Endoscopic submucosal dissection (ESD) is an effective technique to resect early cancers in the gastrointestinal (GI) tract. Bimanual telerobotic manipulation is an approach to performing ESD intuitively and efficiently, which requires two robotic instruments with flexibility, stiffness, dexterity and accuracy. However, the priority of these properties depends on the specific surgical tasks. In this work, we proposed the first heterogeneous flexible manipulators (HFMs) for bimanual ESD, which can take advantage of different mechanical structures. The grasping instrument employs a serial articulated manipulator (SAM) to perform multidirectional (relatively higher dexterity) and stable (relatively higher stiffness) traction for better submucosal visualization. The electrosurgical instrument utilizes a parallel continuum wrist (PCW) to execute accurate (higher accuracy) tissue dissection. Both HFMs have sufficient flexibility to go through the flexible endoscopic working channels. Based on the HFMs, we established a transendoscopic telerobotic system. The kinematics of the SAM and PCW were built in the endoscope frame using the Denavit-Hartenberg (DH) method and Cosserat rod method,

Cystic fibrosis (CF) patients continue to suffer from mucus accumulation and recurrent infections despite long-term clinical interventions. Conventional airway clearance therapies have limited accessibility to distal bronchial regions with low overall clearance efficiency, and existing studies lack objective quantitative metrics. Here, we present a microfluidic suction robot for targeted mucus clearance and liquid biopsy. The system integrates a pneumatically controlled 2-by-2 pouch matrix for multimodal actuation and a microfluidic suction module for active liquid extraction. A balloon-based pitch control mechanism with an optimal initial pitch-control pressure range of 2-3 kPa is incorporated to enable more controllable pouch deformation and stable intraluminal operation. Single-pouch deformation characterization revealed a cubic relationship between injected volume and expansion ratio, and a pouch radius of 2 mm was selected to achieve a maximum deformation of 246.91%. Destructive burst pressure tests were conducted, and the system’s safety factor is approximately 5.27. Dynamic modeling of the pouch matrix enabled sequential actuation. Differential pouch drive achieved a forward speed of 26.9 mm/min and an average yaw angle of 4.86° per drive cycle. The integrated suction module efficiently extracted 20-80% glycerol solutions within 10 s. In vivo validation confirmed sequential pouch-driven multimodal motion and successful recovery of biological samples containing mucus and tissue fragments following saline irrigation. We demonstrate that this intraluminal robotic platform enables localized mucus clearance and liquid biopsy, offering a promising strategy for minimally invasive intervention in narrow biological lumens.

BACKGROUND:Endoscopic submucosal dissection (ESD) is limited by unstable visualization, insufficient traction, and the ergonomic burden of manual endoscope manipulation, particularly in the colorectum. METHODS:We developed EndoDreams, a flexible robotic ESD assistant integrating master-slave control for both dual-arm instrumentation and endoscope guidance (insertion, withdrawal, and rotation). Master-slave responsiveness was quantified in benchtop latency and repeatability tests. In vivo feasibility was evaluated in a porcine, lesion-level randomized study (n = 24; 12 gastric, 12 colorectal), with each lesion resected under robotic assistance using standardized ESD protocols. RESULTS:Bench testing showed minimal master-slave latency (within 300 ms). In vivo, with comparable lesion sizes (approximately 2.3 cm2), EndoDreams significantly increased dissection efficiency, with shorter dissection time and higher standardized dissection speed, which improved submucosal exposure in both gastric lesions and colorectal lesions. EndoDreams. Safety outcomes suggested a potential benefit for EndoDreams platform, particularly in the colorectal lesions (lower muscular injury and perforation), and in which operator workload (NASA-TLX) was reduced as well. CONCLUSIONS:The EndoDreams system addresses major limitations in conventional ESD by enabling endoscope delivery and precise, dual-arm tissue handling under a master-slave control framework. The combined benchtop and in vivo validations confirm its feasibility and clinical safety.

Biopsies are essential in differentiating benign from malignant lesions in routine gastroscopy. Nevertheless, redundant biopsies increase patients’ expenses and pathologists’ workload. Probe-based confocal laser endomicroscopy (pCLE) enables real-time in vivo histological evaluation for gastric neoplasms and precancerous conditions. However, endoscopists vary widely in skill, and the use of pCLE requires histopathology expertise, which limits its application in nonacademic settings. This study aimed to develop a pCLE computer-aided diagnosis system (CCADS) for real-time whole-chain diagnosis of Correa cascade of gastric cancer and evaluate it in a real clinical setting. Gastric pCLE images and videos from 5771 examinations were retrospectively collected. CCADS was constructed using deep learning networks. It was developed using 47,462 pCLE images and 461 video segments and evaluated via multistep validation. A total of 11,439 images and 667 videos were identified for offline validation. Consecutive patients from October 2019 to September 2021 were enrolled in a prospective diagnostic study for real-time validation, which included 951 patients in the statistics. Blinded expert endoscopists and CCADS independently performed real-time pCLE diagnosis of gastric mucosal lesions in routine examinations, with double-read histopathology as the gold standard. The real-time diagnostic performance of CCADS was evaluated and compared with that of experts. CCADS achieved high diagnostic performance in image test, video test, and a prospective diagnostic study with a large sample size. Overall, 1254 lesions from 951 patients were included in the prospective test. The real-time diagnostic accuracies of CCADS for inflammation, atrophy, gastric intestinal metaplasia (GIM), low-grade intraepithelial neoplasia (LGIN), and high-grade intraepithelial neoplasia and gastric cancer (HGIN/CA) were 91.71

Remote robotic-assisted teleoperation is vital for overcoming geographical barriers to flexible endoscopy. To address the requirements of both flexibility and high precision in telesurgery, an intuitive robotic-assisted system for remote control is implemented. The system utilizes 5G Customer Premises Equipment (CPE) devices, combined with the WireGuard protocol, to establish a secure, low-latency communication link. Through this connection, the remote surgeon controls the endoscope via a Unity-based interface enhanced with Augmented Reality (AR). Furthermore, a trained Long Short-Term Memory (LSTM) model predicts the tip’s position based on motor pulse inputs, improving control accuracy. The long-distance experiments conducted between Shenzhen, Shandong, Switzerland, Dalian, Ireland, and Hong Kong showed that the image transmission delay ranged from 50 to 200 ms, the control delay was approximately 100 ms, and the overall delay was about 300 to 400 ms. In addition, the LSTM model achieved a prediction error predominantly within 5 mm. These results validate a low-cost, high-precision remote endoscope control system with acceptable latency, underscoring its potential for practical telesurgery applications.

With the advances in surgical robotics, robot-assisted endoscopic submucosal dissection (ESD) enables rapid resection of large lesions, minimizing recurrence rates and improving long-term overall survival. Despite these advantages, ESD is technically challenging and carries high risks of complications, necessitating skilled surgeons and precise instruments. Recent advancements in Multimodal Large Language Models (MLLMs) offer promising decision support and predictive planning capabilities for robotic systems, which allow the robot to complete complex tasks in more challenging scenarios. However, the training of MLLMs requires large-scale, well-annotated datasets, and existing datasets for multi-level fine-grained ESD surgical motion reasoning are scarce and lack detailed annotations. In this paper, we design a hierarchical decomposition of ESD motion granularity and introduce a multi-level surgical motion dataset (CoPESD) for training MLLMs as the robotic Co-Pilot of Endoscopic Submucosal Dissection. CoPESD includes 17,679 images with 32,699 bounding boxes and 88,395 multi-level motions, from over 35 hours of ESD videos for both robot-assisted and conventional surgeries. Extensive experiments demonstrate the effectiveness of CoPESD in training MLLMs to comprehend surgical scenarios and reason following surgical robotic motions. As the first multimodal ESD motion dataset, CoPESD supports advanced research in ESD motion decision-making and surgical automation. The dataset is available at https://github.com/gkw0010/CoPESD.

Lung cancer remains the leading cause of cancer-related mortality worldwide, largely due to late-stage diagnosis. Early detection is critical for improving patient outcomes, yet current screening methods, such as low-dose computed tomography (CT), often lack the sensitivity and specificity required for early-stage detection. Here, we present a multimodal early screening platform that integrates a multiplexed laser-induced graphene (LIG) immunosensor with machine learning to enhance the accuracy of lung cancer diagnosis. Our platform enables the rapid, cost-effective, and simultaneous detection of four tumor markers─neuron-specific enolase (NSE), carcinoembryonic antigen (CEA), p53, and SOX2─with limits of detection (LOD) as low as 1.62 pg/mL. By combining proteomic data from the immunosensor with deep learning-based CT imaging features and clinical data, we developed a multimodal predictive model that achieves an area under the curve (AUC) of 0.936, significantly outperforming single-modality approaches. This platform offers a transformative solution for early lung cancer screening, particularly in resource-limited settings, and provides potential technical support for precision medicine in oncology.

Robot-assisted Endoscopic Submucosal Dissection (ESD) improves the surgical procedure by providing a more comprehensive view through advanced robotic instruments and bimanual operation, thereby enhancing dissection efficiency and accuracy. Accurate prediction of dissection trajectories is crucial for better decision-making, reducing intraoperative errors, and improving surgical training. Nevertheless, predicting these trajectories is challenging due to variable tumor margins and dynamic visual conditions. To address this issue, we create the ESD Trajectory and Confidence Map-based Safety Margin (ETSM) dataset with 1849 short clips, focusing on submucosal dissection with a dual-arm robotic system. We also introduce a framework that combines optimal dissection trajectory prediction with a confidence map-based safety margin, providing a more secure and intelligent decision-making tool to minimize surgical risks for ESD procedures. Additionally, we propose the Regression-based Confidence Map Prediction Network (RCMNet), which utilizes a regression approach to predict confidence maps for dissection areas, thereby delineating various levels of safety margins. We evaluate our RCMNet using three distinct experimental setups: indomain evaluation, robustness assessment, and out-of-domain evaluation. Experimental results show that our approach excels in the confidence map-based safety margin prediction task, achieving a mean absolute error (MAE) of only 3.18. To the best of our knowledge, this is the first study to apply a regression approach for visual guidance concerning delineating varying safety levels of dissection areas. Our approach bridges gaps in current research by improving prediction accuracy and enhancing the safety of the dissection process, showing great clinical significance in practice. The dataset and code are available at https://github.com/FrankMOWJ/RCMNet.

Background:Several animal studies have demonstrated the safety and efficacy of a flexible auxiliary single-arm transluminal endoscopic robot in assisting gastric endoscopic submucosal dissection (ESD). This study aimed to assess its performance for the first time in a real clinical setting. Methods:We conducted this prospective, single-blind, pilot randomized controlled trial at a tertiary teaching hospital. Patients with gastric high grade intraepithelial neoplasia or intramucosal carcinoma were randomized to either robot-assisted or conventional ESD. The primary outcome was procedure time. Secondary outcomes included R0 and en bloc resection rates, dissection time, and procedure-related complications. Results:48 patients underwent successful procedures without perforation. Despite no significant difference in procedure time between robot-assisted and conventional ESD (58.21 vs. 39.21 minutes; P = 0.08), a learning curve effect was observed, with shorter procedure times in the last 12 cases with robot assistance compared with the last 12 conventional ESDs (56.82 vs. 63.10 minutes; P = 0.71). Muscular injuries were significantly reduced with robot assistance (1.50 vs. 9.00; P < 0.001). No significant difference was found in R0 (95.8% vs. 91.7%; P > 0.99) and en bloc (100% vs. 95.8%; P > 0.99) resection rates between robot-assisted and conventional ESD, respectively. Conclusions:This preliminary evidence supports the feasibility of robot-assisted gastric ESD.

Background: Pneumonia is one of the most common complications after lung resection. However, there are currently no reports of postoperative pneumonia in patients with bronchiectasis. Objectives: Our study aims to construct a new nomogram to predict the risk of postoperative pneumonia in patients with localized bronchiectasis. Design: The clinical data of patients with localized bronchiectasis from April 2012 to August 2022 were retrospectively analyzed. Methods: Independent risk factors were identified through simple linear regression and multiple linear regression analysis, and a new nomogram was constructed based on independent risk factors. The validity of the nomogram was evaluated using the consistency index (C-index), receiver operating characteristic curve, calibration chart, and decision curve analysis chart. Results: The new nomogram prediction model included five independent risk factors: tuberculosis history, smoking history, platelet–lymphocyte ratio (PLR), diffusing capacity of the lung for carbon monoxide, and controlled nutritional status score. The area under the curve of the prediction model is 0.870 (95% CI: 0.750–0.892), showing good discrimination ability, and the probability threshold was set at 0.2013. In addition, the calibration curve shows that the nomogram has good calibration. In the decision curve, the nomogram model showed good clinical net benefit. Conclusion: This study is the first to construct a nomogram prediction model for postoperative pneumonia of localized bronchiectasis, which can more accurately and directly assess the risk probability of postoperative pneumonia, and provide certain help for clinicians in prevention and treatment decisions.

The intricate nature of endoscopic surgical environments poses significant challenges for the task of dissection zone segmentation. Specifically, the boundaries between different tissue types lack clarity, which can result in significant segmentation errors, as the models may misidentify or overlook object edges altogether. Thus, the goal of this work is to achieve the precise dissection zone suggestion under these challenges during endoscopic submucosal dissection (ESD) procedures and enhance the overall safety of ESD. We introduce a prompted-based dissection zone segmentation (PDZSeg) model, aimed at segmenting dissection zones and specifically designed to incorporate different visual prompts, such as scribbles and bounding boxes. Our approach overlays these visual cues directly onto the images, utilizing fine-tuning of the foundational model on a specialized dataset created to handle diverse visual prompt instructions. This shift toward more flexible input methods is intended to significantly improve both the performance of dissection zone segmentation and the overall user experience. We evaluate our approaches using the three experimental setups: in-domain evaluation, evaluation under variability in visual prompts availability, and robustness assessment. By validating our approaches on the ESD-DZSeg dataset, specifically focused on the dissection zone segmentation task of ESD, our experimental results show that our solution outperforms state-of-the-art segmentation methods for this task. To the best of our knowledge, this is the first study to incorporate visual prompt design in dissection zone segmentation. We introduce the prompted-based dissection zone segmentation (PDZSeg) model, which is specifically designed for dissection zone segmentation and can effectively utilize various visual prompts, including scribbles and bounding boxes. This model improves segmentation performance and enhances user experience by integrating a specialized dataset with a novel visual referral method that optimizes the architecture and boosts the effectiveness of dissection zone suggestions. Furthermore, we present the ESD-DZSeg dataset for robot-assisted endoscopic submucosal dissection (ESD), which serves as a benchmark for assessing dissection zone suggestions and visual prompt interpretation, thus laying the groundwork for future research in this field. Our code is available at https://github.com/FrankMOWJ/PDZSeg .

Lung cancer patients undergoing surgery are at increased risk for Venous thromboembolism (VTE). We monitored changes in perioperative coagulation status through Thrombo-elastography (TEG), and monitored the anticoagulant effect of low molecular weight heparin through TEG for the first time. From July 2019 to January 2020, 207 patients receiving curative surgery were retrospectively screened. and 23 patients were excluded because they did not meet the inclusion criteria. Blood samples were required at three time points (prior to, the first and third day after surgery). Some patients were administrated nadroparin calcium daily from the first day after surgery. Repeated measures ANOVA and Chi-square test were used to analyze the coagulation states variation. To balance the confounders, propensity score matching (PSM) was used to determine the differences of coagulation states between patients with or without Low-molecular-weight heparin (LMWH) prophylaxis. In 184 patients, TEG parameters displayed significant procoagulant changes after lung surgery but conventional coagulation tests exhibited paradoxical trends. There were 6.5

In endoscopic submucosal dissection (ESD), the gastrointestinal (GI) tract warrants the surgical instruments to navigate through a long, narrow and tortuous endoscope. This poses a great challenge in developing ESD instruments with small dimensions, flexibility, and high distal dexterity. In this work, we propose the first Transendoscopic Flexible Parallel Continuum Robotic mechanism to develop a miniature dexterous flexible-stiff-balanced Wrist (FPCW). Besides, it can steer multifunctional instruments of diameters 2.5 mm to 3.5 mm, including the electrosurgical knife, injection needle, and forceps. Our FPCW instruments are adaptable to commercially available dual-channel endoscopes (diameter: <12 mm, channel width: 2.8 mm and around 3.8 mm). Furthermore, we develop a surgical telerobotic system, called DREAMS (Dual-arm Robotic Endoscopic Assistant for Minimally Invasive Surgery), by using our smallest FPCW instruments for bimanual ESD procedures. First, we conduct a series of experiments to determine the FPCW's design and kinematics parameters and to verify the mechanical properties of the FPCW instruments' prototypes, including workspace, stiffness, strength, and teleoperation accuracy. Second, we validate the functionality of the FPCW instruments through ex-vivo tests by performing ESD steps on porcine stomachs. Finally, we perform an invivo test on a live porcine model and showcase that our developed DREAMS can be teleoperated intuitively to perform bimanual ESD efficiently with an average dissection speed of 108.95 mm(2)/min at the greater curvature in gastric body, which demonstrates that our DREAMS has satisfactory maneuverability as well as accuracy and is more competitive than counterpart robotic systems.

In robotic-assisted surgery (RAS), traditional surgical instruments without sensing capability cannot perceive accurate operational forces during the task, and such drawbacks can be largely intensified when sophisticated tasks involving flexible and slender arms with small end-effectors, such as in gastrointestinal endoscopic surgery (GES). In this study, we propose a microelectromechanical system (MEMS) piezoresistive 3-axial tactile sensor for GES forceps, which can intuitively provide surgeons with online force feedback during robotic surgery. The MEMS fabrication process facilitates sensor chips with miniaturized dimensions. The fully encapsulated tactile sensors can be effortlessly integrated into miniature GES forceps, which feature a slender diameter of just 3.5 mm and undergo meticulous calibration procedures via the least squares method. Through experiments, the sensor's ability to accurately measure directional forces up to 1.2 N in the Z axis was validated, demonstrating an average relative error of only 1.18% compared with the full-scale output. The results indicate that this tactile sensor can provide effective 3-axial force sensing during surgical operations, such as grasping and pulling, and in ex vivo testing with a porcine stomach. The compact size, high precision, and integrability of the sensor establish solid foundations for clinical application in the operating theater.

The precise tracking and segmentation of surgical instruments have led to a remarkable enhancement in the efficiency of surgical procedures. However, the challenge lies in achieving accurate segmentation of surgical instruments while minimizing the need for manual annotation and reducing the time required for the segmentation process. To tackle this, we propose a novel framework for surgical instrument segmentation and tracking. Specifically, with a tiny subset of frames for segmentation, we ensure accurate segmentation across the entire surgical video. Our method adopts a two-stage approach to efficiently segment videos. Initially, we utilize the Segment-Anything (SAM) model, which has been fine-tuned using the Low-Rank Adaptation (LoRA) on the EndoVis17 Dataset. The fine-tuned SAM model is applied to segment the initial frames of the video accurately. Subsequently, we deploy the XMem++ tracking algorithm to follow the annotated frames, thereby facilitating the segmentation of the entire video sequence. This workflow enables us to precisely segment and track objects within the video. Through extensive evaluation of the in-distribution dataset (EndoVis17) and the out-of-distribution datasets (EndoVis18 & the endoscopic submucosal dissection surgery (ESD) dataset), our framework demonstrates exceptional accuracy and robustness, thus showcasing its potential to advance the automated robotic-assisted surgery.

BACKGROUND:Mucosal healing is one of the principal therapeutic targets for ulcerative colitis (UC). Mitochondria are dynamic organelles that undergo constant fusion and fission; however, the process that is most conducive to mucosal healing remains unclear. This study investigated the role of mitochondrial fission in mucosal healing in UC patients. METHODS:Quantitative polymerase chain reaction, Western blotting, and immunostaining were used to detect mitochondrial fission in UC patients and a dextran sulfate sodium-induced colitis model. Colonic organoids were used to investigate the role of mitochondrial fission in butyrate metabolism. Enzyme activity assays were performed to identify the key proteins involved in this mechanism. RESULTS:It was found that inhibition of mitochondrial fission promoted mucosal healing in mice and that there was an increase in mitochondrial fission in colonic epithelial cells of UC patients. Excessive fission inhibits stem cell proliferation by impairing butyrate metabolism in colonic organoids. The mitochondrial fission antagonist P110 failed to promote mucosal healing in antibiotic-treated mice, and the addition of exogenous butyrate reversed this effect. Increased butyrate exposure in the colonic stem cell niche has also been observed in UC patients. Mechanistically, enzyme activity assays on colonic organoids revealed that excessive fission inhibits mitochondrial acetoacetyl-CoA thiolase activity via reactive oxygen species. CONCLUSIONS:Collectively, these data indicate that excessive mitochondrial fission suppresses mucosal repair by inhibiting butyrate metabolism and provides a potential target for mucosal healing in patients with ulcerative colitis.

In the realm of automated robotic surgery and computer-assisted interventions, understanding robotic surgical activities stands paramount. Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios. Such algorithms often falter in the presence of test samples originating from classes unseen during training phases. To tackle this problem, we introduce an innovative Open-Set Surgical Activity Recognition (OSSAR) framework. Our solution leverages the hyperspherical reciprocal point strategy to enhance the distinction between known and unknown classes in the feature space. Additionally, we address the issue of over-confidence in the closed-set by refining model calibration, avoiding misclassification of unknown classes as known ones. To support our assertions, we establish an open-set surgical activity benchmark utilizing the public JIGSAWS dataset. Besides, we also collect a novel dataset on endoscopic submucosal dissection for surgical activity tasks. Extensive comparisons and ablation experiments on these datasets demonstrate the significant outperformance of our method over existing state-of-the-art approaches. Our proposed solution can effectively address the challenges of real-world surgical scenarios. Our code is publicly accessible at github.com/longbai1006/OSSAR.

Robot-assisted gastrointestinal endoscopic surgery requires flexible manipulators to possess a compact dimension and stiffness tuning capability. Current stiffness-tunable miniature manipulators (STMM) using tendon-sheath mechanism (TSM) experience the problem of stiffness-influenced hysteresis. To address this, we propose the first stiffness-dependent M odified G eneralized P randtl– I shlinski (PI) model and a specific compensation strategy. First, we analyzed the stiffness tuning mechanism and extracted the stiffness parameter. Based on this, the analytical hysteresis model and its inverse function were built and verified in simulations and experiments, which increases the fitting accuracy of the hysteresis. Then, with the assistance of the compensation strategy, the real-time input–output relationship of the STMM's stiffness-tunable joints can be approximately linear. The average errors of trajectory tracking achieve a significant improvement of over 85%. This work provides a new method to model and compensate for the dynamic hysteresis in flexible endoscopic robots with the stiffness-tunable TSM.

Background and Aim: The study aims to evaluate the feasibility of body mass index (BMI)-based individualized small bowel preparation for computed tomography enterography (CTE). Methods: In this prospective randomized controlled study, patients undergoing CTE were randomly assigned to the individualized group or standardized group. Those in individualized group were given different volumes of mannitol solution based on BMI (1000 mL for patients with BMI < 18.5 kg/m(2), 1500 mL for patients with 18.5 kg/m(2) <= BMI < 25 kg/m(2) and 2000 mL for patients with BMI >= 25 kg/m(2)) while patients in the standardized group were all asked to consume 1500-mL mannitol solution. CTE images were reviewed by two experienced radiologists blindly. Each segment of the small bowel was assessed for small bowel image quality and disease detection rates. Patients were invited to record a diary regarding adverse events and acceptance. Results: A total of 203 patients were enrolled and randomly divided into two groups. For patients with BMI < 18.5 kg/m(2), 1000-mL mannitol solution permitted a significantly lower rate of flatulence (P = 0.045) and defecating frequency (P = 0.011) as well as higher acceptance score (P = 0.015), but did not affect bowel image quality and diseases detection compared with conventional dosage. For patients with BMI >= 25 kg/m(2), 2000-mL mannitol solution provided better overall image quality (P = 0.033) but comparable rates of adverse events and patients' acceptance compared with conventional dosage. Conclusions: Individualized bowel preparation could achieve both satisfactory image quality and patients' acceptance thus might be an acceptable alternative in CTE.

Advances in endoscopy use in surgeries face challenges like inadequate lighting. Deep learning, notably the Denoising Diffusion Probabilistic Model (DDPM), holds promise for low-light image enhancement in the medical field. However, DDPMs are computationally demanding and slow, limiting their practical medical applications. To bridge this gap, we propose a lightweight DDPM, dubbed LighTDiff. It adopts a T-shape model architecture to capture global structural information using low-resolution images and gradually recover the details in subsequent denoising steps. We further prone the model to significantly reduce the model size while retaining performance. While discarding certain downsampling operations to save parameters leads to instability and low efficiency in convergence during the training, we introduce a Temporal Light Unit (TLU), a plug-and-play module, for more stable training and better performance. TLU associates time steps with denoised image features, establishing temporal dependencies of the denoising steps and improving denoising outcomes. Moreover, while recovering images using the diffusion model, potential spectral shifts were noted. We further introduce a Chroma Balancer (CB) to mitigate this issue. Our LighTDiff outperforms many competitive LLIE methods with exceptional computational efficiency.