to create biomimetic nanostructures that allow for cell attachment, migration, and proliferation, to promote tissue regeneration and wound healing, as well as controllable drug delivery. In addition to the properties of conventional, synthetic polymer fibers, fibers made from natural polymers, such as proteins, can exhibit enhanced biocompatibility, bioactivity, and biodegradability. Of these proteins, keratin, collagen, silk, elastin, zein, and soy are some the most common used in fiber fabrication. The specific capabilities of these materials have been shown to vary based on their physical properties, as well as their fabrication method. To date, such fabrication methods include electrospinning, wet/dry jet spinning, dry spinning, centrifugal spinning, solution blowing, self-assembly, phase separation, and drawing. This review serves to provide a basic knowledge of these commonly utilized proteins and methods, as well as the fabricated fibers’ applications in biomedical research.

Risk factors of mydelodysplastic syndromes (MDS) remain largely unknown. We conducted a hospital-based case-control study consisting of 403 newly diagnosed MDS patients according to World Health Organization classification and 806 individually gender and age-matched patient controls from 27 major hospitals in Shanghai, China, to examine relation of lifestyle, environmental, and occupational factors to risk of MDS. The study showed that all MDS (all subtypes combined) risk factors included anti tuberculosis drugs [odds ratio (OR) IntroductionMyelodysplastic syndromes (MDS) represent a heterogeneous group of neoplastic clonal stem cell disorders characterized by clinical presentations of anemia, thrombocytopenia, and leucopenia. MDS may be categorized into subtypes according to histological, immunological, and genetic characteristics. MDS was usually diagnosed by French-American-British (FAB) classification with subtypes including refractory anemia (RA), RA with ringed sideroblasts (RARS), RA with excess of blasts (RAEB), RAEB in transformation (RAEB-T), and chronic myelomonocytic leukemia (CMML) [1]. Since its publication in 2001, World Health Organization (WHO) classification for MDS has become widely adopted [2]. In the WHO MDS system, blast cutoff is less than 20% compared to 30% in FAB system. Additional WHO MDS subtypes include refractory cytopenia with multiple dysplasia (RCMD), MDS with isolated del(5q), and MDS unclassifiable (MDS-u) and the WHO MDS system does not include CMML [3].Secondary MDS is usually resulted from radiation and chemotherapy. Little is known about the etiology of primary or de novo MDS. Most previous studies on MDS risk factors focused on FAB MDS [4][5][6]. Here, we report a large hospital-based case-control study of 403 WHO MDS cases and 806 age and sex-matched controls in a Chinese population to assess effects of lifestyle, environmental, and occupational factors on MDS development.

BackgroundPreoperative differentiation of borderline from malignant epithelial ovarian tumors (BEOT from MEOT) can impact surgical management. MRI has improved this assessment but subjective interpretation by radiologists may lead to inconsistent results.PurposeTo develop and validate an objective MRI‐based machine‐learning (ML) assessment model for differentiating BEOT from MEOT, and compare the performance against radiologists' interpretation.Study TypeRetrospective study of eight clinical centers.PopulationIn all, 501 women with histopathologically‐confirmed BEOT (n = 165) or MEOT (n = 336) from 2010 to 2018 were enrolled. Three cohorts were constructed: a training cohort (n = 250), an internal validation cohort (n = 92), and an external validation cohort (n = 159).Field Strength/SequencePreoperative MRI within 2 weeks of surgery. Single‐ and multiparameter (MP) machine‐learning assessment models were built utilizing the following four MRI sequences: T2‐weighted imaging (T2WI), fat saturation (FS), diffusion‐weighted imaging (DWI), apparent diffusion coefficient (ADC), and contrast‐enhanced (CE)‐T1WI.AssessmentDiagnostic performance of the models was assessed for both whole tumor (WT) and solid tumor (ST) components. Assessment of the performance of the model in discriminating BEOT vs. early‐stage MEOT was made. Six radiologists of varying experience also interpreted the MR images.Statistical TestsMann–Whitney U‐test: significance of the clinical characteristics; chi‐square test: difference of label; DeLong test: difference of receiver operating characteristic (ROC).ResultsThe MP‐ST model performed better than the MP‐WT model for both the internal validation cohort (area under the curve [AUC] = 0.932 vs. 0.917) and external validation cohort (AUC = 0.902 vs. 0.767). The model showed capability in discriminating BEOT vs. early‐stage MEOT, with AUCs of 0.909 and 0.920, respectively. Radiologist performance was considerably poorer than both the internal (mean AUC = 0.792; range, 0.679–0.924) and external (mean AUC = 0.797; range, 0.744–0.867) validation cohorts.Data ConclusionPerformance of the MRI‐based ML model was robust and superior to subjective assessment of radiologists. If our approach can be implemented in clinical practice, improved preoperative prediction could potentially lead to preserved ovarian function and fertility for some women.Level of EvidenceLevel 4.Technical EfficacyStage 2. J. Magn. Reson. Imaging 2020;52:897–904.