PurposeWith interest in modern construction methods and new technologies on the rise, construction companies globally are increasingly looking at how to embrace new ideas and engage with new approaches to do things better. A significant amount of work has been carried out investigating the use of individual technologies in the construction sector. However, there is no holistic understanding of the new and emerging technologies that have had proven benefits for construction projects. To fill this gap, this paper aims to provide a landscape of technologies that have been implemented in the construction industry and the benefits associated with their implementation.Design/methodology/approachA systematic review approach and PRISMA guidelines were used. A total of 175 articles published between 2001 and 2020 were identified and thoroughly reviewed.FindingsThe results show that a total of 26 technologies were identified from the literature, and these can be categorised into five groups in terms of their functionality in construction process, namely: (1) data acquisition, (2) analytics, (3) visualisation, (4) communication and (5) design and construction automation. Digital technologies, especially for data acquisition and visualisation, generally appear to underpin and enable innovation in many aspects of construction. Improvements in work efficiency, health and safety, productivity, quality and sustainability have been cited as being the primary benefits of using these technologies. Of these, building information modelling (BIM) appears to be the single most commonly used technology thus far. With the development of computer technology, BIM has constantly been used in combination with other technologies/tools such as unmanned aerial vehicles/systems (unmanned aerial vehicle (UAV)/UAS), geographic information systems (GIS), light detection and ranging (LiDAR) and multidimensional modelling to realise a specifically defined benefit.Practical implicationsThe findings from this review would help construction practitioners identify the types of technologies that can be implemented in different stages of construction projects to achieve desired outcomes, and thus, make appropriate decisions on technology investment and adoption. This review also suggests that to reap the full potential that these technologies offer, aside from construction companies changing their culture and business models, corresponding changes in the construction sector’s operating systems related to building regulation, education and training, as well as contracting and procurement are required.Originality/valueThis paper undertakes a comprehensive systematic review of studies on technology implementation in the construction sector published between 2001 and 2020. It is the first attempt internationally to provide a holistic picture of technologies that have been studied and implemented in construction projects.

LBA3001 Background: Chimeric antigen receptor engineered T cell (CAR-T) is a novel immunotherapeutic approach for cancer treatment and has been clinically validated in the treatment of acute lymphoblastic leukemia (ALL). Here we report an encouraging breakthrough of treating multiple myeloma (MM) using a CAR-T designated LCAR-B38M CAR-T, which targets principally BCMA. Methods: A single arm clinical trial was conducted to assess safety and efficacy of this approach. A total of 19 patients with refractory/relapsed multiple myeloma were included in the trial. The median number of infused cells was 4.7 (0.6 ~ 7.0) × 10e6/ kg. The median follow-up times was 208 (62 ~ 321) days. Results: Among the 19 patients who completed the infusion, 7 patients were monitored for a period of more than 6 months. Six out of the 7 achieved complete remission (CR) and minimal residual disease (MRD)-negative status. The 12 patients who were followed up for less than 6 months met near CR criteria of modified EBMT criteria for various degrees of positive immunofixation. All these effects were observed with a progressive decrease of M-protein and thus expected to eventually meet CR criteria. In the most recent follow-up examination, all 18 survived patients were determined to be free of myeloma-related biochemical and hematologic abnormalities. One of the most common adverse event of CAR-T therapy is acute cytokine release syndrome (CRS). This was observed in 14 (74%) patients who received treatment. Among these 14 patients there were 9 cases of grade 1, 2 cases of grade 2, 1 case of grade 3, and 1 case of grade 4 patient who recovered after treatments. Conclusions: A 100% objective response rate (ORR) to LCAR-B38M CAR-T cells was observed in refractory/relapsed myeloma patients. 18 out of 19 (95%) patients reached CR or near CR status without a single event of relapse in a median follow-up of 6 months. The majority (14) of the patients experienced mild or manageable CRS, and the rest (5) were even free of diagnosable CRS. Based on the encouraging safety and efficacy outcomes, we believe that our LCAR-B38M CAR-T cell therapy is an innovative and highly effective treatment for multiple myeloma.

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

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