M. Liao

Background:The timely management of large-scale wounds and the regeneration of skin appendages constitute major clinical issues.The production of high-precision and customizable artificial skin via 3D bioprinting offers a feasible means to surmount the predicament, within which the selection of bioactive materials and seed cells is critical.This study is aimed at employing skin stem cells and multicomponent hydrogels to prefabricate artificial skin through 3D bioprinting, which enables the regeneration of skin and its appendages.Methods and Results: We employed gelatin methacrylate (GelMA) and hyaluronic acid methacrylate (HAMA) as bioactive materials, in conjunction with epidermal stem cells (Epi-SCs) and skin-derived precursors (SKPs), to fabricate artificial skin utilizing 3D bioprinting.The photosensitive multicomponent hydrogel, comprising 5% GelMA and 0.5% HAMA, demonstrated excellent printability, suitable solubility and swelling rates, as well as stable mechanical properties.Moreover, this hydrogel exhibited exceptional biocompatibility, effectively facilitating the proliferation of SKPs while maintaining the cellular characteristics of both SKPs and Epi-SCs.The transplantation of this artificial skin into cutaneous wounds in nude mice led to complete wound healing and functional tissue regeneration.The regenerated tissue comprised epidermis, dermis, hair follicles, blood vessels, and sebaceous glands, closely resembling native skin.Remarkably, the artificial skin demonstrated sustained tissue regeneration capacity even after 12 h of in vitro culture, facilitating comprehensive functional skin regeneration.Conclusions: Our research presented a skin repair strategy for prefabricated cell-loaded artificial skin, thereby successfully facilitating the regeneration of the epidermis, dermis, hair follicles, blood vessels, and sebaceous glands within the wound.

For patients with small cell lung cancer (SCLC) in their early stages (TNM I, II), surgery for cure was used to eliminate the primary tumour and its regional lymph-nodes followed by intermittent chemotherapy and radiotherapy within the first six postoperative months. After the pathohistological examination of the operation-specimen a two-arm-randomization was performed: standard chemotherapy (1000 mg/m2 cyclophosphamide, 50 mg/m2 doxorubicin, 1.4 mg/m2 vincristine) compared with sequential chemotherapy using three different drug-combinations (A: 1500 mg/m2 cyclophosphamide, 100 mg/m2 lomustine, 15 mg/m2 methotrexate; B: 1000 mg/m2 cyclophosphamide, 40 mg/m2 doxorubicin, 1 mg/m2 vincristine; C: 5 x 1.6 g/m2 ifosfamide plus mesna, 5 x 120 mg/m2 etopside). Thereafter disease-free patients only received prophylactic cranial irradiation (PCI: administering 3600 TD Gy/18 fractions) according to the protocols of the International Society of Chemotherapy Studies I and II. Preliminary evaluations in March 1990 of 170 patients from 24 cooperating departments for thoracic surgery showed that the projected life-table four-year-survival rate of 63 patients with SCLC at pTNM-stage I was 61%, of 54 patients at pTNM-stage II was 35%, of 13 patients at stage pT3, 4 NO, 1 MO was 59% and of 40 patients at stage pT N2 MO was 35%. The indication for surgery is emphasized for pTNM-stages I + II. For N2-lesions surgery would not be recommended in general, but the survival rate seems to indicate that this treatment was not detrimental, being rather more favourable compared with chemotherapy or radiotherapy alone. The continuation and enlargement of these studies seem not only justified, but emphatically indicated.(ABSTRACT TRUNCATED AT 250 WORDS)

The advancement of large Vision-Language-Action (VLA) models has significantly improved robotic manipulation in terms of language-guided task execution and generalization to unseen scenarios. While existing VLAs adapted from pretrained large Vision-Language-Models (VLM) have demonstrated promising generalizability, their task performance is still unsatisfactory as indicated by the low tasks success rates in different environments. In this paper, we present a new advanced VLA architecture derived from VLM. Unlike previous works that directly repurpose VLM for action prediction by simple action quantization, we propose a omponentized VLA architecture that has a specialized action module conditioned on VLM output. We systematically study the design of the action module and demonstrates the strong performance enhancement with diffusion action transformers for action sequence modeling, as well as their favorable scaling behaviors. We also conduct comprehensive experiments and ablation studies to evaluate the efficacy of our models with varied designs. The evaluation on 5 robot embodiments in simulation and real work shows that our model not only significantly surpasses existing VLAs in task performance and but also exhibits remarkable adaptation to new robots and generalization to unseen objects and backgrounds. It exceeds the average success rates of OpenVLA which has similar model size (7B) with ours by over 35% in simulated evaluation and 55% in real robot experiments. It also outperforms the large RT-2-X model (55B) by 18% absolute success rates in simulation. Code and models can be found on our project page (https://cogact.github.io/).