This article presents an improved two-flap method for microtia reconstruction. In the first stage of this method, a tissue expander is inserted in the mastoid region through a subfascial pocket, after which the overlying fascia and skin are expanded simultaneously with saline infusion for about 5 months. In the second stage, the expanded fascial and skin layers are split and prepared as anteriorly based skin and fascial flaps defined by their vascularity. An erect, three-dimensional, contouraccentuated ear framework fabricated with autogenous rib cartilage is inserted between the two flaps. The anteroauricular surface of the framework is draped with the thin, expanded skin, and the postauricular surface is draped with the thin, expanded fascia and overlying grafted skin. In the third stage, remnant auricular cartilage is removed and the crus helicis, tragus, intertragic notch, conchal floor, and a hollow mimicking the external auditory meatus are shaped. In this study, 146 microtias were reconstructed consecutively using the improved two-flap method. The final results were promising—major complications were minimal and most patients showed consistently favorable aesthetic results. This method married a two-flap procedure with a gradual tissue expansion, conveniently exploiting the advantages of both methods, but without the disadvantages. (Plast. Reconstr. Surg. 106: 1473, 2000.)

This article presents an updated review of our experience with 122 temporoparietal fascial flaps, which were used for coverage of fabricated autogenous cartilage frameworks in total auricular reconstructions. Our indications for use of the temporoparietal fascial flap are presented. Partial flap necrosis occurred in 5 cases, total necrosis in 2 of 14 microsurgically transplanted cases, cartilage infection in 2 cases, and paralysis of the frontal branch of facial nerve in 1 case. Prospective observations of vascular anatomy were carried out in the last 93 temporoparietal fascial flaps during flap elevations. Only 59 flaps (63.4 percent) showed a typical pattern, distributed mainly by the superficial temporal artery and vein. Others (36.6 percent) were distributed mainly by various combinations of the posterior auricular artery or vein, occipital artery or vein, diploic vein, and the superficial temporal artery or vein. At the upper margin of the imaginary reconstructed auricle, the mean diameters of the artery and vein were 1.7 mm and 2.2 mm, respectively. There were no significant differences of vascular patterns and their diameters between the temporoparietal fascial flap of microtia sides and of nonmicrotia sides (sides with acquired ear deformities or freeflap donor sides). We are presenting our technical evolution in using the temporoparietal fascial flap for total auricular reconstruction with the goal of reducing surgical complications and improving aesthetic results. (Plast. Reconstr. Surg. 104: 1295, 1999.)

FUNDC1 regulates mitochondrial morphology, localization, and function to support cancer cell proliferation.

Background: COVID-19, which is accompanied by acute respiratory distress, multiple organ failure, and death, has spread worldwide much faster than previously thought. However, at present, it has limited treatments. Objective: To overcome this issue, we developed an artificial intelligence (AI) model of COVID-19, named EDRnet (ensemble learning model based on deep neural network and random forest models), to predict in-hospital mortality using a routine blood sample at the time of hospital admission. Methods: We selected 28 blood biomarkers and used the age and gender information of patients as model inputs. To improve the mortality prediction, we adopted an ensemble approach combining deep neural network and random forest models. We trained our model with a database of blood samples from 361 COVID-19 patients in Wuhan, China, and applied it to 106 COVID-19 patients in three Korean medical institutions. Results: In the testing data sets, EDRnet provided high sensitivity (100%), specificity (91%), and accuracy (92%). To extend the number of patient data points, we developed a web application (BeatCOVID19) where anyone can access the model to predict mortality and can register his or her own blood laboratory results. Conclusions: Our new AI model, EDRnet, accurately predicts the mortality rate for COVID-19. It is publicly available and aims to help health care providers fight COVID-19 and improve patients’ outcomes.

Over the past few decades, basic studies aimed at curing patients with cancer have been constantly evolving. A myriad of mechanistic studies on physiological changes and related factors in tumor growth and metastasis have been reported. Recently, several studies have been considerate to how tumors adapt to unfavorable environments, such as glucose deprivation, oxidative stress, hypoxic conditions, and immune responses. Tumors attempt to adapt to unfavorable environments with genetic or non-genetic changes, the alteration of metabolic signals, or the reconfiguration of their environment through migration to other organs. One of the distinct features in solid tumors is heterogeneity because their environments vary due to the characteristics of colony growth. For this reason, researchers are paying attention to the communication between growing tumors and neighboring environments, including stromal cells, immune cells, fibroblasts, and secreted molecules, such as proteins and RNAs. During cancer survival and progression, tumor cells undergo phenotype and molecular changes collectively referred to as cellular plasticity, which result from microenvironment signals, genetics and epigenetic alterations thereby contributing to tumor heterogeneity and therapy response. In this review, we herein discuss the adaptation process of tumors to adverse environments via communication with neighboring cells for overcoming unfavorable growth conditions. Understanding the physiology of these tumors and their communication with the tumor environment can help to develop promising tumor treatment strategies.

Ri Zhe Zhu, Bing Si Li, Shang Shang Gao, Jae Ho Seo, and Byung-Min Choi. Korean J Physiol Pharmacol 2021;25:297-305. https://doi.org/10.4196/kjpp.2021.25.4.297