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Abstract Background Rectal cancer is a common cancer worldwide and lacks effective prognostic markers. The development of prognostic markers by computational pathology methods has attracted increasing attention. This paper aims to construct a prognostic signature from whole slide images for predicting progression-free survival (PFS) of rectal cancer through an unsupervised artificial intelligence algorithm. Methods A total of 238 patients with rectal cancer from two datasets were collected for the development and validation of the prognostic signature. A tumor detection model was built by transfer learning. Then, on the basis of the tumor patches recognized by the tumor detection model, a convolutional autoencoder model was built for decoding the tumor patches into deep latent features. Next, on the basis of the deep latent features, the tumor patches were divided into different clusters. The cluster number and other hyperparameters were optimized by a nested cross-validation method. The percentage of each cluster from the patient’s tumor patches, which is hereafter called PCF, was calculated for prognostic signature construction. The prognostic signature was constructed by Cox proportional hazard regression with L2 regularization. Finally, bioinformatic analysis was performed to explore the underlying biological mechanisms of the PCFs. Results The accuracy of the tumor detection model in distinguishing tumor patches from non-tumor patches achieved 99.3%. The optimal cluster number was determined to be 9. Therfore, 9 PCFs were calculated to construct the prognostic signature. The prognostic signature achieved a concordance index of 0.701 in the validation cohort. The Kaplan-Meier survival curves showed the prognostic signature had good risk stratification ability. Through the bioinformatic analysis, several PCF-associated genes were identified. These genes were enriched in various gene ontology terms. Conclusion The developed prognostic signature can effectively predict PFS in patients with rectal cancer and exploration of the underlying biological mechanisms may help to promote its clinical translation.

The current standard of care for anal squamous cell carcinoma (ASCC) is definitive concurrent chemoradiotherapy (CRT). However, about a third of patients may experience treatment failure. Recently, immunotherapy has emerged as a novel strategy for metastatic ASCC patients. We evaluated the efficacy and safety of surgery, CRT alone, and CRT with immunotherapy (CRT-I) in 100 nonmetastatic ASCC patients, treated from April 2012 through May 2023, by determining survival outcomes and acute adverse events. The median (range) follow-up was 30.7 (7.6 to 134.9) months. The study cohort 3-year overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and locoregional recurrence-free survival (LRFS) rates were 80.7 %, 62.2 %, 71.1 %, and 67.6 %, respectively. The Surgery group had significantly lower rates than the CRT and CRT-I groups for 3-year PFS (33.1% vs. 65.2% vs. 92.9 %, P < 0.001), DMFS (46.7% vs. 74.6% vs. 92.9 %, P = 0.002) and LRFS (37.0% vs. 73.3% vs. 92.9 %, P < 0.001), respectively. All patients receiving CRT-I were alive at last follow-up. Of 100 patients, 26 (26.0 %) experienced severe (≥ grade 3) acute toxicity. Of 24 patients receiving CRT-I, 8 (33.3 %) had severe acute toxicity. Using immunohistochemistry, peritumoural stromal infiltration by CD8+ T cells was significantly higher after CRT-I compared to before CRT-I and to after CRT alone. The addition of immunotherapy to CRT may be an effective first-line treatment option with favourable survival outcomes and acceptable toxicity for patients with ASCC. A prospective, randomized trial assessing the efficacy of CRT combined with a PD-1 inhibitor in patients with locally advanced ASCC is in progress.

Abstract Liver cancer is one of the most common malignant tumors worldwide. Although some progress has been made in the diagnosis and treatment of Hepatocellular carcinoma (HCC), the diagnosis and treatment of HCC is still facing great challenges because of the high mortality rate and poor prognosis of HCC. The purpose of this study was to explore the relationship between adhesion-regulating molecule1 (ADRM1), and liver cancer, and the relationship between prognoses. ADRM1 is highly expressed in tumors and is closely associated with the prognosis of patients with liver cancer. In our previous study, we found that ADRM1 was highly expressed in HCC and was closely related to tumor immune and immune checkpoint levels in HCC. We validated the immune expression of ADRM1 in liver cancer cells using flow cytometry. In hepatocellular carcinoma tissues, miR-891a-5p regulates ADRM1. Upregulation of miR-891a-5p upregulates ADRM1, and downregulation of miR-891a-5p downregulates ADRM1. It is suggested that ADRM1 plays a key role in the occurrence and development of hepatocellular carcinoma. This study is expected to provide new ideas for the research and development of anti-HCC drugs targeting miR-891a-5p/ADRM1. However, further trials are needed to confirm these results and explore the actual results in patients with HCC.

Abstract Background Patients with DNA mismatch repair‐proficient/microsatellite stable (pMMR/MSS) colorectal cancer (CRC), which accounts for 85% of all CRC cases, display a poor respond to immune checkpoint inhibitors (i.e., anti‐PD‐1 antibodies). pMMR/MSS CRC patients with locally advanced cancers need effective combined therapies. Methods In this pilot study, we administered six preoperative doses of each 2‐week cycle of the anti‐PD‐1 antibody sintilimab (at a fixed dose of 200 mg), oxaliplatin, and 5‐FU/CF (mFOLFOX6) combined with five doses of bevacizumab (the number of doses was reduced to prevent surgical delays) to patients with cT4NxM0 colon or upper rectal cancers. And radical surgery was performed approximately 2 weeks after the last dose of neoadjuvant therapy. The primary endpoint was a pathologic complete response (pCR). We also evaluated major pathologic response (MPR, ≤10% residual viable tumor), radiological and pathological regression, safety, and tumor mutation burden (TMB), and tumor microenvironment (TME) characteristics. Results By the cutoff date (September 2023), 22 patients with cT4NxM0 pMMR/MSS colon or upper rectal cancers were enrolled and the median follow‐up was 24.7 months (IQR: 21.1–26.1). All patients underwent R0 surgical resection without treatment‐related surgical delays. pCR occurred in 12 of 22 resected tumors (54.5%) and MPR occurred in 18 of 22 (81.8%) patients. At the cutoff date, all patients were alive, and 21/22 were recurrence‐free. Treatment‐related adverse events of grade 3 or higher occurred in of 2/22 (9.1%) patients. Among the pCR tumors, two were found to harbor POLE mutations. The degree of pathological regression was significantly greater than that of radiological regression (p = 1.35 × 10−8). The number of CD3+/CD4+ cells in the tumor and stroma in pretreated biopsied tissues was markedly lower in pCR tumors than in non‐pCR tumors (p = 0.038 and p = 0.015, respectively). Conclusions Neoadjuvant sintilimab combined with bevacizumab and mFOLFOX6 was associated with few side effects, did not delay surgery, and led to pCR and non‐pCR in 54.5% and 81.8% of the cases, respectively. Downregulation of CD3/CD4 expression in the tumor and stroma is related to pCR. However, the molecular mechanisms underlying PD‐1 blockade‐enhanced targeted chemotherapy require further investigation.

Abstract Background This study was to establish and validate prediction models to predict the cancer-specific survival (CSS) and overall survival (OS) of small-cell lung cancer (SCLC) patients with liver metastasis. Methods In the retrospective cohort study, SCLC patients with liver metastasis between 2010 and 2015 were retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomly divided into the training group and testing group (3: 1 ratio). The Cox proportional hazards model was used to determine the predictive factors for CSS and OS in SCLC with liver metastasis. The prediction models were conducted based on the predictive factors. The performances of the prediction models were evaluated by concordance indexes (C-index), and calibration plots. The clinical value of the models was evaluated by decision curve analysis (DCA). Results In total, 8,587 patients were included, with 154 patients experiencing CSS and 154 patients experiencing OS. The median follow-up was 3 months. Age, gender, marital status, N stage, lung metastases, multiple metastases surgery of metastatic site, chemotherapy, and radiotherapy were independent predictive factors for the CSS and OS of SCLC patients with liver metastasis. The prediction models presented good performances of CSS and OS among patients with liver metastasis, with the C-index for CSS being 0.724, whereas the C-index for OS was 0.732, in the training set. The calibration curve showed a high degree of consistency between the actual and predicted CSS and OS. DCA suggested that the prediction models provided greater net clinical benefit to these patients. Conclusion Our prediction models showed good predictive performance for the CSS and OS among SCLC patients with liver metastasis. Our developed nomograms may help clinicians predict CSS and OS in SCLC patients with liver metastasis.

Abstract Background Lymph node metastasis (LNM) is a prognostic biomarker and affects therapeutic selection in colorectal cancer (CRC). Current evaluation methods are not adequate for estimating LNM in CRC. H&E images contain much pathological information, and collagen also affects the biological behavior of tumor cells. Hence, the objective of the study is to investigate whether a fully quantitative pathomics-collagen signature (PCS) in the tumor microenvironment can be used to predict LNM. Methods Patients with histologically confirmed stage I-III CRC who underwent radical surgery were included in the training cohort (n = 329), the internal validation cohort (n = 329), and the external validation cohort (n = 315). Fully quantitative pathomics features and collagen features were extracted from digital H&E images and multiphoton images of specimens, respectively. LASSO regression was utilized to develop the PCS. Then, a PCS-nomogram was constructed incorporating the PCS and clinicopathological predictors for estimating LNM in the training cohort. The performance of the PCS-nomogram was evaluated via calibration, discrimination, and clinical usefulness. Furthermore, the PCS-nomogram was tested in internal and external validation cohorts. Results By LASSO regression, the PCS was developed based on 11 pathomics and 9 collagen features. A significant association was found between the PCS and LNM in the three cohorts (P

Abstract Accurate identification of the physiological intersegmental plane is crucial for successful anatomical segmentectomy. Current techniques, such as the inflation‐deflation method, may result in uncertain cutting lines, leading to unsuitable resection extents. Here, we demonstrated the successful use of electromagnetic navigation with methylene blue dye‐marking to preoperatively and precisely identify the physiological intersegmental plane in two patients with small‐sized peripheral non‐small cell lung cancer (NSCLC). This novel technique offers the potential for precise cutting lines that align closely with the physiological intersegmental plane, thus improving the accuracy and efficacy of anatomical segmentectomy for these selected NSCLC patients.

ObjectiveTo investigate the performance of a novel feature fusion radiomics (RFF) model that incorporates features from multiparametric MRIs (mpMRI) in distinguishing different statuses of molecular receptors in breast cancer (BC) preoperatively.Methods460 patients with 466 pathology-confirmed BCs who underwent breast mpMRI at 1.5T in our center were retrospectively included hormone receptor (HR) positive (HR+) (n=336) and HR negative (HR-) (n=130). The HR- patients were further categorized into human epidermal growth factor receptor 2 (HER-2) enriched BC (HEBC) (n=76) and triple negative BC (TNBC) (n=54). All lesions were divided into a training/validation cohort (n=337) and a test cohort (n=129). Volumes of interest (VOIs) delineation, followed by radiomics feature extraction, was performed on T2WI, DWI600 (b=600 s/mm2), DWI800 (b=800 s/mm2), ADC map, and DCE1-6 (six continuous DCE-MRI) images of each lesion. Simulating a radiologist’s work pattern, 150 classification base models were constructed and analyzed to determine the top four optimum sequences for classifying HR+ vs. HR-, TNBC vs. HEBC, TNBC vs. non-TNBC in a random selected training cohort (n=337). Building upon these findings, the optimal single sequence models (Rss) and combined sequences models (RFF) were developed. The AUC, sensitivity, accuracy and specificity of each model for subtype differentiation were evaluated. The paired samples Wilcoxon signed rank test was used for performance comparison.ResultsDuring the three classification tasks, the optimal single sequence for classifying HR+ vs. HR- was DWI600, while the ADC map, derived from DWI800 performed the best in distinguishing TNBC vs. HEBC, as well as identifying TNBC vs. non-TNBC, with corresponding training AUC values of 0.787, 0.788, and 0.809, respectively. Furthermore, the integration of the top four sequences in RFF models yielded improved performance, achieving AUC values of 0.809, 0.805 and 0.847, respectively. Consistent results was observed in both the training/validation and testing cohorts, with AUC values of 0.778, 0.787, 0.818 and 0.726, 0.773, 0.773, respectively (all p < 0.05 except HR+ vs. HR-).ConclusionThe RFF model, integrating mpMRI radiomics features, demonstrated promising ability to mimic radiologists’ diagnosis for preoperative identification of molecular receptors of BC.

The non-destructive study of soil micromorphology via computed tomography (CT) imaging has yielded significant insights into the three-dimensional configuration of soil pores. Precise pore analysis is contingent on the accurate transformation of CT images into binary image representations. Notably, segmentation of 2D CT images frequently harbors inaccuracies. This paper introduces a novel three-dimensional pore segmentation method, BDULSTM, which integrates U-Net with convolutional long short-term memory (CLSTM) networks to harness sequence data from CT images and enhance the precision of pore segmentation. The BDULSTM method employs an encoder–decoder framework to holistically extract image features, utilizing skip connections to further refine the segmentation accuracy of soil structure. Specifically, the CLSTM component, critical for analyzing sequential information in soil CT images, is strategically positioned at the juncture of the encoder and decoder within the U-shaped network architecture. The validation of our method confirms its efficacy in advancing the accuracy of soil pore segmentation beyond that of previous deep learning techniques, such as U-Net and CLSTM independently. Indeed, BDULSTM exhibits superior segmentation capabilities across a diverse array of soil conditions. In summary, BDULSTM represents a state-of-the-art artificial intelligence technology for the 3D segmentation of soil pores and offers a promising tool for analyzing pore structure and soil quality.

Abstract The current tumor‐node‐metastasis staging system does not provide sufficient prognostic prediction or adjuvant chemotherapy benefit information for stage II‐III colon cancer (CC) patients. Collagen in the tumor microenvironment affects the biological behaviors and chemotherapy response of cancer cells. Hence, in this study, we proposed a collagen deep learning (collagenDL) classifier based on the 50‐layer residual network model for predicting disease‐free survival (DFS) and overall survival (OS). The collagenDL classifier was significantly associated with DFS and OS (P

Abstract Bronchial carcinoid tumors are low‐grade malignant and lung‐sparing surgery is preferred for the removal of these tumors. We describe a surgical technique of robot‐assisted sleeve segmentectomy via single utility port approach with three robotic arms. This operation was performed in an aged patient with decreased pulmonary function, whose carcinoid tumor was located at the origin of the right superior segmental bronchus. A 1.5‐cm incision was performed in the eighth intercostal space of the midaxillary line and another 4‐cm incision was made in the fifth intercostal space of the anterior axillary line. Postoperative recovery of the patient was smooth without postoperative complications.

BackgroundEndoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series.MethodsTwo independent cohorts’ series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC.ResultsThe clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830.ConclusionsOur results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients.

Abstract Video‐assisted thoracoscopic surgery (VATS) subsegmentectomy has been widely used to resect small‐sized lung lesions in clinical practice. Precise identification of the subsegmental bronchus is one of the essential steps in performing thoracoscopic anatomic subsegmentectomy. Here, we report a thoracoscopic right S2a segmentectomy with preoperative electromagnetic navigational bronchoscopy (ENB)‐guided injection of methylene blue to identify the subsegmental bronchus in a 51‐year‐old male. We successfully performed complicated surgery using this method. This ENB‐guided dye marking method may accurately distinguish the subsegmental bronchus to effectively guide surgery.

Abstract Background Tumor spread through air spaces (STAS) is a novel pathologic characteristic in lung adenocarcinomas that indicates invasive tumor behavior. We aimed to explore the relationship between Twist, Slug and STAS in lung adenocarcinoma and to investigate the potential relationship between epithelial‐mesenchymal transition (EMT) and STAS. Materials and methods Our study retrospectively analyzed 115 patients with resected lung adenocarcinomas to evaluate the relationship between Twist, Slug and STAS. STAS was diagnosed using hematoxylin‐eosin (H&E) staining. Immunohistochemistry was used to evaluate the expression levels of Slug and Twist. Results In this study, 56 (48.7%) patients had STAS, 40 (34.8%) patients had Slug overexpression, and 28 (24.3%) patients had Twist overexpression. Patients with either STAS or Slug and Twist overexpression experienced poor recurrence‐free survival (RFS) and overall survival (OS). There were significant associations between Twist overexpression, Slug overexpression and the presence of STAS. The logistic model further revealed that pathological stage, Twist overexpression and Slug overexpression were independent risk factors for STAS. A multivariate analysis that contained Twist, Slug, pathologic stage and STAS, showed that pathologic stage and STAS were independent prognostic factors for poor RFS and OS. Another multivariate model that contained Twist, Slug and pathologic stage, showed that pathologic stage, Twist overexpression and Slug overexpression were independent risk factors for poor RFS and OS. In the cohort with STAS, the multivariate analysis showed that pathologic stage and Twist overexpression were independent risk factors for poor survival. The subgroup analysis showed that patients with both Slug overexpression and Twist overexpression with STAS received a poor prognosis. Conclusions STAS, Slug and Twist were correlated with poor RFS and OS in resected lung adenocarcinomas. Additionally, STAS was correlated with the overexpression of Twist and Slug, which could potentially provide information on the mechanism of STAS.

To address large data transmission requirements and high transmission power consumptions characterizing micro-environment monitoring systems that are commonly used in forest health and safety applications, we propose an ecological big data adaptive switching compression method based on a 1D convolutional neural network (1D CNN). First, to ensure that data samples apply to different compression dictionaries, a 1D CNN is used to classify the samples into two sets according to the characteristics of samples. Subsequently, based on the classification results, the switching factor $S$ is defined, such that the discrete cosine transform (DCT) predefined dictionary and the learning dictionary (K-SVD) can be used to adaptively achieve sparse expression and data compression. Finally, the orthogonal matching pursuit (OMP) algorithm reconstructs the sparse signal. To evaluate the feasibility and robustness of the proposed method, we conduct experiments on four types of data: air temperature (AT), air humidity (AH), soil temperature (ST), and soil humidity (SH). The results indicate that the proposed method, compared to K-SVD and DCT dictionary, exhibits excellent performance for all data samples having smaller sparse error (SE), smaller reconstruction error (RE), and larger compression ratio (CR) at different sparsity levels. In particular, when sparsity $K$ is 16, the reconstructed signal is the most similar to the original signal. In addition, the proposed method reduces power consumption by 79.90%, compared with uncompressed data transmission. Considering four factors, the adaptive switching compression method based on 1D CNN has higher reconstruction accuracy and lower power consumption than using only K-SVD or DCT dictionary.

Background and objective More patients with pulmonary nodules are being referred to thoracic surgeons under the increasing use of computed tomography scans (CT). Impalpable peripheral subpleural solitary pulmonary nodules are difficult to be localized by video assisted thoracic surgery. Although some common techniques including CT-guided puncture positioning and electromagnetic navigation bronchoscopy (ENB)-guided methylene blue staining positioning, can bring good results in positioning, there are still some complications such as pneumothorax, hemorrhage and inaccurate positioning. Vectorial localization guided by electromagnetic navigational bronchoscopy followed by thoracoscopic resection is a novel alternative technique by us firstly for definitive diagnosis, which can avoid the possible injury of pleural or enlargement of the location area, providing some guidance for ENB-guided location technology. The main objective of this study was to evaluate the feasibility and our initial experience of vectorial localization guided by electromagnetic navigation followed by video-assisted thoracoscopic pulmonary solitary nodules resection. Methods We retrospectively analyzed 22 cases who undergoing vectorial localization of peripheral pulmonary lesion guided by electromagnetic navigation prior to video assisted lung resection, and characteristics and intraoperative outcomes were explored. Results Twenty-two nodules of twenty-two patients were all localized by this method successfully with an average location time (17.5±4.2) min. The average nodule size was (11.0±3.6) mm. The distance between the locatable guide probe (LG) and lesion on the electromagnetic navigation bronchoscopy screen was (14.5±10.1) mm. The distance between the lesion and probe mark on the dissected specimen was (15.3±11.0) mm. There was no displacement of any case. No conversion to thoracotomy was found. And there were no adverse events during the localization and operation procedure. Length of hospital stay was (3.8±1.2) d and the operative mortality was 0.0%. Malignant lesions were found in 19 patients and they were all completely resected with negative microscopic margins. Conclusion Our initial experience with vectorial localization of peripheral pulmonary lesion guided by electromagnetic navigation and minimally invasive resection proved that this technique was an alternative accurate and safe way for small pulmonary nodules. Thoracic surgeons should further investigate this method and apply it to clinical practice.

Abstract Visceral pleural invasion (VPI) has been identified as an adverse prognostic factor for non‐small cell lung cancer (NSCLC). Accurate nodal staging for NSCLC correlates with improved survival, but it is unclear whether tumors with VPI require a more extensive lymph nodes (LNs) dissection to optimize survival. We aimed to evaluate the impact of VPI status on the optimal extent of LNs dissection in stage I NSCLC, using the Surveillance, Epidemiology, and End Results (SEER) database. We identified 9297 surgically treated T1‐2aN0M0 NSCLC patients with at least one examined LNs. Propensity score matching was conducted to balance the baseline clinicopathologic characteristics between the VPI group and non‐VPI group. Log‐rank tests along with Cox proportional hazards regression methods were performed to evaluate the impact of extent of LNs dissection on survival. VPI was correlated with a significant worse survival, but there was no significant difference in survival rate between PL1 and PL2. Patients who underwent sublobectomy had slightly decreased survival than those who underwent lobectomy. Pathologic LNs examination was significantly correlated with survival. Examination of 7‐8 LNs and 14‐16 LNs conferred the lowest hazard ratio for T1‐sized/non‐VPI tumors (stage IA) and T1‐sized/VPI tumors (stage IB), respectively. The optimal extent of LNs dissection varied by VPI status, with T1‐sized/VPI tumors (stage IB) requiring a more extensive LNs dissection than T1‐sized/non‐VPI tumors (stage IA). These results might provide guidelines for surgical procedure in early stage NSCLC.

Shengma Biejia decoction (SMBJD), a traditional Chinese formula recorded in the Golden Chamber, has been widely used for the treatment of malignant tumors. However, its underlying molecular targets and mechanisms are still unclear. This study showed that SMBJD inhibited tumor growth and stimulated hemogram recovery significantly in a multiple myeloma xenograft model. Western blot and immunohistochemistry assays of tumor tissues showed that SMBJD reduced the ratio of autophagy-related proteins LC3-II/LC3-I, while P62 and apoptosis-related proteins cleaved caspase-3/caspase-3 and Bax/Bcl-2 were upregulated. In vitro experiments demonstrated the time-dependent and dose-dependent cytotoxicity of SMBJD on multiple myeloma cell lines H929 and U266 through MTT assays. The LC3-II/LC3-I ratio and number of GFP-LC3 puncta showed that SMBJD inhibited the autophagy process of H929 and U266 cells. Moreover, both SMBJD and 3-methyladenine (3-MA) caused a decrease in LC3-II/LC3-I, and SMBJD could not reverse the upregulation of LC3-II/LC3-I caused by bafilomycin A1 (Baf-A1). Furthermore, the results of annexin V-FITC and propidium iodide double staining demonstrated that SMBJD treatment induced the apoptosis of H929 and U266 cells. These data prove that SMBJD inhibits autophagy and promotes apoptosis in H929 and U266 cells. The results also show that rapamycin could reduce the rate of SMBJD-induced apoptosis in H929 and U266 cells, at a concentration which had no effect on apoptosis but activated autophagy. In addition, analysis of the mechanism indicated that levels of phosphorylated ERK and phosphorylated mTOR were increased by treatment with SMBJD in vivo and in vitro. These results indicate that SMBJD, an old and effective herbal compound, could inhibit the viability of H929 and U266 cells and induce autophagy-mediated apoptosis through the ERK/mTOR pathway. Thus, it represents a potential therapy strategy for multiple myeloma.

Background and Aim: This work aims to study the relationship between MRI-defined mucin pool (MP) patterns prior to treatment and the efficacy of neoadjuvant therapy (NAT) in locally advanced rectal mucinous adenocarcinoma (RMAC).Methods: This retrospective study included 278 RMAC patients evaluated between January 2012 and January 2019. After having been trained by using 118 cases with postoperative pathological images, radiologists distinguished MRI-defined MP status as mixed type (MTMP) and separate type (STMP) in a NAT cohort (160 patients) in addition to tumor characteristics, invasion of mesorectal facia, and nodal status. Reader reproducibility was determined using the κ coefficient. The main outcome was the accuracy of MP dichotomy in predicting whether patients had tumor responsiveness or not.Results: Among 278 cases, MTMP and STMP accounted for 49.6 and 50.4% of MPs, respectively. A total of 72 patients received neoadjuvant chemoradiotherapy and 88 received chemotherapy. The tumor responsiveness rate in the chemoradiotherapy group was higher than that in the chemotherapy group (58.3 vs. 21.6%, P < 0.001). In the chemotherapy group, the tumor responsiveness rate in patients with MTMPs was lower than that in patients with STMPs (4.9 vs. 25.5%, P = 0.002). The baseline MRI-defined MTMP was associated with lower responsiveness rates after NAT in the chemotherapy group (odds ratio, 11.050, with 95% CI, 2.368–51.571, P = 0.002).Conclusions: MP dichotomy can be reliably evaluated by using MRI. In the chemotherapy group, MTMP may be a dependent predictor to indicate a lower likelihood of tumor responsiveness after NAT.

Background and objective In clinical Ia (cT1N0M0) patients, some may have poor prognosis, for it might occur pathologic N1 (pN1) or N2 (pN2) postoperatively. The aim of this study is to determine the radiologicaland pathological factors related to clinical Ia adenocarcinoma. Methods The retrospective study was conducted on 297 clinical Ia adenocarcinoma patients resected at our hospital between May 2012 to December 2016. The clinical profiles, radiological and pathological features were analyzed between nodal upstaging group and non-upstaging group. Results Of 297 patients treated for cN0 tumors, 250 cases (84.2%) were confirmed postoperatively as having pN0 tumors, and 47 (15.8%) were confirmed as having pN1 or pN2 tumors. Female, low smoking index, micropapillary predominant and solid predominant adenocarcinoma, puresolid tumor and large tumor size were all more frequently seen in the nodal upstaging group than in the pN0 group (P

Robotic surgery for circumferential tracheal resection and reconstruction has not previously been reported. Herein we describe the case of a 48‐year‐old man with primary extra‐luminal tracheal leiomyosarcoma. A preoperative bronchoscopy exam and chest computed tomography revealed a tracheal neoplasm, which derived from lower membranous trachea and nearly obstructed the orifice of the left main bronchus. The patient underwent circumferential tracheal resection and reconstruction via a completely portal robotic approach with four arms. Benefitting from flexible instruments and a three‐dimensional magnified view, telescope anastomosis was performed and modulated. No postoperative complications presented. Follow‐up showed the reconstructed trachea without stenosis or recurrence.

Both primary auditory cortex (A1) and anterior auditory field (AAF) are core regions of auditory cortex of many mammalians. While the function of A1 has been well documented, the role of AAF in sound related behavioral remain largely unclear. Here in adult rats, sound cued fear conditioning paradigm, surgical ablation, and chemogenetic manipulations were used to examine the role of AAF in fear related sound context recognition. Precise surgical ablation of AAF cannot block sound cued freezing behavior but the fear conditioning became non-selective to acoustic cue. Reversible inhibition of AAF using chemogenetic activation at either training or testing phase can both lead to strong yet non-selective sound cued freezing behavior. These simple yet clear results suggested that in sound cued fear conditioning, sound cue and detailed content in the cue (e.g., frequency) are processed through distinct neural circuits and AAF is a critical part in the cortex dependent pathway. In addition, AAF is needed and playing a gating role for precise recognition of sound content in fear conditioning task through inhibiting fear to harmless cues.

A Disintegrin and Metalloproteinase with Thrombospondin motifs 19 (ADAMTS19) has been reported to participate in the pathogenesis of solid cancers. However, its role in gastric cancer (GC) remains undocumented. Using immunohistochemistry (IHC) staining and quantitative real-time polymerase chain reaction (qRT-PCR) on GC tissues and adjacent normal tissues, we found that ADAMTS19 was downregulated in GC tissues (IHC: p < 0.001; qRT-PCR: p = 0.017). Further investigation revealed that ADAMTS19 correlated with distant metastasis (p = 0.008) and perineural invasion (p = 0.018) and that patients with low ADAMTS19 had worse overall survival (p = 0.021). Gain- and loss-of-function assays showed that ADAMTS19 suppressed cell migration and invasion in vitro. Using bioinformatics analysis and co-immunoprecipitation, immunofluorescence, and dual-luciferase reporter gene assays, we confirmed that ADAMTS19 binds with cytoplasm P65, decreasing the nucleus phosphorylation of P65, a crucial transcription factor in the nuclear factor kappa-B (NF-κB) pathway, thereby downregulating S100 calcium-binding protein A16 (S100A16) expression. S100A16 acted as the downstream of ADAMTS19, reversing the suppression of cell migration and invasion by ADAMTS19 in vitro. A combination of ADAMTS19 and S100A16 expression provided the optimal prognostic indicator for GC. Patients with ADAMTS19high-S100A16low had better overall survival than ADAMTS19low-S100A16high patients (p = 0.006). These results suggest that ADAMTS19 suppresses cell migration and invasion by targeting S100A16 via the NF-κB pathway and that ADAMTS19 and S100A16 are potential metastasis and survival biomarkers for GC.

In order to solve the problem of data loss in sensor data collection, this paper took the stem moisture data of plants as the object, and compared the filling value of missing data in the same data segment with different data filling methods to verify the validity and accuracy of the stem water filling data of the LSTM (Long Short-Term Memory) model. This paper compared the accuracy of missing stem water data for plants under different data filling methods to solve the problem of data loss in sensor data collection. Original stem moisture data was selected from Lagerstroemia Indica which was planted in the Haidian District of Beijing in June 2017. Part of the data which treated as missing data was manually deleted. Interpolation methods, time series statistical methods, the RNN (Recurrent Neural Network), and LSTM neural network were used to fill in the missing part and the filling results were compared with the original data. The result shows that the LSTM has more accurate performance than the RNN. The error values of the bidirectional LSTM model are the smallest among several models. The error values of the bidirectional LSTM are much lower than other methods. The MAPE (mean absolute percent error) of the bidirectional LSTM model is 1.813%. After increasing the length of the training data, the results further proved the effectiveness of the model. Further, in order to solve the problem of one-dimensional filling error accumulation, the LSTM model is used to conduct the multi-dimensional filling experiment with environmental data. After comparing the filling results of different environmental parameters, three environmental parameters of air humidity, photosynthetic active radiation, and soil temperature were selected as input. The results show that the multi-dimensional filling can greatly extend the sequence length while maintaining the accuracy, and make up for the defect that the one-dimensional filling accumulates errors with the increase of the sequence. The minimum MAPE of multidimensional filling is 1.499%. In conclusion, the data filling method based on LSTM neural network has a great advantage in filling the long-lost time series data which would provide a new idea for data filling.

Analysis of a tree ring is the primary method for determining the growth and age of a tree. In a microdestructive tree-ring measurement system, the tree under test is drilled with a microdrill at a constant rotating speed to detect the difference in density between the early and late wood, thereby realizing a microdestructive measurement of the tree-ring. The measurement system comprises a microdrill with a diameter of 3 mm, mechanical transmission, direct current (DC) servomotor, stepper motor, and control and detection circuit. The DC servomotor and stepper motor realize rotation and translation of the microdrill, respectively, through mechanical transmission. When the microdrill rotates and drills into the tree, the control and detection circuit samples and acquires the armature current of the DC servomotor, which is proportional to the resistance encountered by the drill bit and reflects the change in the density of the tree. The tree-ring number can be obtained by filtering the sampled original signals of the armature current using a finite impulse response (FIR) filtering algorithm. The annual rings of larch and fir tree discs were measured and tested using the designed system. It was observed that the average annual ring measurement accuracy of the larch discs reached 95.28%, while that of the fir discs was 84.16%. The diameter of the drill hole in the trunk was less than 3 mm after measuring the living wood, thereby achieving a microdestructive measurement of the tree-ring.

Diseases from Ginkgo biloba have brought great losses to medicine and the economy. Therefore, if the degree of disease can be automatically identified in Ginkgo biloba leaves, people will take appropriate measures to avoid losses in advance. Deep learning has made great achievements in plant disease identification and classification. For this paper, the convolution neural network model was used to classify the different degrees of ginkgo leaf disease. This study used the VGGNet-16 and Inception V3 models. After preprocessing and training 1322 original images under laboratory conditions and 2408 original images under field conditions, 98.44% accuracy was achieved under laboratory conditions and 92.19% under field conditions with the VGG model. The Inception V3 model achieved 92.3% accuracy under laboratory conditions and 93.2% under field conditions. Thus, the Inception V3 model structure was more suitable for field conditions. To our knowledge, there is very little research on the classification of different degrees of the same plant disease. The success of this study will have a significant impact on the prediction and early prevention of ginkgo leaf blight.

Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China’s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.

This article presents a non-destructive methodology to determine the modulus of elasticity (MOE) in static bending of wood through the use of near-infrared (NIR) spectroscopy. Wood specimens were obtained from Quercus mongolica growing in Northeast of China. The NIR spectra of specimens were acquired by using a one-chip NIR fiber optic spectrometer whose spectral range was 900~1900 nm. The raw spectra of specimens were pretreated by multiplication scatter correlation and Savitzky-Golay smoothing and differentiation filter. To reduce the dimensions of data and complexity of modeling, the synergy interval partial least squares and successive projections algorithm were applied to extract the characteristic wavelengths, which had closing relevance with the MOE of wood, and five characteristic wavelengths were selected from full 117 variables of a spectrum. Taking the characteristic wavelengths as input values, partial least square regression (PLSR) and the propagation neural network (BPNN) were implemented to establish calibration models. The predictive ability of the models was estimated by the coefficient of determination (rp) and the root mean square error of prediction (RMSEP) and in the prediction set. In comparison with the predicted results of the models, BPNN performed better results with the higher rp of 0.91 and lower RMSEP of 0.76. The results indicate that it is feasible to accurately determine the MOE of wood by using the NIR spectroscopy technique.

Forest duff (fermentation and humus) water content is an important parameter for fire risk prediction and water resource management. However, accurate determination of forest duff water content is difficult due to its loose structure. This study evaluates the feasibility of a standing wave ratio (SWR) sensor to accurately determine the forest duff water content. The performance of this sensor was tested on fermentation and humus with eight different compaction levels. Meanwhile, a commercialized time domain reflectometry (TDR) was employed for comparison. Calibration results showed that there were strong linear relationships between the volumetric water content (θV) and the SWR sensor readings (VSWR) at different compaction classes for both fermentation and humus samples. The sensor readings of both SWR and TDR underestimated the forest duff water content at low compacted levels, proving that the compaction of forest duff could significantly affect the measurement accuracy of both sensors. Experimental data also showed that the accuracy of the SWR sensor was higher than that of TDR according to the root mean square error (RMSE). Furthermore, low cost is another important advantage of the SWR sensor in comparison with TDR. This low-cost SWR sensor performs well in loose materials and is feasible for evaluating the water content of forest duff. In addition, the results indicate that decomposition of the forest duff should be taken into account for continuous and long-term water content measurement.

Metastasis and drug resistance are major barriers for the treatment of non-small cell lung cancer (NSCLC). To explore new therapeutic options, we successfully encapsulated MicroRNA-34a (miR-34a), a potent endogenous tumor suppressor in NSCLC into S6 aptamer-conjugated dendrimer to form lung cancer-targeted gene delivery nanoparticles (PAM-Ap/pMiR-34a NPs). PAM-Ap/pMiR-34a NPs had a diameter of 100-200 nm and Zeta potential of ~30 mV at applied N/P ratio. The aptamer conjugation significantly improved cellular uptake as well as gene transfection efficiency of PAM-Ap/pMiR-34a NPs in cultured NSCLC cells. We showed that PAM-Ap/pMiR-34a NPs enhanced the regulation of targeted genes, BCL-2 and p53 in vitro. In addition, we revealed PAM-Ap/pMiR-34a NPs significantly inhibited cell growth, migration, invasion and induced apoptosis of lung cancer cells compared with non-targeted NPs. The method provided a novel therapeutic strategy for the experimental treatment of NSCLC.

We have previously demonstrated the growth-promoting effect of intrathecal delivery of recombinant rat IL-6 immediately after corticospinal tract (CST) injury. Our present study aims to further clarify whether intrathecal delivery of IL-6 after CST injury could reactivate the intrinsic growth capacity of pyramidal cells in the sensorimotor cortex which project long axons to the spinal cord. We examined, by ELISA, levels of cyclic adenosine monophosphate (cAMP), adenylyl cyclase (AC, which synthesizes cAMP), phosphodiesterases (PDE, which degrades cAMP), and, by RT-PCR, the expression of regeneration-associated genes in the rat sensorimotor cortex after intrathecal delivery of IL-6 for 7 days, started immediately after CST injury. Furthermore, we injected retrograde neuronal tracer Fluorogold (FG) to the spinal cord to label pyramidal cells in the sensorimotor cortex, layers V and VI, combined with βIII-tubulin immunostaining, then we analyzed by immunohistochemisty and western blot the expression of the co-receptor gp-130 of IL-6 family, and pSTAT3 and mTOR, downstream IL-6/JAK/STAT3 and PI3K/AKT/mTOR signaling pathways respectively. We showed that intrathecal delivery of IL-6 elevated cAMP level and upregulated the expression of regeneration-associated genes including GAP-43, SPRR1A, CAP-23 and JUN-B, and the expression of pSTAT3 and mTOR in pyramidal cells of the sensorimotor cortex. In contrast, AG490, an inhibitor of JAK, partially blocked these effects of IL-6. All these results indicate that intrathecal delivery of IL-6 immediately after spinal cord injury can reactivate the intrinsic growth capacity of pyramidal cells in the sensorimotor cortex and these effects of IL-6 were partially JAK/STAT3-dependent.

Diverse sensing techniques have been developed and combined with machine learning method for forest fire detection, but none of them referred to identifying smoldering and flaming combustion phases. This study attempts to real-time identify different combustion phases using a developed wireless sensor network (WSN)-based multi-sensor system and artificial neural network (ANN). Sensors (CO, CO2, smoke, air temperature and relative humidity) were integrated into one node of WSN. An experiment was conducted using burning materials from residual of forest to test responses of each node under no, smoldering-dominated and flaming-dominated combustion conditions. The results showed that the five sensors have reasonable responses to artificial forest fire. To reduce cost of the nodes, smoke, CO2 and temperature sensors were chiefly selected through correlation analysis. For achieving higher identification rate, an ANN model was built and trained with inputs of four sensor groups: smoke; smoke and CO2; smoke and temperature; smoke, CO2 and temperature. The model test results showed that multi-sensor input yielded higher predicting accuracy (≥82.5%) than single-sensor input (50.9%–92.5%). Based on these, it is possible to reduce the cost with a relatively high fire identification rate and potential application of the system can be tested in future under real forest condition.