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Multiple myeloma (MM) is a hematologic malignancy characterized by the proliferation of clonal plasma cells. Although advances in treatment have markedly improved survival outcomes for patients with MM, this disease is still considered incurable owing to its high incidence of relapse and refractoriness. Isatuximab is an anti-CD38 monoclonal antibody that can induce apoptosis in myeloma cells through a variety of mechanisms. Many clinical studies have demonstrated the efficacy and efficiency of isatuximab in both relapsed/refractory multiple myeloma (RRMM) and newly diagnosed multiple myeloma, leading to its approval for the treatment of adults with RRMM in combination therapies. In this review, the structure, mechanisms of action, pharmacokinetics, pharmacogenetics, and safety profile of isatuximab in MM are summarized. Additionally, isatuximab is compared with daratumumab in terms of mechanism and efficacy.

Interminable surveillance and reconnaissance through various sophisticated multispectral detectors present threats to military equipment and manpower. However, a combination of detectors operating in different wavelength bands (from hundreds of nanometers to centimeters) and based on different principles raises challenges to the conventional single-band camouflage devices. In this paper, multispectral camouflage is demonstrated for the visible, mid-infrared (MIR, 3–5 and 8–14 μm), lasers (1.55 and 10.6 μm) and microwave (8–12 GHz) bands with simultaneous efficient radiative cooling in the non-atmospheric window (5–8 μm). The device for multispectral camouflage consists of a ZnS/Ge multilayer for wavelength selective emission and a Cu-ITO-Cu metasurface for microwave absorption. In comparison with conventional broadband low emittance material (Cr), the IR camouflage performance of this device manifests 8.4/5.9 °C reduction of inner/surface temperature, and 53.4/13.0% IR signal decrease in mid/long wavelength IR bands, at 2500 W ∙ m −2 input power density. Furthermore, we reveal that the natural convection in the atmosphere can be enhanced by radiation in the non-atmospheric window, which increases the total cooling power from 136 W ∙ m −2 to 252 W ∙ m −2 at 150 °C surface temperature. This work may introduce the opportunities for multispectral manipulation, infrared signal processing, thermal management, and energy-efficient applications. Manipulating electromagnetic waves to camouflage objects is an important tool. Here, the authors present a camouflage that covers a wide range of frequencies based on multilayer and metasurface technologies.

Here we report a compact multilayer film structure for angle robust color filtering, which is verified by theoretical calculations and experiment results. The introduction of the amorphous silicon in the proposed unsymmetrical resonant cavity greatly reduces the angular sensitivity of the filters, which is confirmed by the analysis of the phase shift within the structure. The temperature of the substrate during the deposition is expressly investigated to obtain the best optical performance with high peak reflectance and good angle insensitive color filtering by compromising the refractive index of dielectric layer and the surface roughness of the multilayer film. And the outlayer of the structure, worked as the anti-reflection layer, have an enormous impact on the filtering performance. This method, described in this paper, can have enormous potential for diverse applications in display, colorful decoration, anti-counterfeiting and so forth.

Due to the emergence of mass personalized production, intelligent welding systems must achieve high levels of productivity and flexibility. Therefore, a self-learning welding-task sequencing method that is driven by data and knowledge was developed during this study. First, a minimized dataset of welding sequences, which is required to predict the welding deformation, was designed according to the number and directions of the welds included in the welding tasks. The dataset consisted of a finite number of welding sequences and their corresponding welding deformation data. Then, an algorithm to predict the welding deformation was developed. To improve the interpretability of the results, domain knowledge was integrated into the construction and training processes of a self-learning model. Finally, a case study regarding bracket welding was investigated. With FEA as the benchmark, the maximum relative error of the welding deformation predicted by the algorithm designed to predict the welding deformation was 8%. The maximum deformation of the optimal welding-task sequence output by the self-learning welding-task sequencing method driven by data and knowledge was 32.31% less than that produced by the rule-based reasoning method. The study results demonstrate that the proposed welding-task sequencing method is effective for welding sequence planning of laser welding bracket structures.

Many applications requiring both spectral and spatial information at high resolution benefit from spectral imaging. Although different technical methods have been developed and commercially available, computational spectral cameras represent a compact, lightweight, and inexpensive solution. However, the tradeoff between spatial and spectral resolutions, dominated by the limited data volume and environmental noise, limits the potential of these cameras. In this study, we developed a deeply learned broadband encoding stochastic hyperspectral camera. In particular, using advanced artificial intelligence in filter design and spectrum reconstruction, we achieved 7000–11,000 times faster signal processing and ~10 times improvement regarding noise tolerance. These improvements enabled us to precisely and dynamically reconstruct the spectra of the entire field of view, previously unreachable with compact computational spectral cameras.

Active temperature control devices are widely used for the thermal management of enclosures, including vehicles and buildings. Passive radiative cooling has been extensively studied; however, its integration with existing actively temperature regulated and decorative enclosures has slipped out of the research at status quo. Here, we present a photonic-engineered dual-side thermal management strategy for reducing the active power consumption of the existing temperature-regulated enclosure without sacrificing its aesthetics. By coating the exterior and interior of the enclosure roof with two visible-transparent films with distinctive wavelength-selectivity, simultaneous control over the energy exchange among the enclosure with the hot sun, the cold outer space, the atmosphere, and the active cooler can be implemented. A power-saving of up to 63% for active coolers of the enclosure is experimentally demonstrated by measuring the heat flux compared to the ordinary enclosure when the set temperature is around 26°C. This photonic-engineered dual-side thermal management strategy offers facile integration with the existing enclosures and represents a new paradigm toward carbon neutrality.

Major depressive disorder (MDD) is a leading psychiatric disorder with increasing global prevalence, yet its underlying pathogenesis remains inadequately elucidated. Increasing evidence highlights the complex interplay between the immune system, gut microbiota, and their bidirectional crosstalk with the central nervous system. Gut microbiota dysbiosis affects neuroimmune and intestinal immune homeostasis, driving bidirectional peripheral-central immune responses through immune-to-brain and gut-to-brain communication. This process involves impaired intestinal barrier integrity (bacterial translocation), systemic low-grade inflammation, activation of innate immune signaling pathways (e.g., TLR4 and NLRP3 inflammasomes), glial cell activation, neuroinflammation, and blood–brain barrier (BBB) dysfunction, ultimately leading to neuronal injury and disturbances in mood, cognition, and behavior. Conversely, gut microbiota and their metabolites exert neuroprotective effects through facilitating neurotransmitter synthesis, regulating the hypothalamic–pituitary–adrenal axis activity, and modulating immune response. Collectively, these actions enhance synaptic plasticity, suppress hippocampal neuronal apoptosis, and maintain BBB integrity. Understanding these immune-mediated multidimensional mechanisms not only deepens our understanding of the pathophysiology of MDD but also provides new perspectives for identifying potential biomarkers and developing therapeutic targets.

The emergence of mass personalized production has increased the adaptability and intelligence requirements of welding robots. To address the challenges associated with mass personalized production, this paper proposes a novel knowledge-driven framework for intelligent welding process planning in cloud robotics systems. This framework integrates cloud-edge-end collaborative computing with ontology-based knowledge representation to enable efficient welding process optimization. A hierarchical knowledge-based architecture was developed using the SQLite 3.38.0, Redis 5.0.4, and HBase 2.1.0 tools. The ontology models formally define the welding tasks, resources, processes, and results, thereby enabling semantic interoperability across heterogeneous systems. A hybrid knowledge evolution method that combines cloud-based welding simulation and transfer learning is presented as a means of achieving inexpensive, efficient, and intelligent evolution of welding process knowledge. Experiments demonstrated that, with respect to pure cloud-based solutions, edge-based knowledge bases can reduce the average response time by 86%. The WeldNet-152 model achieved a welding parameter prediction accuracy of 95.1%, while the knowledge evolution method exhibited a simulation-to-reality transfer accuracy of 78%. The proposed method serves as a foundation for significant enhancements in the adaptability of welding robots to Industry 5.0 manufacturing environments.

Angle insensitive color filter based on Metal-SiO x -Metal structure is proposed in this paper, which can keep the same perceived transmitted color when the incidence angle changes from 0° to 60°, especially for p-polarization light. Various silicon oxide films deposited by reaction magnetron sputtering with a tunable refractive index from 1.97 to 3.84 is introduced to meet the strict angle insensitive resonance conditions. The angle resolved spectral filtering for both p-polarization light and s-polarization light are quite well, which can be attributed to the different physical origins for the high angular tolerance for two polarizations. Finally, the effect of SiO x absorption and Ag thickness on the peak transmittance are analyzed.

Post-stroke depression (PSD) is a prevalent and severe sequela of stroke. It is an emotional disorder that significantly impacts functional recovery, prognosis, secondary stroke risk, and mortality among stroke survivors. The incidence rate of PSD is 18 %∼33 %, with symptoms such as low mood, decreased interest, sleep disorders, decreased appetite, impaired attention, and in severe cases, hallucinations and even suicidal tendencies. While diverse therapeutic modalities are employed globally to address PSD, each approach carries its inherent advantages and limitations. Notably, acupuncture stands out as a promising and effective intervention for ameliorating PSD symptoms and enhancing stroke prognosis. This study aims to conduct a bibliometric analysis to scrutinize the current landscape, identify hotspots, and explore frontiers in acupuncture research for PSD. A systematic search for acupuncture and PSD-related research was conducted from January 2014 to October 2023 on the Web of Science Core Collection (WoSCC). The data were downloaded and processed using Bibliometrix and VOSviewer to generate knowledge visualization maps. A total of 11,540 articles related to acupuncture and PSD were retrieved. China emerged as the leading contributor with the highest volume of articles on acupuncture and PSD. Author Liu CZ attained the highest H-index, focusing primarily on investigating the compatibility effects and mechanisms of acupoints. Common hotspot keywords included pain, stimulation, mechanisms, complementary, and alternative medicine. The main research frontiers were mechanisms, neuroinflammation, gut microbiota, and therapeutic methods. This study offered multifaceted insights into acupuncture for PSD, unveiling pivotal areas, research hotspots, and emerging trends. The findings aimed to guide researchers in exploring novel research directions and selecting appropriate journals for advancing the understanding and treatment of PSD through acupuncture interventions. •This is the first bibliometric analysis of study on acupuncture treatment for Post-Stroke Depression.•Keywords were classified into acupuncture, post-stroke depression, bibliometric analysis, VOSviewer, and research direction.•The article thoroughly discusses the hotspots and global trends in acupuncture treatment for post-stroke depression.