Association of the pathomics-collagen signature with lymph node metastasis in colorectal cancer: a retrospective multicenter study.

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 < 0.001). Then, the PCS-nomogram based on PCS, preoperative CEA level, lymphadenectasis on CT, venous emboli and/or lymphatic invasion and/or perineural invasion (VELIPI), and pT stage achieved AUROCs of 0.939, 0.895, and 0.893 in the three cohorts. The calibration curves identified good agreement between the nomogram-predicted and actual outcomes. Decision curve analysis indicated that the PCS-nomogram was clinically useful. Moreover, the PCS was still an independent predictor of LNM at station Nos. 1, 2, and 3. The PCS nomogram displayed AUROCs of 0.849-0.939 for the training cohort, 0.837-0.902 for the internal validation cohort, and 0.851-0.895 for the external validation cohorts in the three nodal stations. CONCLUSIONS: This study proposed that PCS integrating pathomics and collagen features was significantly associated with LNM, and the PCS-nomogram has the potential to be a useful tool for predicting individual LNM in CRC patients.

Study on the variation characteristics and influencing factors of stem water content of Acer truncatum during the overwintering period.

Stem water content serves as a pivotal parameter that reflects the plant vitality and maintains their internal water balance. Given the insufficient comprehension regarding the stem water content characteristics and its influencing factors during different stages of the overwintering period, the study focused on Acer truncatum Bunge and developed an Internet of Things (IoT)-based ecological information monitoring system. The system incorporated a proprietary stem water content sensor, allowing non-invasive, in-situ and real time acquisition of stem water content while monitoring diverse environmental parameters. We conducted a detailed elucidation of stem water content variation characteristics and their responses to diverse environmental factors. The results showed: (1) During the overwintering period, stem water content exhibited diurnal variations characterized by " daytime ascent and nighttime descent" across the three stages, exhibiting differences in the moment when the stem water content reaches extremal values and daily fluctuations ranges. Stem water content exhibited minimal fluctuations during deciduous and bud-breaking stages but experienced significant freezing-thawing alternations during the dormant stage, leading to an increased daily fluctuation range. (2) The Pearson correlation coefficients between environmental parameters and stem water content varied dynamically across stages. Path analysis revealed that during the deciduous stage, stem temperature and saturation vapor pressure deficit were dominant factors influencing stem water content; during dormant stage, air temperature and saturation vapor pressure deficit directly impacted stem water content; during the bud-breaking stage, the primary parameters affecting stem water content were saturation vapor pressure deficit and stem temperature. The study provides valuable insights into unveiling the water transport patterns within tree stems tissue and their environmental adaptation mechanisms during the overwintering period, aiding in the scientific development of winter management strategies to protect trees from severe cold and freezing damage, while fostering healthy growth in the subsequent year.

Development and validation of prognostic models for small cell lung cancer patients with liver metastasis: a SEER population-based study.

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.

ADAMTS10 inhibits aggressiveness via JAK/STAT/c-MYC pathway and reprograms macrophage to create an anti-malignant microenvironment in gastric cancer.

BACKGROUND: A disintegrin and metalloproteinase with thrombospondin motifs 10 (ADAMTS10) plays a role in extracellular matrix and correlates with Weill-Marchesani syndrome. However, its role in gastric cancer remains unknown. Thus, we started this research to unveil the role of ADAMTS10 in gastric cancer (GC). METHODS: The expression of ADAMTS10 in GC was analyzed by immunohistochemical staining and quantitative RT-PCR (qRT-PCR). The effects of ADAMTS10 inhibiting GC cell progression were conducted by functional experiments in vitro and in vivo. Flow cytometry was used to discover changing of cell cycle, apoptosis and ROS by ADAMTS10 in GC cell. Western blot was applied to identify targets of ADAMTS10. Western blot, qRT-PCR and flow cytometry were applied to discover the effect of ADAMT10 on THP1. RESULTS: ADAMTS10 expression was downregulated in GC tissue and patients with low ADAMTS10 levels had poorer overall survival. ADAMTS10 overexpression altered cell cycle, promoted apoptosis, and inhibited proliferation, migration, and invasion in vitro and in vivo. ADAMTS10 regulated TXNIP and ROS through the JAK/STAT/c-MYC pathway. Decreasing TXNIP and ROS reversed the inhibitory effect of ADAMTS10 on cell migration and invasion in vitro. ADAMTS10 secreted by GC cells was absorbed by THP1 and regulated TXNIP and ROS in THP1. ADAMTS10 secreted by GC cells inhibited macrophage M2 polarization. CONCLUSIONS: These results suggest that ADAMTS10 targets TXNIP and ROS via the JAK/STAT/c-MYC pathway and that may play important roles in GC progression and macrophage polarization which indicates that ADAMTS10 can be a potential survival marker for gastric cancer.

The Novel Protein ADAMTS16 Promotes Gastric Carcinogenesis by Targeting IFI27 through the NF-κb Signaling Pathway.

A disintegrin and metalloproteinase with thrombospondin motifs 16 (ADAMTS16) has been reported to be involved in the pathogenesis of solid cancers. However, its role in gastric cancer (GC) is unclear. In this study, the role of ADAMTS16 in gastric cancer was investigated. The effects of ADAMTS16 on cell migration, invasion, and proliferation were investigated by functional experiments in vivo and in vitro. Downstream signal pathways of ADAMTS16 were confirmed by using bioinformatics analysis, co-immunoprecipitation, and immunofluorescence. Meanwhile, bioinformatics analysis, qRT-PCR, western blot, and dual-luciferase reporter gene analysis assays were used to identify ADAMTS16 targets. The expression of ADAMTS16 in GC was analyzed in public datasets. The expression of ADAMTS16 and its correlations with the clinical characteristics of GC were investigated by immunohistochemistry. Ectopic ADAMTS16 expression significantly promoted tumor cell migration, invasion, and growth. Bioinformatics analysis and western blot showed that ADAMTS16 upregulated the IFI27 protein through the NF-κb pathway, which was confirmed by immunofluorescence and western blot. Dual-luciferase reporter gene analysis identified a binding site between P65 and IFI27 that may be directly involved in the transcriptional regulation of IFI27. IFI27 knockdown reversed the promoting effect of ADAMTS16 on cell invasion, migration, and proliferation indicating that ADAMTS16 acts on GC cells by targeting the NF-κb/IFI27 axis. ADAMTS16 was associated with poor prognosis in clinical characteristics. ADAMTS16 promotes cell migration, invasion, and proliferation by targeting IFI27 through the NF-κB pathway and is a potential progressive and survival biomarker of GC.

Comparison of outcomes between neoadjuvant imatinib and upfront surgery in patients with localized rectal GIST: An inverse probability of treatment weighting analysis.

BACKGROUND AND OBJECTIVES: This study aimed to compare outcomes between neoadjuvant imatinib and upfront surgery in patients with localized rectal gastrointestinal stromal tumors (GIST) patients. METHODS: Eighty-five patients with localized rectal GIST were divided into two groups: upfront surgery ± adjuvant imatinib (Group A, n = 33) and the neoadjuvant imatinib + surgery + adjuvant imatinib (Group B, n = 52). Baseline characteristics between groups were controlled for with inverse probability of treatment weighting (IPTW) adjusted analysis. RESULTS: The response rate to neoadjuvant imatinib was 65.9%. After the IPTW-adjusted analysis, patients who underwent neoadjuvant therapy had better distant recurrence-free survival (DRFS) and disease-specific survival (DSS) compared with those who underwent upfront surgery (5-year DRFS 97.8 vs. 71.9%, hazard ratio [HR], 0.15; 95% CI, 0.03-0.87; p = 0.03; 5-year DSS 100 vs. 77.1%; HR, 0.11; 95% CI, 0.01-0.92; p = 0.04). While no significant association was found between overall survival (OS) and treatment groups (p = 0.07), 5-year OS was higher for the neoadjuvant group than upfront surgery group (97.8% vs. 71.9%; HR, 0.2; 95% CI, 0.03-1.15). CONCLUSIONS: In patients with localized rectal GIST, neoadjuvant imatinib not only shrunk the tumor size but also decreased the risk of metastasis and tumor-related deaths when compared to upfront surgery and adjuvant imatinib alone.

The projections from the anterior cingulate cortex to the nucleus accumbens and ventral tegmental area contribute to neuropathic pain-evoked aversion in rats.

Although the anterior cingulate cortex (ACC) plays a vital role in neuropathic pain-related aversion, the underlying mechanisms haven't been fully studied. The mesolimbic dopamine system encodes reward and aversion, and participates in the exacerbation of chronic pain. Therefore, we investigated whether the ACC modulates aversion to neuropathic pain via control of the mesolimbic dopamine system, in a rat model of chronic constriction injury (CCI) to the sciatic nerve. Using anterograde and retrograde tracings, we confirmed that a subgroup of ACC neurons projected to the nucleus accumbens (NAc) and ventral tegmental area (VTA), which are two crucial nodes of the mesolimbic dopamine system. Combining electrophysiology in juvenile rats 7 days post-CCI, we found that the NAc/VTA-projecting neurons were hyperexcitable after CCI. Chemogenetic inhibition of these projections induced conditioned place preference in young adult rats 10-14 days post-CCI, without modulating the evoked pain threshold, whereas activation of these projections in sham rats mimicked aversive behavior. Furthermore, the function of the ACC projections was probably mediated by NAc D2-type medium spiny neurons and VTA GABAergic neurons. Taken together, our findings suggest that projections from the ACC to the NAc and VTA mediate neuropathic pain-related aversive behavior.

Capital assets framework for analysing household vulnerability during disaster.

This paper examines the vulnerability of households to disasters, using an asset vulnerability framework to represent livelihoods. Such frameworks are widely employed to analyse household poverty and focus on living conditions and well-being rather than money-metric measures of consumption and income. The conceptualisation of household vulnerability is a challenge in current studies on coping with disasters. The paper considers whether a capital assets framework is useful in identifying and assessing household vulnerability in the context of the Wenchuan earthquake in China in 2008. The framework has five categories of assets (financial, human, natural, physical, and social capital) and attempts to measure the resilience and vulnerability of households. When applied to a major disaster, asset-based methods face the problem of heterogeneity of the population, such as with regard to livelihood type or residence. Moreover, the effect of external interventions, such as the provision of relief assistance, must be taken into account.

Design and Test of a Microdestructive Tree-Ring Measurement System.

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.

A Time Series Data Filling Method Based on LSTM-Taking the Stem Moisture as an Example.

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.

Cognitive Impairment Due to Leptin Withdrawal in Rat Offspring of Dams with Maternal Diet-Induced Obesity.

BACKGROUND Obesity during pregnancy is a potential threat to the health and neurodevelopment of the offspring. This study investigated the effect of maternal diet-induced obesity (DIO) on the cognitive abilities of the offspring in rats. MATERIAL AND METHODS Female Sprague-Dawley rats were fed a high-fat diet to induce obesity, and the leptin levels in dams and offspring were evaluated using ELISA. The effect of DIO on the learning and memory in offspring was measured using electrophysiology and the Morris water maze test. In addition, the expression of molecules related to synaptic plasticity was investigated. Furthermore, the effect of leptin on neuronal cells was investigated, and the influence of leptin on the regulation of calcium current activity was evaluated in vitro. RESULTS Results showed that DIO dams had increased leptin levels during gestation, and offspring had drastically decreased leptin levels after delivery. The cognitive ability of offspring with maternal DIO was mildly impaired after delivery. Furthermore, long-term potentiation in DIO neonatal offspring was lower than in the control group at 2-3 weeks old; decreased expression of the leptin receptor was accompanied by N-methyl-D-aspartate receptor (NMDAR) downregulation during neonatal development. In addition, it was demonstrated that leptin enhanced NMDAR activity and promoted calcium current activity in a concentration-dependent manner. CONCLUSIONS The results indicated that the neonatal offspring of DIO dams showed cognitive impairment during neonatal development, which may be attributed to leptin withdrawal.

An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning.

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.

Determining Forest Duff Water Content Using a Low-Cost Standing Wave Ratio Sensor.

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.

Non-Destructive Methodology to Determine Modulus of Elasticity in Static Bending of Quercus mongolica Using Near-Infrared Spectroscopy.

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.

Inhibition of Metabotropic Glutamate Receptor Subtype 1 Alters the Excitability of the Commissural Pyramidal Neuron in the Rat Anterior Cingulate Cortex after Chronic Constriction Injury to the Sciatic Nerve.

BACKGROUND: Inhibition of the metabotropic glutamate receptor subtype 1 in the anterior cingulate cortex has an analgesic effect during sustained nociceptive hypersensitivity. However, the specific changes in different subtypes of anterior cingulate cortex layer 5 pyramidal neurons, as well as the distinct effect of metabotropic glutamate receptor subtype 1 inhibition on different neuronal subtypes, have not been well studied. METHODS: Retrograde labeling combined with immunofluorescence, whole cell clamp recording, and behavioral tests combined with RNA interference were performed in a rat model of chronic constriction injury to the sciatic nerve. RESULTS: Commissural layer 5 pyramidal neurons (projecting to the contralateral cortex) existed in the anterior cingulate cortex. The voltage-gated potassium channel subunit 2-mediated current in these neurons were substantially reduced after chronic constriction injury (current densities at +30 mV for the sham, and chronic constriction injury neurons were [mean ± SD] 10.22 ± 3.42 pA/pF vs. 5.58 ± 2.71 pA/pF, respectively; n = 11; P < 0.01), which increased the spike width and fast afterhyperpolarization potential, resulting in hyperexcitability. Inhibition of metabotropic glutamate receptor subtype 1 alleviated the down-regulation of voltage-gated potassium channel subunit 2 currents (current density increased by 8.11 ± 3.22 pA/pF; n = 7; P < 0.01). Furthermore, knockdown of voltage-gated potassium channel subunit 2 current in the commissural neurons attenuated the analgesic effect of metabotropic glutamate receptor subtype 1 inhibition (n = 6 rats; P < 0.05). CONCLUSIONS: The effect of metabotropic glutamate receptor subtype 1 inhibition on commissural anterior cingulate cortex layer 5 pyramidal neurons is likely different with the modification of previously studied hyperpolarization-activated/cyclic nucleotide-gated channel-dependent neurons but relies on the alteration of voltage-gated potassium channel subunit 2 currents. These results will contribute to a better understanding of the therapeutic role of metabotropic glutamate receptor subtype 1 in chronic pain.

Real-Time Identification of Smoldering and Flaming Combustion Phases in Forest Using a Wireless Sensor Network-Based Multi-Sensor System and Artificial Neural Network.

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.

The high expression of ADRM1 in hepatocellular carcinoma is closely related to tumor immune infiltration and is regulated by miR-891a-5p.

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.

Construction and validation of a deep learning prognostic model based on digital pathology images of stage III colorectal cancer.

BACKGROUND: TNM staging is the main reference standard for prognostic prediction of colorectal cancer (CRC), but the prognosis heterogeneity of patients with the same stage is still large. This study aimed to classify the tumor microenvironment of patients with stage III CRC and quantify the classified tumor tissues based on deep learning to explore the prognostic value of the developed tumor risk signature (TRS). METHODS: A tissue classification model was developed to identify nine tissues (adipose, background, debris, lymphocytes, mucus, smooth muscle, normal mucosa, stroma, and tumor) in whole-slide images (WSIs) of stage III CRC patients. This model was used to extract tumor tissues from WSIs of 265 stage III CRC patients from The Cancer Genome Atlas and 70 stage III CRC patients from the Sixth Affiliated Hospital of Sun Yat-sen University. We used three different deep learning models for tumor feature extraction and applied a Cox model to establish the TRS. Survival analysis was conducted to explore the prognostic performance of TRS. RESULTS: The tissue classification model achieved 94.4 % accuracy in identifying nine tissue types. The TRS showed a Harrell's concordance index of 0.736, 0.716, and 0.711 in the internal training, internal validation, and external validation sets. Survival analysis showed that TRS had significant predictive ability (hazard ratio: 3.632, p = 0.03) for prognostic prediction. CONCLUSION: The TRS is an independent and significant prognostic factor for PFS of stage III CRC patients and it contributes to risk stratification of patients with different clinical stages.

Association of Collagen Changes in Distal Anastomotic Margin and Anastomotic Stenosis after Neoadjuvant Chemoradiotherapy for Rectal Cancer.

BACKGROUND: Neoadjuvant chemoradiotherapy(nCRT) for rectal cancer can lead to structural changes in collagen in the tumor microenvironment and increase the risk of postoperative anastomotic stenosis (AS). However, the quantitative relationship between AS and collagen has not been defined. This study is to quantitatively analyze the collagen features in rectal cancer and explore the relationship between the changes of collagen and postoperative anastomotic stenosis after nCRT. STUDY DESIGN: This study is a retrospective study. A total of 371 patients with rectal cancer were included. Collagen features in the resection margin of rectal cancer anastomosis was extracted by multi-photon imaging. LASSO-logistic regression was performed to select features related to AS and the collagen score (CS) was constructed. Area under the receiver operating curve (AUROC) and decision curve analysis was performed to evaluate the discrimination and clinical benefit of the nomogram. RESULTS: The probability of AS was 23% in the training cohort and 15.9% in the validation cohort. In the training cohort, the distance between tumor and resection margin, anastomotic leakage and CS were independent risk factors for postoperative AS in univariate and multivariate analyses. A nomogram was constructed based on the above results. The prediction nomogram showed good discrimination (AUROC, 0.864;95% CI, 0.776 to 0.952) and was validated in the validation cohort (AUROC, 0.918;95% CI, 0.851 to 0.985). CONCLUSIONS: CS is an independent risk factor for AS in rectal cancer after nCRT. The predictive model based on CS can predict the occurrence of postoperative AS.