Mitochondrial stress is relayed to the cytosol by an OMA1-DELE1-HRI pathway.

In mammalian cells, mitochondrial dysfunction triggers the integrated stress response, in which the phosphorylation of eukaryotic translation initiation factor 2α (eIF2α) results in the induction of the transcription factor ATF41-3. However, how mitochondrial stress is relayed to ATF4 is unknown. Here we show that HRI is the eIF2α kinase that is necessary and sufficient for this relay. In a genome-wide CRISPR interference screen, we identified factors upstream of HRI: OMA1, a mitochondrial stress-activated protease; and DELE1, a little-characterized protein that we found was associated with the inner mitochondrial membrane. Mitochondrial stress stimulates OMA1-dependent cleavage of DELE1 and leads to the accumulation of DELE1 in the cytosol, where it interacts with HRI and activates the eIF2α kinase activity of HRI. In addition, DELE1 is required for ATF4 translation downstream of eIF2α phosphorylation. Blockade of the OMA1-DELE1-HRI pathway triggers an alternative response in which specific molecular chaperones are induced. The OMA1-DELE1-HRI pathway therefore represents a potential therapeutic target that could enable fine-tuning of the integrated stress response for beneficial outcomes in diseases that involve mitochondrial dysfunction.

TMEM16A controls EGF-induced calcium signaling implicated in pancreatic cancer prognosis.

Pancreatic cancer typically spreads rapidly and has poor survival rates. Here, we report that the calcium-activated chloride channel TMEM16A is a biomarker for pancreatic cancer with a poor prognosis. TMEM16A is up-regulated in 75% of cases of pancreatic cancer and high levels of TMEM16A expression are correlated with low patient survival probability. TMEM16A up-regulation is associated with the ligand-dependent EGFR signaling pathway. In vitro, TMEM16A is required for EGF-induced store-operated calcium entry essential for pancreatic cancer cell migration. TMEM16A also has a profound impact on phosphoproteome remodeling upon EGF stimulation. Moreover, molecular actors identified in this TMEM16A-dependent EGFR-induced calcium signaling pathway form a gene set that makes it possible not only to distinguish neuro-endocrine tumors from other forms of pancreatic cancer, but also to subdivide the latter into three clusters with distinct genetic profiles that could reflect their molecular underpinning.

A New Photographic Reproduction Method Based on Feature Fusion and Virtual Combined Histogram Equalization.

A desirable photographic reproduction method should have the ability to compress high-dynamic-range images to low-dynamic-range displays that faithfully preserve all visual information. However, during the compression process, most reproduction methods face challenges in striking a balance between maintaining global contrast and retaining majority of local details in a real-world scene. To address this problem, this study proposes a new photographic reproduction method that can smoothly take global and local features into account. First, a highlight/shadow region detection scheme is used to obtain prior information to generate a weight map. Second, a mutually hybrid histogram analysis is performed to extract global/local features in parallel. Third, we propose a feature fusion scheme to construct the virtual combined histogram, which is achieved by adaptively fusing global/local features through the use of Gaussian mixtures according to the weight map. Finally, the virtual combined histogram is used to formulate the pixel-wise mapping function. As both global and local features are simultaneously considered, the output image has a natural and visually pleasing appearance. The experimental results demonstrated the effectiveness of the proposed method and the superiority over other seven state-of-the-art methods.

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management.

Electricity is a vital resource for various human activities, supporting customers' lifestyles in today's modern technologically driven society. Effective demand-side management (DSM) can alleviate ever-increasing electricity demands that arise from customers in downstream sectors of a smart grid. Compared with the traditional means of energy management systems, non-intrusive appliance load monitoring (NIALM) monitors relevant electrical appliances in a non-intrusive manner. Fog (edge) computing addresses the need to capture, process and analyze data generated and gathered by Internet of Things (IoT) end devices, and is an advanced IoT paradigm for applications in which resources, such as computing capability, of a central data center acted as cloud computing are placed at the edge of the network. The literature leaves NIALM developed over fog-cloud computing and conducted as part of a home energy management system (HEMS). In this study, a Smart HEMS prototype based on Tridium's Niagara Framework® has been established over fog (edge)-cloud computing, where NIALM as an IoT application in energy management has also been investigated in the framework. The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive deployment of plug-load power meters (smart plugs), where a two-stage NIALM approach is completed. The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart building for residential DSM. As demonstrated and reported in this study, the established SHEMS prototype using the investigated two-stage NIALM approach is feasible and usable.

A Machine Learning-Based Fall Risk Assessment Model for Inpatients.

Falls are one of the most common accidents among inpatients and may result in extended hospitalization and increased medical costs. Constructing a highly accurate fall prediction model could effectively reduce the rate of patient falls, further reducing unnecessary medical costs and patient injury. This study applied data mining techniques on a hospital's electronic medical records database comprising a nursing information system to construct inpatient-fall-prediction models for use during various stages of inpatient care. The inpatient data were collected from 15 inpatient wards. To develop timely and effective fall prediction models for inpatients, we retrieved the data of multiple-time assessment variables at four points during hospitalization. This study used various supervised machine learning algorithms to build classification models. Four supervised learning and two classifier ensemble techniques were selected for model development. The results indicated that Bagging+RF classifiers yielded optimal prediction performance at all four points during hospitalization. This study suggests that nursing personnel should be aware of patients' risk factors based on comprehensive fall risk assessment and provide patients with individualized fall prevention interventions to reduce inpatient fall rates.

A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning.

Non-intrusive load monitoring (NILM) is a cost-effective approach that electrical appliances are identified from aggregated whole-field electrical signals, according to their extracted electrical characteristics, with no need to intrusively deploy smart power meters (power plugs) installed for individual monitored electrical appliances in a practical field of interest. This work addresses NILM by a parallel Genetic Algorithm (GA)-embodied Artificial Neural Network (ANN) for Demand-Side Management (DSM) in a smart home. An ANN's performance in terms of classification accuracy depends on its training algorithm. Additionally, training an ANN/deep NN learning from massive training samples is extremely computationally intensive. Therefore, in this work, a parallel GA has been conducted and used to integrate meta-heuristics (evolutionary computing) with an ANN (neurocomputing) considering its evolution in a parallel execution relating to load disaggregation in a Home Energy Management System (HEMS) deployed in a real residential field. The parallel GA that involves iterations to excessively cost its execution time for evolving an ANN learning model from massive training samples to NILM in the HEMS and works in a divide-and-conquer manner that can exploit massively parallel computing for evolving an ANN and, thus, reduce execution time drastically. This work confirms the feasibility and effectiveness of the parallel GA-embodied ANN applied to NILM in the HEMS for DSM.

Effects of health-information-based diabetes shared care program participation on preventable hospitalizations in Taiwan.

BACKGROUND: Taiwan's Diabetes Shared Care Program has been implemented since 2012, and the health information system plays a vital role in supporting most services of this program. However, little is known regarding the effectiveness of this information-based program. Therefore, this study investigated the effects of the participation of the Diabetes Shared Care Program on preventable hospitalizations. METHODS: This longitudinal study examined the data of health-care claims from 2011 to 2014 obtained from the diabetes mellitus health database. Patients with diabetes aged ≥18 years were included. Preventable hospitalizations were identified on the basis of prevention quality indicators developed for administrative data by the US Agency for Healthcare Research and Quality. A multilevel logistic regression was performed to examine the effects of the participation of the Diabetes Shared Care Program on preventable hospitalizations after adjustment for other variables. Analyses were conducted in late 2018. RESULTS: A medium level of participation (p = 0.05), age between 40 and 64 years(p < 0.0001), and absence of a catastrophic illness(p < 0.0001) were associated with a lower probability of having a preventable hospitalization. Male sex(p < 0.0001), age ≥ 65 years(p = 0.0203), low income level(p < 0.0001), living in the Southern division(p = 0.0106), and presence of many comorbidities(p < 0.0001) were associated with a higher probability of having a preventable hospitalization after adjustment for characteristics at the individual and county levels. CONCLUSIONS: The health information system records patients' medical history, monitors quality of care, schedules patient follow-ups, and reminds case managers to provide timely health education. This health-information-based Diabetes Shared Care Program is associated with better quality care of ambulatory, so it should be promoted on a broader scale.

A Smart Autonomous Time- and Frequency-Domain Analysis Current Sensor-Based Power Meter Prototype Developed over Fog-Cloud Analytics for Demand-Side Management.

Electrical energy management, or demand-side management (DSM), in a smart grid is very important for electrical energy savings. With the high penetration rate of the Internet of Things (IoT) paradigm in modern society, IoT-oriented electrical energy management systems (EMSs) in DSM are capable of skillfully monitoring the energy consumption of electrical appliances. While many of today's IoT devices used in EMSs take advantage of cloud analytics, IoT manufacturers and application developers are devoting themselves to novel IoT devices developed at the edge of the Internet. In this study, a smart autonomous time and frequency analysis current sensor-based power meter prototype, a novel IoT end device, in an edge analytics-based artificial intelligence (AI) across IoT (AIoT) architecture launched with cloud analytics is developed. The prototype has assembled hardware and software to be developed over fog-cloud analytics for DSM in a smart grid. Advanced AI well trained offline in cloud analytics is autonomously and automatically deployed onsite on the prototype as edge analytics at the edge of the Internet for online load identification in DSM. In this study, auto-labeling, or online load identification, of electrical appliances monitored by the developed prototype in the launched edge analytics-based AIoT architecture is experimentally demonstrated. As the proof-of-concept demonstration of the prototype shows, the methodology in this study is feasible and workable.

Design and Implementation of Cloud Analytics-Assisted Smart Power Meters Considering Advanced Artificial Intelligence as Edge Analytics in Demand-Side Management for Smart Homes.

In a smart home linked to a smart grid (SG), demand-side management (DSM) has the potential to reduce electricity costs and carbon/chlorofluorocarbon emissions, which are associated with electricity used in today's modern society. To meet continuously increasing electrical energy demands requested from downstream sectors in an SG, energy management systems (EMS), developed with paradigms of artificial intelligence (AI) across Internet of things (IoT) and conducted in fields of interest, monitor, manage, and analyze industrial, commercial, and residential electrical appliances efficiently in response to demand response (DR) signals as DSM. Usually, a DSM service provided by utilities for consumers in an SG is based on cloud-centered data science analytics. However, such cloud-centered data science analytics service involved for DSM is mostly far away from on-site IoT end devices, such as DR switches/power meters/smart meters, which is usually unacceptable for latency-sensitive user-centric IoT applications in DSM. This implies that, for instance, IoT end devices deployed on-site for latency-sensitive user-centric IoT applications in DSM should be aware of immediately analytical, interpretable, and real-time actionable data insights processed on and identified by IoT end devices at IoT sources. Therefore, this work designs and implements a smart edge analytics-empowered power meter prototype considering advanced AI in DSM for smart homes. The prototype in this work works in a cloud analytics-assisted electrical EMS architecture, which is designed and implemented as edge analytics in the architecture described and developed toward a next-generation smart sensing infrastructure for smart homes. Two different types of AI deployed on-site on the prototype are conducted for DSM and compared in this work. The experimentation reported in this work shows the architecture described with the prototype in this work is feasible and workable.

Visual IoT Security: Data Hiding in AMBTC Images Using Block-Wise Embedding Strategy.

This study investigates combining the property of human vision system and a 2-phase data hiding strategy to improve the visual quality of data-embedded compressed images. The visual Internet of Things (IoT) is indispensable in smart cities, where different sources of visual data are collected for more efficient management. With the transmission through the public network, security issue becomes critical. Moreover, for the sake of increasing transmission efficiency, image compression is widely used. In order to respond to both needs, we present a novel data hiding scheme for image compression with Absolute Moment Block Truncation Coding (AMBTC). Embedding secure data in digital images has broad security uses, e.g., image authentication, prevention of forgery attacks, and intellectual property protection. The proposed method embeds data into an AMBTC block by two phases. In the intra-block embedding phase, a hidden function is proposed, where the five AMBTC parameters are extracted and manipulated to embed the secret data. In the inter-block embedding phase, the relevance of high mean and low mean values between adjacent blocks are exploited to embed additional secret data in a reversible way. Between these two embedding phases, a halftoning scheme called direct binary search is integrated to efficiently improve the image quality without changing the fixed parameters. The modulo operator is used for data extraction. The advantages of this study contain two aspects. First, data hiding is an essential area of research for increasing the IoT security. Second, hiding in compressed images instead of original images can improve the network transmission efficiency. The experimental results demonstrate the effectiveness and superiority of the proposed method.

An Adaptive Exposure Fusion Method Using fuzzy Logic and Multivariate Normal Conditional Random Fields.

High dynamic range (HDR) has wide applications involving intelligent vision sensing which includes enhanced electronic imaging, smart surveillance, self-driving cars, intelligent medical diagnosis, etc. Exposure fusion is an essential HDR technique which fuses different exposures of the same scene into an HDR-like image. However, determining the appropriate fusion weights is difficult because each differently exposed image only contains a subset of the scene's details. When blending, the problem of local color inconsistency is more challenging; thus, it often requires manual tuning to avoid image artifacts. To address this problem, we present an adaptive coarse-to-fine searching approach to find the optimal fusion weights. In the coarse-tuning stage, fuzzy logic is used to efficiently decide the initial weights. In the fine-tuning stage, the multivariate normal conditional random field model is used to adjust the fuzzy-based initial weights which allows us to consider both intra- and inter-image information in the data. Moreover, a multiscale enhanced fusion scheme is proposed to blend input images when maintaining the details in each scale-level. The proposed fuzzy-based MNCRF (Multivariate Normal Conditional Random Fields) fusion method provided a smoother blending result and a more natural look. Meanwhile, the details in the highlighted and dark regions were preserved simultaneously. The experimental results demonstrated that our work outperformed the state-of-the-art methods not only in several objective quality measures but also in a user study analysis.

Repurposing tofacitinib as an anti-myeloma therapeutic to reverse growth-promoting effects of the bone marrow microenvironment.

The myeloma bone marrow microenvironment promotes proliferation of malignant plasma cells and resistance to therapy. Activation of JAK/STAT signaling is thought to be a central component of these microenvironment-induced phenotypes. In a prior drug repurposing screen, we identified tofacitinib, a pan-JAK inhibitor Food and Drug Administration (FDA) approved for rheumatoid arthritis, as an agent that may reverse the tumor-stimulating effects of bone marrow mesenchymal stromal cells. Herein, we validated in vitro, in stromal-responsive human myeloma cell lines, and in vivo, in orthotopic disseminated xenograft models of myeloma, that tofacitinib showed efficacy in myeloma models. Furthermore, tofacitinib strongly synergized with venetoclax in coculture with bone marrow stromal cells but not in monoculture. Surprisingly, we found that ruxolitinib, an FDA approved agent targeting JAK1 and JAK2, did not lead to the same anti-myeloma effects. Combination with a novel irreversible JAK3-selective inhibitor also did not enhance ruxolitinib effects. Transcriptome analysis and unbiased phosphoproteomics revealed that bone marrow stromal cells stimulate a JAK/STAT-mediated proliferative program in myeloma cells, and tofacitinib reversed the large majority of these pro-growth signals. Taken together, our results suggest that tofacitinib reverses the growth-promoting effects of the tumor microenvironment. As tofacitinib is already FDA approved, these results can be rapidly translated into potential clinical benefits for myeloma patients.

Residential Consumer-Centric Demand-Side Management Based on Energy Disaggregation-Piloting Constrained Swarm Intelligence: Towards Edge Computing.

The emergence of smart Internet of Things (IoT) devices has highly favored the realization of smart homes in a down-stream sector of a smart grid. The underlying objective of Demand Response (DR) schemes is to actively engage customers to modify their energy consumption on domestic appliances in response to pricing signals. Domestic appliance scheduling is widely accepted as an effective mechanism to manage domestic energy consumption intelligently. Besides, to residential customers for DR implementation, maintaining a balance between energy consumption cost and users’ comfort satisfaction is a challenge. Hence, in this paper, a constrained Particle Swarm Optimization (PSO)-based residential consumer-centric load-scheduling method is proposed. The method can be further featured with edge computing. In contrast with cloud computing, edge computing—a method of optimizing cloud computing technologies by driving computing capabilities at the IoT edge of the Internet as one of the emerging trends in engineering technology—addresses bandwidth-intensive contents and latency-sensitive applications required among sensors and central data centers through data analytics at or near the source of data. A non-intrusive load-monitoring technique proposed previously is utilized to automatic determination of physical characteristics of power-intensive home appliances from users’ life patterns. The swarm intelligence, constrained PSO, is used to minimize the energy consumption cost while considering users’ comfort satisfaction for DR implementation. The residential consumer-centric load-scheduling method proposed in this paper is evaluated under real-time pricing with inclining block rates and is demonstrated in a case study. The experimentation reported in this paper shows the proposed residential consumer-centric load-scheduling method can re-shape loads by home appliances in response to DR signals. Moreover, a phenomenal reduction in peak power consumption is achieved by 13.97%.

The impact of central line insertion bundle on central line-associated bloodstream infection.

BACKGROUND: Knowledge about the impact of each central line insertion bundle on central line-associated bloodstream infection (CLABSI) is limited. METHODS: A quality-improvement intervention, including education, central venous catheter (CVC) insertion bundle, process and outcome surveillance, have been introduced since March 2013. Outcome surveillances, including CLABSI per 1,000 catheter-days, CLABSI per 1,000 inpatient-days, and catheter utilization rates (days of catheter use divided by total inpatient-days), were measured. As a baseline measurement for a comparison, we retrospectively collected data from March 1, 2012 to December 31, 2012. RESULTS: During this 10-month period, there were a total of 687 CVC insertions, and 627 (91.2%) insertions were performed by intensivists. The rate of CLABSI significantly declined from 1.65 per 1000 catheter-day during the pre-intervention period to 0.65 per 1000 catheter-day post-intervention period (P=0.039). CLABSI more likely developed in subjects in which a maximal sterile barrier was not used compared with subjects in which it was used (P=0.03). Moreover, CVC inserted by non-intensivists were more likely to become infected than CVC inserted by intensivists (P=0.010). CONCLUSIONS: This multidisciplinary infection control intervention, including a central line insertion care bundle, can effectively reduce the rate of CLABSI. The impact of different care bundle varies, and a maximal sterile barrier precaution during catheter insertion is an essential component of the care line insertion bundle.

The impact of job stressors on health-related quality of life of nursing assistants in long-term care settings.

This study aimed to investigate the relationship between various job stressors and health-related quality of life among female nursing assistants working in long-term care facilities. A cross-sectional study was conducted in Taiwan. Data were collected using a structured, well-designed, pre-tested questionnaire with background questions and questions about job stressors and health-related quality of life as measured by SF-12. Our empirical results show that nursing assistants with higher scores for job control and work-related social support tend to enjoy better mental health, as indicated by higher mental component summary scores. Additionally, nursing assistants with higher psychological demand scores tend to have worse overall health, as indicated by lower physical component summary and mental component summary scores. We suggest reducing selected job stressors and enhancing job control to improve nursing assistants' health-related quality of life.

Topical Antibiotic Prophylaxis for Preventing Surgical Site Infections of Clean Wounds: A Systematic Review and Meta-Analysis.

Background: Topical antibiotic agents are not generally indicated for preventing of surgical site infections (SSIs) in clean incisions, and the drug concentrations that should be delivered to local incision sites remain uncertain. The aim of this study was to critically assess the efficacy of topical antibiotic agents in comparison with non-antibiotic agents for preventing SSIs in clean incisions by performing a systematic review and meta-analysis. Methods: We conducted a search of literature in PubMed, Embase, and Cochrane Databases and included randomized controlled trials (RCTs) on topical antibiotic use for patients with clean post-surgical incisions. The primary outcome was the incidence of SSI, presented as the event rate. Eleven RCTs were included. Results: Using random-effects modeling, the pooled risk ratio (RR) of developing a post-surgical incisions infection was 0.83 (95% confidence interval [CI], 0.61-1.16; I2, 0%). In subgroup analyses, no reductions in SSI were observed when topical antibiotic agents were used to treat incisions due to spinal (RR, 0.75; 95% CI, 0.40-1.38; I2, 0%), orthopedic (RR, 0.69; 95% CI, 0.37-1.29; I2, 0%), dermatologic (RR, 0.77; 95% CI, 0.39-1.55; I2, 65%), or cardiothoracic surgeries (RR, 1.31; 95% CI, 0.83-2.06; I2: 0%). The incidence of SSI across different operative phases did not differ for the application of topical antibiotic agents compared with non-antibiotic agents (RR, 0.80; 95% CI, 0.56-1.14; I2, 0%). Conclusions: The results of this meta-analysis show that topical antibiotic agents provide no clinical benefit for preventing SSI in clean incisions.

Gas exchange and chlorophyll fluorescence responses of Camellia sinensis grown under various cultivations in different seasons.

Sod culture (SC) and conventional agriculture (CA) represent two distinct field management approaches utilized in the cultivation of tea plants in Taiwan. In this study, we employed gas exchange and chlorophyll fluorescence techniques to assess the impact of SC and CA methods on the photosynthetic machinery of Camellia sinensis cv. TTES No.12 (Jhinhsuan) in response to variable light intensities across different seasons. In spring, at photosynthetic photon flux densities (PPFD) ranging from 800 to 2,000 µmol photon m-2 s-1, the net photosynthesis rate (Pn, 10.43 µmol CO2 m-2 s-1), stomatal conductance (Gs, 126.11 mmol H2O m-2 s-1), electron transport rate (ETR, 137.94), and ΔF/Fm' and Fv/Fm (50.37) values for plants grown using SC were comparatively higher than those cultivated under CA. Conversely, the non-photochemical quenching (NPQ) values for SC-grown plants were relatively lower (3.11) compared to those grown under CA at 800 to 2,000 PPFD in spring. Additionally, when tea plants were exposed to PPFD levels below 1,500 µmol photon m- 2 s- 1, there was a concurrent increase in Pn, Gs, ETR, and NPQ. These photosynthetic parameters are crucial for devising models that optimize cultivation practices across varying seasons and specific tillage requirements, and for predicting photosynthetic and respiratory responses of tea plants to seasonally or artificially altered light irradiances. The observed positive impacts of SC on maximum photosynthetic rate (Amax), Fv/Fm, Gs, water-use efficiency (WUE), and ETR suggest that SC is advantageous for enhancing the productivity of tea plants, thereby offering a more adaptable management model for tea gardens.

Low but highly geographically structured genomic diversity of East Asian Eurasian otters and its conservation implications.

Populations of Eurasian otters Lutra lutra, one of the most widely distributed apex predators in Eurasia, have been depleted mainly since the 1950s. However, a lack of information about their genomic diversity and how they are organized geographically in East Asia severely impedes our ability to monitor and conserve them in particular management units. Here, we re-sequenced and analyzed 20 otter genomes spanning continental East Asia, including a population at Kinmen, a small island off the Fujian coast, China. The otters form three genetic clusters (one of L. l. lutra in the north and two of L. l. chinensis in the south), which have diverged in the Holocene. These three clusters should be recognized as three conservation management units to monitor and manage independently. The heterozygosity of the East Asian otters is as low as that of the threatened carnivores sequenced. Historical effective population size trajectories inferred from genomic variations suggest that their low genomic diversity could be partially attributed to changes in the climate since the mid-Pleistocene and anthropogenic intervention since the Holocene. However, no evidence of genetic erosion, mutation load, or high level of inbreeding was detected in the presumably isolated Kinmen Island population. Any future in situ conservation efforts should consider this information for the conservation management units.

Targeting high-risk multiple myeloma genotypes with optimized anti-CD70 CAR-T cells.

Despite the success of BCMA-targeting CAR-Ts in multiple myeloma, patients with high-risk cytogenetic features still relapse most quickly and are in urgent need of additional therapeutic options. Here, we identify CD70, widely recognized as a favorable immunotherapy target in other cancers, as a specifically upregulated cell surface antigen in high risk myeloma tumors. We use a structure-guided design to define a CD27-based anti-CD70 CAR-T design that outperforms all tested scFv-based CARs, leading to >80-fold improved CAR-T expansion in vivo. Epigenetic analysis via machine learning predicts key transcription factors and transcriptional networks driving CD70 upregulation in high risk myeloma. Dual-targeting CAR-Ts against either CD70 or BCMA demonstrate a potential strategy to avoid antigen escape-mediated resistance. Together, these findings support the promise of targeting CD70 with optimized CAR-Ts in myeloma as well as future clinical translation of this approach.

A CRISPR-engineered isogenic model of the 22q11.2 A-B syndromic deletion.

22q11.2 deletion syndrome, associated with congenital and neuropsychiatric anomalies, is the most common copy number variant (CNV)-associated syndrome. Patient-derived, induced pluripotent stem cell (iPS) models have provided insight into this condition. However, patient-derived iPS cells may harbor underlying genetic heterogeneity that can confound analysis. Furthermore, almost all available models reflect the commonly-found ~ 3 Mb "A-D" deletion at this locus. The ~ 1.5 Mb "A-B" deletion, a variant of the 22q11.2 deletion which may lead to different syndromic features, and is much more frequently inherited than the A-D deletion, remains under-studied due to lack of relevant models. Here we leveraged a CRISPR-based strategy to engineer isogenic iPS models of the 22q11.2 "A-B" deletion. Differentiation to excitatory neurons with subsequent characterization by transcriptomics and cell surface proteomics identified deletion-associated alterations in proliferation and adhesion. To illustrate in vivo applications of this model, we further implanted neuronal progenitor cells into the cortex of neonatal mice and found potential alterations in neuronal maturation. The isogenic models generated here will provide a unique resource to study this less-common variant of the 22q11.2 microdeletion syndrome.