Better Rough than Scarce: Proximal Femur Fracture Segmentation with Rough Annotations.

Proximal femoral fracture segmentation in computed tomography (CT) is essential in the preoperative planning of orthopedic surgeons. Recently, numerous deep learning-based approaches have been proposed for segmenting various structures within CT scans. Nevertheless, distinguishing various attributes between fracture fragments and soft tissue regions in CT scans frequently poses challenges, which have received comparatively limited research attention. Besides, the cornerstone of contemporary deep learning methodologies is the availability of annotated data, while detailed CT annotations remain scarce. To address the challenge, we propose a novel weakly-supervised framework, namely Rough Turbo Net (RT-Net), for the segmentation of proximal femoral fractures. We emphasize the utilization of human resources to produce rough annotations on a substantial scale, as opposed to relying on limited fine-grained annotations that demand a substantial time to create. In RT-Net, rough annotations pose fractured-region constraints, which have demonstrated significant efficacy in enhancing the accuracy of the network. Conversely, the fine annotations can provide more details for recognizing edges and soft tissues. Besides, we design a spatial adaptive attention module (SAAM) that adapts to the spatial distribution of the fracture regions and align feature in each decoder. Moreover, we propose a fine-edge loss which is applied through an edge discrimination network to penalize the absence or imprecision edge features. Extensive quantitative and qualitative experiments demonstrate the superiority of RT-Net to state-of-the-art approaches. Furthermore, additional experiments show that RT-Net has the capability to produce pseudo labels for raw CT images that can further improve fracture segmentation performance and has the potential to improve segmentation performance on public datasets. The code is available at: https://github.com/zyairelu/RT-Net.

Dietary rutin improves the antidiarrheal capacity of weaned piglets by improving intestinal barrier function, antioxidant capacity and cecal microbiota composition.

BACKGROUND: Early weaning is prone to damage intestinal barrier function, resulting in diarrhea, whereas rutin, as a natural flavonoid with multiple biological functions, shows potential in piglets. Therefore, the effects of dietary rutin on growth, antidiarrheal, barrier function, antioxidant status and cecal microbiota of weaned piglets were investigated with the control group (CON) (basal diet) and Rutin (basal diet+500 mg kg-1 rutin) groups fed for 14 days. RESULTS: The results showed that dietary 500 mg kg-1 rutin significantly decreased diarrhea index, serum diamine oxidase activity and total aerobic bacterial population in mesenteric lymph nodes, whereas it significantly increased the gain-to-feed ratio (G:F) and serum growth hormone content, jejunal villus height and villus height to crypt depth ratio, and also enhanced jejunal claudin-1 and zonula occludens-1 mRNA and protein expression. Meanwhile, dietary rutin significantly decreased inflammation-associated mRNA expression, malondialdehyde (MDA) content, swollen mitochondrial number and mitochondrial area in the jejunum, whereas it increased the total superoxide dismutase (T-SOD) and glutathione peroxidase activities and activated the Nrf2 signaling pathway. Moreover, dietary rutin significantly increased Firmicutes abundance and decreased Campylobacterota abundance, which were closely associated with the decreased diarrhea index and MDA content or increased Claudin-1 expression and T-SOD activity. CONCLUSION: Dietary 500 mg kg-1 rutin increased G:F by improving intestinal morphology, and alleviated diarrhea by enhancing intestinal barrier, which might be associated with the enhanced antioxidant capacity via activating the Nrf2/Keap1 signaling pathway and the improved cecal microbial composition in weaned piglets. © 2024 Society of Chemical Industry.

Comprehensive advancement in endoscopy: optical design, algorithm enhancement, and clinical validation for merged WLI and CBI imaging.

As endoscopic imaging technology advances, there is a growing clinical demand for enhanced imaging capabilities. Although conventional white light imaging (WLI) endoscopy offers realistic images, it often cannot reveal detailed characteristics of the mucosa. On the other hand, optical staining endoscopy, such as Compound Band Imaging (CBI), can discern subtle structures, serving to some extent as an optical biopsy. However, its image brightness is low, and the colors can be abrupt. These two techniques, commonly used in clinical settings, have complementary advantages. Nonetheless, they require different lighting conditions, which makes it challenging to combine their imaging strengths on living tissues. In this study, we introduce a novel endoscopic imaging technique that effectively combines the advantages of both WLI and CBI. Doctors don't need to manually switch between these two observation modes, as they can obtain the image information of both modes in one image. We calibrated an appropriate proportion for simultaneous illumination with the light required for WLI and CBI. We designed a new illumination spectrum tailored for gastrointestinal examination, achieving their fusion at the optical level. Using a new algorithm that focuses on enhancing specific hemoglobin tissue features, we restored narrow-band image characteristics lost due to the introduction of white light. Our hardware and software innovations not only boost the illumination brightness of the endoscope but also ensure the narrow-band feature details of the image. To evaluate the reliability and safety of the new endoscopic system, we conducted a series of tests in line with relevant international standards and validated the design parameters. For clinical trials, we collected a total of 256 sets of images, each set comprising images of the same lesion location captured using WLI, CBI, and our proposed method. We recruited four experienced clinicians to conduct subjective evaluations of the collected images. The results affirmed the significant advantages of our method. We believe that the novel endoscopic system we introduced has vast potential for clinical application in the future.

Extent of surgical resection for radiologically subsolid T1N0 invasive lung adenocarcinoma: When is a wedge resection acceptable?

OBJECTIVE: To evaluate whether wedge resection (WR) was appropriate for the patients with peripheral T1 N0 solitary subsolid invasive lung adenocarcinoma. METHODS: Patients with peripheral T1N0 solitary subsolid invasive lung adenocarcinoma who received sublobar resection were retrospectively reviewed. Clinicopathologic characteristics, 5-year recurrence-free survival, and 5-year lung cancer-specific overall survival were analyzed. Cox regression model was used to elucidate risk factors for recurrence. RESULTS: Two hundred fifty-eight patients receiving WR and 1245 patients receiving segmentectomy were included. The mean follow-up time was 36.87 ± 16.21 months. Five-year recurrence-free survival following WR was 96.89% for patients with ground-glass nodule (GGN) ≤2 cm and 0.25< consolidation-to-tumor ratio (CTR) ≤0.5, not statistically different from 100% for those with GGN≤2 cm and CTR ≤0.25 (P = .231). The 5-year recurrence-free survival was 90.12% for patients with GGN between 2 and 3 cm and CTR ≤0.5, significantly lower than that of patients with GGN ≤2 cm and CTR ≤0.25 (P = .046). For patients with GGN≤2 cm and 0.25

A Monocular Variable Magnifications 3D Laparoscope System Using Double Liquid Lenses.

During minimal invasive surgery (MIS), the laparoscope only provides a single viewpoint to the surgeon, leaving a lack of 3D perception. Many works have been proposed to obtain depth and 3D reconstruction by designing a new optical structure or by depending on the camera pose and image sequences. Most of these works modify the structure of the conventional laparoscopes and cannot provide 3D reconstruction of different magnification views. In this study, we propose a laparoscopic system based on double liquid lenses, which provide doctors with variable magnification rates, near observation, and real-time monocular 3D reconstruction. Our system composes of an optical structure that can obtain auto magnification change and autofocus without any physically moving element, and a deep learning network based on the Depth from Defocus (DFD) method, trained to suit inconsistent camera intrinsic situations and estimate depth from images of different focal lengths. The optical structure is portable and can be mounted on conventional laparoscopes. The depth estimation network estimates depth in real-time from monocular images of different focal lengths and magnification rates. Experiments show that our system provides a 0.68-1.44x zoom rate and can estimate depth from different magnification rates at 6fps. Monocular 3D reconstruction reaches at least 6mm accuracy. The system also provides a clear view even under 1mm close working distance. Ex-vivo experiments and implementation on clinical images prove that our system provides doctors with a magnified clear view of the lesion, as well as quick monocular depth perception during laparoscopy, which help surgeons get better detection and size diagnosis of the abdomen during laparoscope surgeries.

Viewpoint-dependent highlight depiction with microdisparity for autostereoscopic displays.

The rendering of specular highlights is a critical aspect of 3D rendering on autostereoscopic displays. However, the conventional highlight rendering techniques on autostereoscopic displays result in depth conflicts between highlights and diffuse surfaces. To address this issue, we propose a viewpoint-dependent highlight depiction method with head tracking, which incorporates microdisparity of highlights in binocular parallax and preserves the motion parallax of highlights. Our method was found to outperform physical highlight depiction and highlight depiction with microdisparity in terms of depth perception and realism, as demonstrated by experimental results. The proposed approach offers a promising alternative to traditional physical highlights on autostereoscopic displays, particularly in applications that require accurate depth perception.

Double-attention Assisted Multi-task Learning for the Alzheimer's Disease Prediction from Mild Cognitive Impairment.

Alzheimer's disease (AD) is a progressive neurode-generative disease. Identifying the mild cognitive impairment (MCI) subjects who will convert to AD is essential for early intervention to slow the irreversible brain damage and cognitive decline. In this paper, we propose a novel double-attention assisted multi-task framework for the MCI conversion prediction task. By introducing an auxiliary grey matter segmentation task along with an adaptive dynamic weight average strategy to balance the impact of each task. Then, a double-attention module is incorporated to leverage both the classification and the segmentation attention information to guide the network to focus more on the structural alteration regions for better discrimination of AD pathology, as well as increase the interpretability of the network. Extensive experiments on a publicly available dataset demonstrate that the proposed method significantly outperforms the approaches using the same image modality.

Diffusion MRI is valuable in brainstem glioma genotyping with quantitative measurements of white matter tracts.

OBJECTIVES: To investigate the value of diffusion MRI (dMRI) in H3K27M genotyping of brainstem glioma (BSG). METHODS: A primary cohort of BSG patients with dMRI data (b = 0, 1000 and 2000 s/mm2) and H3K27M mutation information were included. A total of 13 diffusion tensor and kurtosis imaging (DTI; DKI) metrics were calculated, then 17 whole-tumor histogram features and 29 along-tract white matter (WM) microstructural measurements were extracted from each metric and assessed within genotypes. After feature selection through univariate analysis and the least absolute shrinkage and selection operator method, multivariate logistic regression was used to build dMRI-derived genotyping models based on retained tumor and WM features separately and jointly. Model performances were tested using ROC curves and compared by the DeLong approach. A nomogram incorporating the best-performing dMRI model and clinical variables was generated by multivariate logistic regression and validated in an independent cohort of 27 BSG patients. RESULTS: At total of 117 patients (80 H3K27M-mutant) were included in the primary cohort. In total, 29 tumor histogram features and 41 WM tract measurements were selected for subsequent genotyping model construction. Incorporating WM tract measurements significantly improved diagnostic performances (p < 0.05). The model incorporating tumor and WM features from both DKI and DTI metrics showed the best performance (AUC = 0.9311). The nomogram combining this dMRI model and clinical variables achieved AUCs of 0.9321 and 0.8951 in the primary and validation cohort respectively. CONCLUSIONS: dMRI is valuable in BSG genotyping. Tumor diffusion histogram features are useful in genotyping, and WM tract measurements are more valuable in improving genotyping performance. CLINICAL RELEVANCE STATEMENT: This study found that diffusion MRI is valuable in predicting H3K27M mutation in brainstem gliomas, which is helpful to realize the noninvasive detection of brainstem glioma genotypes and improve the diagnosis of brainstem glioma. KEY POINTS: • Diffusion MRI has significant value in brainstem glioma H3K27M genotyping, and models with satisfactory performances were built. • Whole-tumor diffusion histogram features are useful in H3K27M genotyping, and quantitative measurements of white matter tracts are valuable as they have the potential to improve model performance. • The model combining the most discriminative diffusion MRI model and clinical variables can help make clinical decision.

A Segmentation Framework With Unsupervised Learning-Based Label Mapper for the Ventricular Target of Intracranial Germ Cell Tumor.

Intracranial germ cell tumors are rare tumors that mainly affect children and adolescents. Radiotherapy is the cornerstone of interdisciplinary treatment methods. Radiation of the whole ventricle system and the local tumor can reduce the complications in the late stage of radiotherapy while ensuring the curative effect. However, manually delineating the ventricular system is labor-intensive and time-consuming for physicians. The diverse ventricle shape and the hydrocephalus-induced ventricle dilation increase the difficulty of automatic segmentation algorithms. Therefore, this study proposed a fully automatic segmentation framework. Firstly, we designed a novel unsupervised learning-based label mapper, which is used to handle the ventricle shape variations and obtain the preliminary segmentation result. Then, to boost the segmentation performance of the framework, we improved the region growth algorithm and combined the fully connected conditional random field to optimize the preliminary results from both regional and voxel scales. In the case of only one set of annotated data is required, the average time cost is 153.01 s, and the average target segmentation accuracy can reach 84.69%. Furthermore, we verified the algorithm in practical clinical applications. The results demonstrate that our proposed method is beneficial for physicians to delineate radiotherapy targets, which is feasible and clinically practical, and may fill the gap of automatic delineation methods for the ventricular target of intracranial germ celltumors.

Advances in deep learning: From diagnosis to treatment.

Deep learning has brought about a revolution in the field of medical diagnosis and treatment. The use of deep learning in healthcare has grown exponentially in recent years, achieving physician-level accuracy in various diagnostic tasks and supporting applications such as electronic health records and clinical voice assistants. The emergence of medical foundation models, as a new approach to deep learning, has greatly improved the reasoning ability of machines. Characterized by large training datasets, context awareness, and multi-domain applications, medical foundation models can integrate various forms of medical data to provide user-friendly outputs based on a patien's information. Medical foundation models have the potential to integrate current diagnostic and treatment systems, providing the ability to understand multi-modal diagnostic information and real-time reasoning ability in complex surgical scenarios. Future research on foundation model-based deep learning methods will focus more on the collaboration between physicians and machines. On the one hand, developing new deep learning methods will reduce the repetitive labor of physicians and compensate for shortcomings in their diagnostic and treatment capabilities. On the other hand, physicians need to embrace new deep learning technologies, comprehend the principles and technical risks of deep learning methods, and master the procedures for integrating them into clinical practice. Ultimately, the integration of artificial intelligence analysis with human decision-making will facilitate accurate personalized medical care and enhance the efficiency of physicians.

Diffusion MRI-based connectomics features improve the noninvasive prediction of H3K27M mutation in brainstem gliomas.

PURPOSE: To establish an individualized predictive model to identify patients with brainstem gliomas (BSGs) at high risk of H3K27M mutation, with the inclusion of brain structural connectivity analysis based on diffusion MRI (dMRI). MATERIALS AND METHODS: A primary cohort of 133 patients with BSGs (80 H3K27M-mutant) were retrospectively included. All patients underwent preoperative conventional MRI and dMRI. Tumor radiomics features were extracted from conventional MRI, while two kinds of global connectomics features were extracted from dMRI. A machine learning-based individualized H3K27M mutation prediction model combining radiomics and connectomics features was generated with a nested cross validation strategy. Relief algorithm and SVM method were used in each outer LOOCV loop to select the most robust and discriminative features. Additionally, two predictive signatures were established using the LASSO method, and simplified logistic models were built using multivariable logistic regression analysis. An independent cohort of 27 patients was used to validate the best model. RESULTS: 35 tumor-related radiomics features, 51 topological properties of brain structural connectivity networks, and 11 microstructural measures along white matter tracts were selected to construct a machine learning-based H3K27M mutation prediction model, which achieved an AUC of 0.9136 in the independent validation set. Radiomics- and connectomics-based signatures were generated and simplified combined logistic model was built, upon which derived nomograph achieved an AUC of 0.8827 in the validation cohort. CONCLUSION: dMRI is valuable in predicting H3K27M mutation in BSGs, and connectomics analysis is a promising approach. Combining multiple MRI sequences and clinical features, the established models have good performance.

Frequency constraint-based adversarial attack on deep neural networks for medical image classification.

The security of AI systems has gained significant attention in recent years, particularly in the medical diagnosis field. To develop a secure medical image classification system based on deep neural networks, it is crucial to design effective adversarial attacks that can embed hidden, malicious behaviors into the system. However, designing a unified attack method that can generate imperceptible attack samples with high content similarity and be applied to diverse medical image classification systems is challenging due to the diversity of medical imaging modalities and dimensionalities. Most existing attack methods are designed to attack natural image classification models, which inevitably corrupt the semantics of pixels by applying spatial perturbations. To address this issue, we propose a novel frequency constraint-based adversarial attack method capable of delivering attacks in various medical image classification tasks. Specially, our method introduces a frequency constraint to inject perturbation into high-frequency information while preserving low-frequency information to ensure content similarity. Our experiments include four public medical image datasets, including a 3D CT dataset, a 2D chest X-Ray image dataset, a 2D breast ultrasound dataset, and a 2D thyroid ultrasound dataset, which contain different imaging modalities and dimensionalities. The results demonstrate the superior performance of our model over other state-of-the-art adversarial attack methods for attacking medical image classification tasks on different imaging modalities and dimensionalities.

Spinal CBX2 contributes to neuropathic pain by activating ERK signaling pathway in male mice.

The deregulated spinal cord proteins induced by nerve injury are the key to neuropathic pain. Integrated transcriptome and translatome analyses can screen out deregulated proteins controlled by only post-transcriptional regulation. By comparing RNA sequencing (RNA-seq) and ribosome profiling sequencing (Ribo-seq) data, we identified an upregulated protein, chromobox 2 (CBX2), with its mRNA level unchanged in the spinal cord after peripheral nerve injury. CBX2 was mainly distributed in the spinal cord neurons. Blocking the SNL-induced increase of spinal CBX2 attenuated the neuronal and astrocytes hyperactivities and pain hypersensitivities in both the development and maintenance phases. Conversely, mimicking the upregulation of CBX2 in the spinal cord facilitated the activities of neurons and astrocytes and produced evoked nociceptive hypersensitivity and spontaneous pain. Our results also revealed that activating the ERK pathway, upregulating CXCL13 in neurons, and CXCL13 further inducing astrocyte activation were possible downstream signaling mechanisms of CBX2 in pain processing. In conclusion, upregulation of CBX2 after nerve injury leads to nociceptive hyperalgesia by promoting neuronal and astrocyte hyperactivities through the ERK pathway. Inhibiting CBX2 upregulation may be therapeutically beneficial.

Effects of Dietary Nano-Zinc Oxide Supplementation on Meat Quality, Antioxidant Capacity and Cecal Microbiota of Intrauterine Growth Retardation Finishing Pigs.

As nano-zinc oxide (Nano-ZnO), a new type of nanomaterial, has antioxidant and intestinal protection effects, we hypothesized that dietary Nano-ZnO could modulate poor meat quality, oxidative stress and disturbed gut microbiota in the finishing pig model of naturally occurring intrauterine growth retardation (IUGR). A total of 6 normal-born weight (NBW) and 12 IUGR piglets were selected based on birth weight. The pigs in the NBW group received a basal diet, and IUGR pigs were randomly divided into two groups and treated with basal diet and 600 mg/kg Nano-ZnO-supplemented diet. Dietary Nano-ZnO ameliorated IUGR-associated declined meat quality by lowering the drip loss48h, cooking loss, shearing force and MyHc IIx mRNA expression, and raising the redness (a*), peak area ratio of immobilized water (P22), sarcomere length and MyHc Ia mRNA expression. Nano-ZnO activated the nuclear factor erythroid 2-related factor 2-glutamyl cysteine ligase (Nrf2-GCL) signaling pathway by promoting the nuclear translocation of Nrf2, increasing the GCL activities, and mRNA and protein expression of its catalytic/modify subunit (GCLC/GCLM), thereby attenuating the IUGR-associated muscle oxidative injury. Additionally, the composition of IUGR pigs' cecal microbiota was altered by Nano-ZnO, as seen by changes in Shannon and Simpson indexes, the enhanced UCG-005, hoa5-07d05 gut group and Rikenellaceae RC9 gut group abundance. The UCG-005 and hoa5-07d05 gut group abundance were correlated with indicators that reflected the meat quality traits and antioxidant properties. In conclusion, Nano-ZnO improved the IUGR-impaired meat quality by altering water holding capacity, water distribution and the ultrastructure of muscle, activating the Nrf2-GCL signaling pathway to alleviate oxidative status and regulating the cecal microbial composition.

The G allele of SNP rs3922 reduces the binding affinity between IGF2BP3 and CXCR5 correlating with a lower antibody production.

Effective vaccines that function through humoral immunity seek to produce high-affinity antibodies. Our previous research identified the single-nucleotide polymorphism rs3922G in the 3'UTR of CXCR5 as being associated with nonresponsiveness to the hepatitis B vaccine. The differential expression of CXCR5 between the dark zone (DZ) and light zone (LZ) is critical for organizing the functional structure of the germinal center (GC). In this study, we report that the RNA-binding protein IGF2BP3 can bind to CXCR5 mRNA containing the rs3922 variant to promote its degradation via the nonsense-mediated mRNA decay pathway. Deficiency of IGF2BP3 leads to increased CXCR5 expression, which results in the disappearance of CXCR5 differential expression between DZ and LZ, disorganized GCs, aberrant somatic hypermutations, and reduced production of high-affinity antibodies. Furthermore, the affinity of IGF2BP3 for the rs3922G-containing sequence is lower than that for the rs3922A counterpart, which may explain the nonresponsiveness to the hepatitis B vaccination. Together, our findings suggest that IGF2BP3 plays a crucial role in the production of high-affinity antibodies in the GC by binding to the rs3922-containing sequence to regulate CXCR5 expression.

A stiffness-tunable soft actuator inspired by helix for medical applications.

PURPOSE: This paper introduces the stiffness-tunable soft actuator (STSA), a novel device that combines a silicone body with a thermoplastic resin structure (TPRS). The STSA's design allows for the variable stiffness of soft robots, significantly increasing their potential for use in medical scenarios such as minimally invasive surgeries (MIS). By adjusting the stiffness of the STSA, it is possible to enhance the robot's dexterity and adaptability, making it a promising tool for performing complex tasks in narrow and delicate spaces. METHODS: The stiffness of the STSA can be modulated by altering the temperature of the TPRS, which has been inspired by the helix and is integrated into the soft actuator to achieve a broad range of stiffness modulation while maintaining flexibility. The STSA has been designed with both diagnostic and therapeutic functions in mind, with the hollow area of the TPRS serving as an instrument channel for delivering surgical instruments. Additionally, the STSA features three uniformly arranged pipelines for actuation by air or tendon, and can be expanded with more functional chambers for endoscopy, illumination, water injection, and other purposes. RESULTS: Experimental results show that the STSA can achieve a maximum 30-fold stiffness tuning, providing a significant improvement in load capacity and stability when compared to pure soft actuators (PSAs). Of particular importance, the STSA is capable of achieving stiffness modulation below 45 °C, thereby ensuring a safe entry into the human body and creating an environment conducive to the normal operation of surgical instruments such as endoscope. CONCLUSION: The experimental findings indicate that the soft actuator with TPRS can achieve a broad range of stiffness modulation while retaining flexibility. Moreover, the STSA can be designed to have a diameter of 8-10 mm, which satisfies the diameter requirements of a bronchoscope. Furthermore, the STSA has the potential to be utilized for clamping and ablation in a laparoscopic scenario, thereby demonstrating its potential for clinical use. Overall, these results suggest that the STSA has significant promise for use in medical applications, particularly in the context of minimally invasive surgeries.

Comparative life cycle assessment of ammonia production by coke oven gas via single and coproduction processes.

As an abundant H2-rich byproduct from coking production, coke oven gas (COG) is a favorable feedstock for ammonia production. Recently, three COG-based ammonia processes have been applied, including single process, coproduction of ammonia with methanol, and coproduction of ammonia with liquefied natural gas (LNG). To systematically evaluate the environmental impacts of three COG routes, a comparative life cycle assessment was conducted with industrial data. Besides, the effects of ammonia synthesis pressure and electricity sources to the total LCA result were discussed. The results indicate that the environmental impacts of COG-based single ammonia route are mainly generated from ammonia production stage, accounting for 69.63 % of the overall normalized results, in which electricity and COG are the dominated contributors. Therefore, employing electricity from renewables like wind, solar, hydro and nuclear could dramatically mitigate the environmental impacts with a reduction of 36.3 %-70.7 % in most environmental indicators. Scenario analysis proves that reducing synthesis pressure from 31.4 MPa to 15 MPa does not show remarkable environmental benefits as expected since higher pressure is more conducive to ammonia synthesis. In comparison with coal based and natural gas-based ammonia routes, COG routes have obvious energy-saving benefit. In three COG-based ammonia routes, the two coproduction routes accounted for 49.1 % and 78.6 % of the energy depletion as single production due to highly efficient utilization of resources and energy. Coproduction of ammonia with methanol route exhibits better environmental performance than these in coproduction of ammonia with LNG route. Therefore, coproduction of ammonia with methanol route is more favorable in COG to ammonia processes. This study intends to provide a valuable reference for COG utilization and ammonia production options through the life cycle aspect.

A sensory neuron-specific long non-coding RNA reduces neuropathic pain by rescuing KCNN1 expression.

Nerve injury to peripheral somatosensory system causes refractory neuropathic pain. Maladaptive changes of gene expression in primary sensory neurons are considered molecular basis of this disorder. Long non-coding RNAs (lncRNAs) are key regulators of gene transcription; however, their significance in neuropathic pain remains largely elusive.Here, we reported a novel lncRNA, named sensory neuron-specific lncRNA (SS-lncRNA), for its expression exclusively in dorsal root ganglion (DRG) and trigeminal ganglion. SS-lncRNA was predominantly expressed in small DRG neurons and significantly downregulated due to a reduction of early B cell transcription factor 1 in injured DRG after nerve injury. Rescuing this downregulation reversed a decrease of the calcium-activated potassium channel subfamily N member 1 (KCNN1) in injured DRG and alleviated nerve injury-induced nociceptive hypersensitivity. Conversely, DRG downregulation of SS-lncRNA reduced the expression of KCNN1, decreased total potassium currents and afterhyperpolarization currents and increased excitability in DRG neurons and produced neuropathic pain symptoms.Mechanistically, downregulated SS-lncRNA resulted in the reductions of its binding to Kcnn1 promoter and heterogeneous nuclear ribonucleoprotein M (hnRNPM), consequent recruitment of less hnRNPM to the Kcnn1 promoter and silence of Kcnn1 gene transcription in injured DRG.These findings indicate that SS-lncRNA may relieve neuropathic pain through hnRNPM-mediated KCNN1 rescue in injured DRG and offer a novel therapeutic strategy specific for this disorder.

A Comparative Evaluation of Optical See-through Augmented Reality in Surgical Guidance.

During traditional surgeries, planning and instrument guidance is displayed on an external screen. Recent developments of augmented reality (AR) techniques can overcome obstacles including hand-eye discoordination and heavy mental load. Among these AR technologies, optical see-through (OST) schemes with stereoscopic displays can provide depth perception and retain the physical scene for safety considerations. However, limitations still exist in certain AR systems and the influence of these factors on surgical performance is yet to explore. To this end, experiments of multi-scale surgical tasks were carried out to compare head-mounted display (HMD) AR and autostereoscopic image overlay (AIO) AR, concerning objective performance and subjective evaluation. To solely analyze effects brought by display techniques, the tracking system in each included display system was identical and similar tracking accuracy was proved by a preliminary experiment. Focus and context rendering was utilized to enhance in-situ visualization for surgical guidance. Latency values of all display systems were assessed and a delay experiment proved the latency differences had no significant impact on user performance. Results of multi-scale surgical tasks showed that HMD outperformed in detailed operations probably due to stable resolution along the depth axis, while AIO had better performance in larger-scale operations for better depth perception. This paper helps point out the critical limitations of current OST AR techniques and potentially promotes the progress of AR applications in surgical guidance.

Extensive clinical target volume in postoperative chemoradiotherapy for esophageal squamous cell carcinoma: a phase II clinical trial (ESO-Shanghai 9).

BACKGROUND: To compare the efficacy and safety of postoperative extensive target volume irradiation with elevated radiation dose and concurrent chemotherapy with radiotherapy only for the postoperative treatment of esophageal squamous cell carcinoma. METHODS: This trial was a single-arm phase II trial. Patients who underwent a radical transthoracic resection with negative margins within 3 months and histologically confirmed esophageal squamous cell carcinoma (pT3-4N0M0 or pTxN + M0, AJCC 7th) were eligible for this study. Postoperative radiotherapy was performed at a total dose of 45 Gy in 25 fractions with clinical target volumes of the tumor bed, anastomosis, bilateral supraclavicular, mediastinal, left gastric and celiac trunk lymph node areas. Five cycles of weekly TC (paclitaxel 50 mg/m2, d1, carboplatin AUC = 2, d1) were given as concurrent chemotherapy. The primary endpoint was the 2-year local control rate, and the secondary endpoints were overall survival, disease free survival, local-regional recurrence free survival, distant metastasis free survival and adverse events. All endpoints were compared with those in ESO-Shanghai 8 study with postoperative radiotherapy alone (40 Gy/20Fx). RESULTS: A total of 70 patients were enrolled from 2016 to 2018. The 2-year local control rate was 87.9% (95% CI: 83.3-92.3) in this study, which achieved the hypothesized 2-year local control rate of at least 83%. Overall survival, disease free survival, local-regional recurrence free survival and distant metastasis free survival in this study were also longer than those in previous ESO-Shanghai 8 study while most toxicities were increased and two patients in this study died of radiation pneumonitis. CONCLUSIONS: Postoperative extensive target volume irradiation with elevated radiation dose and concurrent chemotherapy was effective. Treatment related toxicity was increased due to higher treatment intensity. Trial registration clinicaltrials.gov: NCT02916511.