Dual Modulation Research Articles

Dual role of triglyceride structures facilitates anti-tumor drug delivery: Both as a self-assembling module and a responsive module

Small molecule prodrugs self-assembled nano-delivery systems with tumor responsive linkages are emerging as an effective platform. However, the heterogeneity of tumor microenvironment may limit the anti-tumor effect of prodrug nanomedicines with a single response module. Here, we chose disulfide bond as the response module and branched chain alcohol as the self-assembly modification module to construct a single-responsive prodrug. We also constructed a double-responsive paclitaxel prodrug combining triglyceride and disulfide bond, taking into account of the highly expressed lipase and glutathione levels in tumor cells. The results showed that the anti-tumor effect of single-responsive branched chain alcohol modified prodrug nanoparticles was inferior to triglyceride prodrug nanoparticles with dual response modules. The triglyceride structure can not only serve as a self-assembly modification module, but also serve as a response module for intelligent drug release in tumor. Such dual roles will facilitate the efficient delivery of small molecule self-assembled prodrugs to tumor sites.

HN1-mediated activation of lipogenesis through Akt-SREBP signaling promotes hepatocellular carcinoma cell proliferation and metastasis.

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related deaths worldwide, with more than 800,000 deaths each year, and its 5-year survival rate is less than 12%. The role of the HN1 gene in HCC has remained elusive, despite its upregulation in various cancer types. In our investigation, we identified HN1's heightened expression in HCC tissues, which, upon overexpression, fosters cell proliferation, migration, and invasion, unveiling its role as an oncogene in HCC. In addition, silencing HN1 diminished the viability and metastasis of HCC cells, whereas HN1 overexpression stimulated their growth and invasion. Gene expression profiling revealed HN1 silencing downregulated 379 genes and upregulated 130 genes, and suppressive proteins associated with the lipogenic signaling pathway networks. Notably, suppressing HN1 markedly decreased the expression levels of SREBP1 and SREBP2, whereas elevating HN1 had the converse effect. This dual modulation of HN1 affected lipid formation, hindering it upon HN1 silencing and promoting it upon HN1 overexpression. Moreover, HN1 triggers the Akt pathway, fostering tumorigenesis via SREBP1-mediated lipogenesis and silencing HN1 effectively curbed HCC tumor growth in mouse xenograft models by deactivating SREBP-1, emphasizing the potential of HN1 as a therapeutic target, impacting both external and internal factors, it holds promise as an effective therapeutic strategy for HCC.

Inhalable chitosan-coated nano-assemblies potentiate niclosamide for targeted abrogation of non-small-cell lung cancer through dual modulation of autophagy and apoptosis

Lung carcinoma, particularly non-small-cell lung cancer (NSCLC), accounts for a significant portion of cancer-related deaths, with a fatality rate of approximately 19 %. Niclosamide (NIC), originally an anthelmintic drug, has attracted attention for its potential in disrupting cancer cells through various intracellular signaling pathways. However, its effectiveness is hampered by limited solubility, reducing its bioavailability. This study investigates the efficacy of NIC against lung cancer using inhalable hybrid nano-assemblies with chitosan-functionalized Poly (ε-caprolactone) (PCL) as a carrier for pulmonary delivery. The evaluation encompasses various aspects such as aerodynamic and physicochemical properties, drug release kinetics, cellular uptake, biocompatibility, cell migration, autophagic flux, and apoptotic cell death in A549 lung cancer cells. Increasing NIC dosage correlates with enhanced inhibition of cell proliferation, showing a dose-dependent profile (approximately 75 % inhibition efficiency at 20 μg/mL of NIC). Optimization of inhaled dosage and efficacy is conducted in a murine model of NNK-induced tumor-bearing lung cancer. Following inhalation, NIC-CS-PCL-NA demonstrates significant lung deposition, retention, and metabolic stability. Inhalable nano-assemblies promote autophagy flux and induce apoptotic cell death. Preclinical trials reveal substantial tumor regression with minimal adverse effects, underscoring the potential of inhalable NIC-based nano-formulation as a potent therapeutic approach for NSCLC, offering effective tumor targeting and killing capabilities.

Wafer defect pattern recognition based on differentiable architecture search with dual attention module

Wafer defect pattern recognition based on differentiable architecture search with dual attention module

Low-Dose Metformin and Profibrotic Signature in Central Centrifugal Cicatricial Alopecia

Central centrifugal cicatricial alopecia (CCCA) is a scarring alopecia predominantly affecting Black female individuals. Current conventional treatments target inflammation but not the underlying fibrotic processes, often leading to permanent hair loss. To investigate the associations of low-dose oral metformin, an antidiabetic medication with antifibrotic properties, with clinical symptoms and scalp gene expression patterns in patients with CCCA. This retrospective clinical case series and transcriptomic analysis included patients treated at a single tertiary academic medical center between January 2023 and March 2024. All patients had biopsy-confirmed CCCA refractory to standard treatments. Transcriptomic analysis was performed on patients with previously banked, paired scalp biopsies before and after treatment with adjuvant metformin for at least 6 weeks. Extended-release metformin, 500 mg, once daily was added to participants' baseline CCCA treatment regimens. Clinical assessments included pruritus, inflammation, scalp resistance, and hair regrowth. Gene expression profiling via bulk RNA sequencing analysis evaluated differential gene expression and pathway enrichment. A total of 12 Black female participants were included in the study, and transcriptomic analysis was performed in 4 participants. After at least 6 months of metformin treatment, 9 participants experienced improvement in disease, including scalp pain, inflammation, and/or pruritus, and 6 demonstrated clinical evidence of hair regrowth. The addition of metformin led to reversal of many prominent gene pathways previously identified in CCCA. Transcriptomic analysis revealed upregulation of pathways and genes (keratin-associated proteins [KRTAPs]) involved in keratinization, epidermis development, and the hair cycle (absolute log2-fold change > 4), with concomitant downregulation of fibrosis-related pathways and genes (eg, MMP7, COL6A1) (fold change >1.5; all false discovery rate <.05). Gene set analysis showed reduced expression of helper T cell 17 and epithelial-mesenchymal transition pathways and elevated adenosine monophosphate kinase signaling and KRTAPs after metformin treatment. In this case series of patients with treatment-refractory CCCA, low-dose oral metformin was associated with symptomatic improvement and dual modulation of gene expression, stimulating hair growth pathways while suppressing fibrosis and inflammation markers. These findings provide a rationale for future clinical trials studying metformin as a targeted therapy for CCCA and other cicatricial alopecias.

Unraveling the Mechanisms of S100A8/A9 in Myocardial Injury and Dysfunction.

S100A8 and S100A9, which are prominent members of the calcium-binding protein S100 family and recognized as calprotectin, form a robust heterodimer known as S100A8/A9, crucial for the manifestation of their diverse biological effects. Currently, there is a consensus that S100A8/A9 holds promise as a biomarker for cardiovascular diseases (CVDs), exerting an influence on cardiomyocytes or the cardiovascular system through multifaceted mechanisms that contribute to myocardial injury or dysfunction. In particular, the dualistic nature of S100A8/A9, which functions as both an inflammatory mediator and an anti-inflammatory agent, has garnered significantly increasing attention. This comprehensive review explores the intricate mechanisms through which S100A8/A9 operates in cardiovascular diseases, encompassing its bidirectional regulatory role in inflammation, the initiation of mitochondrial dysfunction, the dual modulation of myocardial fibrosis progression, and apoptosis and autophagy. The objective is to provide new information on and strategies for the clinical diagnosis and treatment of cardiovascular diseases in the future.

BER and outage throughput analysis of DPIM/DHPIM coded QPSK-OFDM based outdoor optical wireless communications

With the growing demand for fast and reliable communication systems, particularly in outdoor environments, it is essential to investigate advanced encoding techniques. Digital Pulse Interval Modulation (DPIM) and Dual Header Pulse Interval Modulation (DHPIM) emerge as promising alternatives to traditional line coding methods, providing enhanced spectral efficiency and resistance to signal disruptions. This paper presents the performance of a Quadrature Phase-Shift Keying (QPSK) Orthogonal Frequency Division Multiplexing (OFDM)-based Optical Wireless Communication (OWC) system using these advanced encoding schemes. The analysis includes simulation results on QPSK-OFDM-based transmitter design, free space optical channel modeling based on Gaussian and log-normal distribution atmospheric turbulence, and recovery of input digital stream using the mentioned line coding techniques. The results demonstrate optimal bit error rate (BER) values for the QPSK-OFDM-based OWC system. The primary innovation of this research lies in the encoding schemes and their performance in outdoor optical wireless communication systems under turbulent conditions. Through extensive simulations and analysis, detailed insights into the bit error rate and outage throughput characteristics of DPIM/DHPIM coded QPSK-OFDM are provided, offering a unique perspective on the performance of these schemes in real-world scenarios. The findings not only highlight the optimal BER values achievable with the QPSK-OFDM-based OWC system but also offer insights into the system's ability to overcome transmission challenges under various atmospheric conditions.

Semantic-Aware Dynamic Generation Networks for Few-Shot Human-Object Interaction Recognition.

Recognizing human-object interaction (HOI) aims at inferring various relationships between actions and objects. Although great progress in HOI has been made, the long-tail problem and combinatorial explosion problem are still practical challenges. To this end, we formulate HOI as a few-shot task to tackle both challenges and design a novel dynamic generation method to address this task. The proposed approach is called semantic-aware dynamic generation networks (SADG-Nets). Specifically, SADG-Net first assigns semantic-aware task representations for different batches of data, which further generates dynamic parameters. It obtains the features that highlight intercategory discriminability and intracategory commonality adaptively. In addition, we also design a dual semantic-aware encoder module (DSAE-Module), that is, verb-aware and noun-aware branches, to yield both action and object prototypes of HOI for each task space, which generalizes to novel combinations by transferring similarities among interactions. Extensive experimental results on two benchmark datasets, that is, humans interacting with common objects (HICO)-FS and trento universal HOI (TUHOI)-FS, illustrate that our SADG-Net achieves superior performance over state-of-the-art approaches, which proves its impressive effectiveness on few-shot HOI recognition.

GLFuse: A Global and Local Four-Branch Feature Extraction Network for Infrared and Visible Image Fusion

Infrared and visible image fusion integrates complementary information from different modalities into a single image, providing sufficient imaging information for scene interpretation and downstream target recognition tasks. However, existing fusion methods often focus only on highlighting salient targets or preserving scene details, failing to effectively combine entire features from different modalities during the fusion process, resulting in underutilized features and poor overall fusion effects. To address these challenges, a global and local four-branch feature extraction image fusion network (GLFuse) is proposed. On one hand, the Super Token Transformer (STT) block, which is capable of rapidly sampling and predicting super tokens, is utilized to capture global features in the scene. On the other hand, a Detail Extraction Block (DEB) is developed to extract local features in the scene. Additionally, two feature fusion modules, namely the Attention-based Feature Selection Fusion Module (ASFM) and the Dual Attention Fusion Module (DAFM), are designed to facilitate selective fusion of features from different modalities. Of more importance, the various perceptual information of feature maps learned from different modality images at the different layers of a network is investigated to design a perceptual loss function to better restore scene detail information and highlight salient targets by treating the perceptual information separately. Extensive experiments confirm that GLFuse exhibits excellent performance in both subjective and objective evaluations. It deserves note that GLFuse effectively improves downstream target detection performance on a unified benchmark.

DualAttlog: Context aware dual attention networks for log-based anomaly detection

Most existing log-driven anomaly detection methods assume that logs are static and unchanged, which is often impractical. To address this, we propose a log anomaly detection model called DualAttlog. This model includes word-level and sequence-level semantic encoding modules, as well as a context-aware dual attention module. Specifically, The word-level semantic encoding module utilizes a self-matching attention mechanism to explore the interactive properties between words in log sequences. By performing word embedding and semantic encoding, it captures the associations and evolution processes between words, extracting local-level semantic information. while The sequence-level semantic encoding module encoding the entire log sequence using a pre-trained model. This extracts global semantic information, capturing overall patterns and trends in the logs. The context-aware dual attention module integrates these two levels of encoding, utilizing contextual information to reduce redundancy and enhance detection accuracy. Experimental results show that the DualAttlog model achieves an F1-Score of over 95% on 7 public datasets. Impressively, it achieves an F1-Score of 82.35% on the Real-Industrial W dataset and 83.54% on the Real-Industrial Q dataset. It outperforms existing baseline techniques on 9 datasets, demonstrating its significant advantages.

Reconstruction of natural images from human fMRI using a three-stage multi-level deep fusion model

BackgroundImage reconstruction is a critical task in brain decoding research, primarily utilizing functional magnetic resonance imaging (fMRI) data. However, due to challenges such as limited samples in fMRI data, the quality of reconstruction results often remains poor. New methodWe proposed a three-stage multi-level deep fusion model (TS-ML-DFM). The model employed a three-stage training process, encompassing components such as image encoders, generators, discriminators, and fMRI encoders. In this method, we incorporated distinct supplementary features derived separately from depth images and original images. Additionally, the method integrated several components, including a random shift module, dual attention module, and multi-level feature fusion module. ResultsIn both qualitative and quantitative comparisons on the Horikawa17 and VanGerven10 datasets, our method exhibited excellent performance.Comparison with existing methods: For example, on the primary Horikawa17 dataset, our method was compared with other leading methods based on metrics the average hash value, histogram similarity, mutual information, structural similarity accuracy, AlexNet(2), AlexNet(5), and pairwise human perceptual similarity accuracy. Compared to the second-ranked results in each metric, the proposed method achieved improvements of 0.99 %, 3.62 %, 3.73 %, 2.45 %, 3.51 %, 0.62 %, and 1.03 %, respectively. In terms of the SwAV top-level semantic metric, a substantial improvement of 10.53 % was achieved compared to the second-ranked result in the pixel-level reconstruction methods. ConclusionsThe TS-ML-DFM method proposed in this study, when applied to decoding brain visual patterns using fMRI data, has outperformed previous algorithms, thereby facilitating further advancements in research within this field.

SARDIMM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging

The range-Doppler algorithm (RDA), a key technique for generating synthetic aperture radar (SAR) images, offers high-resolution images but requires significant memory resources and involves complex signal processing. Moreover, the multitude of fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations in RDA necessitates high bandwidth and lacks data reuse, leading to bottlenecks. This paper introduces a synthetic aperture radar dual in-line memory module (SARDIMM), which executes RDA operations near memory via near-memory processing (NMP), thereby effectively reducing memory accesses, execution time, and energy consumption. The embedded NMP module in SARDIMM optionally supports a combination of FFT, IFFT, and matched filter operations of the RDA for range and azimuth compression. The operator within the NMP module accelerates the FFT by performing two radix-2 single butterfly operations in parallel. The NMP module was implemented and validated on a Xilinx UltraScale+ field-programmable gate array (FPGA) using Verilog-HDL. The acceleration performance of RDA for images of various sizes was evaluated through a simulator modified with gem5 and DRAMSim3 and achieved a 6.34–6.93× speedup and 41.9–48.2% energy savings.

Reflux Power Optimization of a Dual-Active Hybrid Full-Bridge Converter Based on Active Disturbance Rejection Control

The dual-active hybrid full-bridge (H-FDAB) DC–DC converter has great potential in medium-voltage high-power photovoltaic power station applications by introducing a three-level bridge arm to increase the output voltage range. However, its mathematical model and optimum modulation schemes have not been fully explored. Under the traditional PI control, the H-FDAB DC–DC converter will produce significant reflux power, which will lead to a decrease in converter efficiency and output voltage fluctuation. On this basis, this paper proposes a reflux power optimization strategy for an H-FDAB DC-DC converter based on active disturbance rejection control (ADRC). Firstly, the structure and power characteristics of the H-FDAB DC–DC converter are analyzed, and the relationship among the reflux power, the transmission power, and the phase shift angle is derived. Secondly, to reduce the complexity of the control calculation, upon the foundation of dual phase-shifting modulation, the Karush–Kuhn–Tucker (KKT) condition is used to solve for the phase shift angle that corresponds to the minimum reflux power. Simultaneously, we develop an ADRC loop utilizing an extended state observer (ESO) for the real-time estimation of system states. We also consider the sudden changes in input voltage, load switching, and transmission power fluctuations caused by reflux power optimization strategies as system disturbances and compensate for them accordingly. Finally, the experiments conclusively validate the designed control strategy’s correctness and feasibility.

Inhibited Passivation by Bioinspired Cell Membrane Zn Interface for Zn-Air Batteries with Extended Temperature Adaptability.

Due to the slow dynamics of mass and charge transfer at Zn|electrolyte interface, the stable operation of Zn-air batteries (ZABs) is challenging, especially at low temperature. Herein, inspired by cell membrane, a hydrophilic-hydrophobic dual modulated Zn|electrolyte interface is constructed. This amphiphilic design enables the quasi-solid-state (QSS) ZABs to display a long-term cyclability of 180 h@50mAcm-2 at 25°C. Moreover, a record-long time of 173 h@4mAcm-2 at -60°C is also achieved, which is almost threefolds of untreated QSS ZABs. Control experiments and (in situ) characterization reveal that the growth of insulating ZnO passivation layers is largely inhibited by tuned hydrophilic-hydrophobic behavior. Thus, the enhanced transfer dynamic of Zn2+ at Zn|electrolyte interface from 25 to -60°C is attained. As an extension, the QSS Al-air batteries (AABs) with bioinspired interface also show unprecedented discharge stability of 420 h@1mAcm-2 at -40°C, which is about two times of untreated QSS AABs. This bioinspired-hydrophilic-hydrophobic dual modulation strategy may provide a reference for energy transform and storage devices with broad temperature adaptability.

MCG-RTDETR: Multi-Convolution and Context-Guided Network with Cascaded Group Attention for Object Detection in Unmanned Aerial Vehicle Imagery

In recent years, object detection in unmanned aerial vehicle (UAV) imagery has been a prominent and crucial task, with advancements in drone and remote sensing technologies. However, detecting targets in UAV images pose challenges such as complex background, severe occlusion, dense small targets, and lighting conditions. Despite the notable progress of object detection algorithms based on deep learning, they still struggle with missed detections and false alarms. In this work, we introduce an MCG-RTDETR approach based on the real-time detection transformer (RT-DETR) with dual and deformable convolution modules, a cascaded group attention module, a context-guided feature fusion structure with context-guided downsampling, and a more flexible prediction head for precise object detection in UAV imagery. Experimental outcomes on the VisDrone2019 dataset illustrate that our approach achieves the highest AP of 29.7% and AP50 of 58.2%, surpassing several cutting-edge algorithms. Visual results further validate the model’s robustness and capability in complex environments.

3DRecNet: A 3D Reconstruction Network with Dual Attention and Human-Inspired Memory

Humans inherently perceive 3D scenes using prior knowledge and visual perception, but 3D reconstruction in computer graphics is challenging due to complex object geometries, noisy backgrounds, and occlusions, leading to high time and space complexity. To addresses these challenges, this study introduces 3DRecNet, a compact 3D reconstruction architecture optimized for both efficiency and accuracy through five key modules. The first module, the Human-Inspired Memory Network (HIMNet), is designed for initial point cloud estimation, assisting in identifying and localizing objects in occluded and complex regions while preserving critical spatial information. Next, separate image and 3D encoders perform feature extraction from input images and initial point clouds. These features are combined using a dual attention-based feature fusion module, which emphasizes features from the image branch over those from the 3D encoding branch. This approach ensures independence from proposals at inference time and filters out irrelevant information, leading to more accurate and detailed reconstructions. Finally, a Decoder Branch transforms the fused features into a 3D representation. The integration of attention-based fusion with the memory network in 3DRecNet significantly enhances the overall reconstruction process. Experimental results on the benchmark datasets, such as ShapeNet, ObjectNet3D, and Pix3D, demonstrate that 3DRecNet outperforms existing methods.

A visible-infrared person re-identification method based on meta-graph isomerization aggregation module

Due to different imaging principles of visible-infrared cameras, there are modal differences between similar person images. For visible-infrared person re-identification (VI-ReID), existing works focus on extracting and aligning cross-modal global descriptors in the shared feature space, while ignoring local variations and graph-structural correlations in cross-modal image pairs. In order to bridge the modal differences with graph-structure between key-parts, a Meta-Graph Isomerization Aggregation Module (MIAM) is proposed, which includes Meta-Graph Node Isomerization Module (MNI) and Dual Aggregation Module (DA). In order to completely describe discriminative in-graph local features, MNI establishes meta-secondary cyclic isomorphism relations of in-graph local features by using a multi-branch embedding generation mechanism. Then, the local features not only contain the limited information of the fixed regions, but also benefit from the neighboring regions. Meanwhile, the secondary node generation process considers similar and different nodes of pedestrian graph structure to reduce the interference of identity differences. In addition, the Dual Aggregation (DA) module combines spatial self-attention and channel self-attention to establish the interdependence of modal heterogeneous graph structure pairs and achieve inter-modal feature aggregation. To match heterogeneous graph structures, a Node Center Joint Mining Loss (NCJM Loss) is proposed to constrain the distance between the node centers of heterogeneous graphs. The experiments performed on the SYSU-MM01, RegDB and LLCM public datasets demonstrate that the proposed method performs excellently in VI-ReID.

Mechanisms of Selected Cassipourea Metabolites for Melasma Treatment: Network Pharmacology and Molecular Dynamics Study

Background Melasma is a common dyschromia, mainly found in women with darker skin types. Although asymptomatic, melasma significantly impacts patients’ quality of life. Due to this complex pathogenesis, melasma is difficult to treat. Plant and plant-derived products have been explored as alternatives for the treatment of melasma. Methods This study utilized network pharmacology coupled with molecular docking and molecular dynamics simulations to investigate the molecular mechanisms of three selected Cassipourea metabolites in the treatment of melasma. Results Of the 202 genes obtained from the 14 profiled metabolites, only PTGS2, TYR, ESR2, and ESR1 were common among metabolites and targets implicated in melasma. From this, The gene ontology highlighted the intracellular steroid hormone receptor, signalling pathway, macromolecular complex, and estrogen receptor activity as the top enriched functional annotations, while the KEGG pathway analysis identified five signalling pathways, from which the prolactin signalling pathway, endocrine resistance, and estrogen signalling pathway were implicated in the pathogenesis of melasma. These pathways were further connected by their linkage to ESR2 and ESR1., Of all Cassipourea metabolites and standards, with afzelechin having the highest docking score for both gens. Further binding interaction analysis showed that ESR2-bound tamoxifen had the highest binding free energy of -47.68 kcal/mol, however, among the interacting Cassipourea metabolites, sitosterol-glycoside exhibited the highest negative binding affinity for both ESR2 (-40.50 kcal/mol) and ESR1 (-78.97 kcal/mol) over 150 ns simulation, suggesting its potential as a dual modulator. Altogether, the metabolites presented remarkable binding stability and thermodynamic compactness with the apo-genes. Conclusion The finding that the selected Cassipourea metabolites are associated with the genes and enzymes implicated in melasma pathogenesis, together with their significant binding effects on the enriched genes, suggests their regulatory potential on the profiled targets and, consequently, in the treatment of melasma.

Dual cross perception network with texture and boundary guidance for camouflaged object detection

Camouflaged object detection (COD) is a task needs to segment objects that subtly blend into their surroundings effectively. Edge and texture information of the objects can be utilized to reveal the edges of camouflaged objects and detect texture differences between camouflaged objects and the surrounding environment. However, existing methods often fail to fully exploit the advantages of these two types of information. Considering this, our paper proposes an innovative Dual Cross Perception Network (DCPNet) with texture and boundary guidance for camouflaged object detection. DCPNet consists of two essential modules, namely Dual Cross Fusion Module (DCFM) and the Subgroup Aggregation Module (SAM). DCFM utilizes attention techniques to emphasize the information that exists in edges and textures by cross-fusing features of the edge, texture, and basic RGB image, which strengthens the ability to capture edge information and texture details in image analysis. SAM gives varied weights to low-level and high-level features in order to enhance the comprehension of objects and scenes of various sizes. Several experiments have demonstrated that DCPNet outperforms 13 state-of-the-art methods on four widely used assessment metrics.

Innovative Integration of Dual Quantum Cascade Lasers on Silicon Photonics Platform.

For the first time, we demonstrate the hybrid integration of dual distributed feedback (DFB) quantum cascade lasers (QCLs) on a silicon photonics platform using an innovative 3D self-aligned flip-chip assembly process. The QCL waveguide geometry was predesigned with alignment fiducials, enabling a sub-micron accuracy during assembly. Laser oscillation was observed at the designed wavelength of 7.2 μm, with a threshold current of 170 mA at room temperature under pulsed mode operation. The optical output power after an on-chip beam combiner reached sub-milliwatt levels under stable continuous wave operation at 15 °C. The specific packaging design miniaturized the entire light source by a factor of 100 compared with traditional free-space dual lasers module. Divergence values of 2.88 mrad along the horizontal axis and 1.84 mrad along the vertical axis were measured after packaging. Promisingly, adhering to i-line lithography and reducing the reliance on high-end flip-chip tools significantly lowers the cost per chip. This approach opens new avenues for QCL integration on silicon photonic chips, with significant implications for portable mid-infrared spectroscopy devices.