Psychrophilic phage VSW-3 RNA polymerase reduces both terminal and full-length dsRNA byproducts in in vitro transcription.

RNA research and applications are underpinned by in vitro transcription (IVT), but RNA impurities resulting from the enzymatic reagents severely impede downstream applications. To improve the stability and purity of synthesized RNA, we have characterized a novel single-subunit RNA polymerase (RNAP) encoded by the psychrophilic phage VSW-3 from a plateau lake. The VSW-3 RNAP is capable of carrying out in vitro RNA synthesis at low temperatures (4-25°C). Compared to routinely used T7 RNAP, VSW-3 RNAP provides a similar yield of transcripts but is insensitive to class II transcription terminators and synthesizes RNA without redundant 3'-cis extensions. More importantly, through dot-blot detection with the J2 monoclonal antibody, we found that the RNA products synthesized by VSW-3 RNAP contained a much lower amount of double-stranded RNA byproducts (dsRNA), which are produced by transcription from both directions and are significant in T7 RNAP IVT products. Taken together, the VSW-3 RNAP almost eliminates both terminal loop-back dsRNA and full-length dsRNA in IVT and thus is especially advantageous for producing RNA for in vivo use.

The Learning Curve and Importance of Collaboration in Endoscopic Thyroidectomy Via Breast Areola Approach: A Single Surgical Team's Experience of 100 Patients.

BACKGROUND: Endoscopic thyroidectomy is popular among young patients because of its excellent cosmetic outcomes. But it takes a long time to become proficient and competent for surgeons. In addition, collaboration plays a critical role in endoscopic thyroidectomy. Our research aims to evaluate the learning curve of endoscopic thyroidectomy via breast areola approach, provide details of this approach, and demonstrate the importance of collaboration. METHODS: The authors retrospectively analyzed 100 cases of benign and malignant thyroid disease who underwent endoscopic thyroidectomy via breast areola approach between January 2015 and December 2020, which were performed by the same group of surgeons with little experience of endoscopic thyroidectomy. The learning curve was analyzed by moving average method. The mean operation time, blood loss, tumor size, postoperative complications were used to determine learning curve progression. RESULTS: The learning curve in the first 30 cases were uplifted, stable at 30 to 60 cases and declined in the following cases. The mean operation time and blood loss decreased significant after the first 30 cases and again after the first 60 cases. And there was no difference in postoperative complications. CONCLUSIONS: A well-trained surgeon with experience in conventional open thyroidectomy can significantly reduce the total operation time by studying the learning curve. The key steps including establishment of working space and reaching for recurrent laryngeal nerve. A stable level can be achieved after 30 cases. More than 60 cases are required to become proficient. A successful endoscopic thyroid surgery requires a stable team.

Genome-wide miRNA analysis and integrated network for flavonoid biosynthesis in Osmanthus fragrans.

BACKGROUND: Osmanthus fragrans is an important economical plant containing multiple secondary metabolites including flavonoids and anthocyanins. During the past years, the roles of miRNAs in regulating the biosynthesis of secondary metabolites in plants have been widely investigated. However, few studies on miRNA expression profiles and the potential roles in regulating flavonoid biosynthesis have been reported in O. fragrans. RESULTS: In this study, we used high-throughput sequencing technology to analyze the expression profiles of miRNAs in leaf and flower tissues of O. fragrans. As a result, 106 conserved miRNAs distributed in 47 families and 88 novel miRNAs were identified. Further analysis showed there were 133 miRNAs differentially expressed in leaves and flowers. Additionally, the potential target genes of miRNAs as well as the related metabolic pathways were predicted. In the end, flavonoid content was measured in flower and leaf tissues and potential role of miR858 in regulating flavonoid synthesis was illustrated in O. fragrans. CONCLUSIONS: This study not only provided the genome-wide miRNA profiles in the flower and leaf tissue of O. fragrans, but also investigated the potential regulatory role of miR858a in flavonoid synthesis in O. fragrans. The results specifically indicated the connection of miRNAs to the regulation of secondary metabolite biosynthesis in non-model economical plant.

Identification of a ferroptosis-related gene signature predictive model in colon cancer.

BACKGROUND: The prognosis of colon cancer (CC) is challenging to predict due to its highly heterogeneous nature. Ferroptosis, an iron-dependent form of cell death, has roles in various cancers; however, the correlation between ferroptosis-related genes (FRGs) and prognosis in CC remains unclear. METHODS: The expression profiles of FRGs and relevant clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression model were performed to build a prognostic model in TCGA cohort. RESULTS: Ten FRGs, five of which had mutation rates ≥ 3%, were found to be related to the overall survival (OS) of patients with CC. Patients were divided into high- and low-risk groups based on the results of Cox regression and LASSO analysis. Patients in the low-risk group had a significantly longer survival time than patients in the high-risk group (P < 0.001). Enrichment analyses in different risk groups showed that the altered genes were associated with the extracellular matrix, fatty acid metabolism, and peroxisome. Age, risk score, T stage, N stage, and M stage were independent predictors of patient OS based on the results of Cox analysis. Finally, a nomogram was constructed to predict 1-, 3-, and 5-year OS of patients with CC based on the above five independent factors. CONCLUSION: A novel FRG model can be used for prognostic prediction in CC and may be helpful for individualized treatment.

Unveiling dioxin dynamics: A whole-process simulation study of municipal solid waste incineration.

Theoretical research has explained the process of dioxin (DXN) formation in the municipal solid waste incineration (MSWI). This process includes the generation, adsorption, and emission of DXN. Actual DXN concentrations often significantly deviate from theoretical models. This discrepancy is influenced by several key factors: the type of integrated municipal solid waste (MSW) treatment process, the characteristics of the waste, and the operational controls. The progression of DXN generation, adsorption, and emission concentrations within the MSWI process remains unclear. This lack of clarity is especially pronounced when examining the accounting for the specific components of the MSW. To unravel the evolution of DXN, this article proposes a comprehensive numerical simulation model for the entire process of DXN concentration in an MSWI plant. The model is designed based on existing knowledge of MSW combustion and DXN mechanisms, leveraging FLIC and ASPEN simulation software. It incorporates six key stages to facilitate the DXN simulation: precipitation and formation, high-temperature pyrolysis, high-temperature gas-phase synthesis, low-temperature catalytic synthesis, adsorption on activated carbon, and emission to the atmosphere. Under both benchmark and multiple operating conditions, the simulated experiments confirm the effective representation of the evolution of DXN concentrations throughout the process. Consequently, this study presents a model designed to enhance the development of strategies aimed at reducing DXN emissions and to foster innovation in intelligent control technologies.

Dynamic estimates of survival of patients with poorly differentiated thyroid carcinoma: a population-based study.

Background: The real-time prognostic data of patients with poorly differentiated thyroid carcinoma (PDTC) after surviving for several years was unclear. This study aimed to employ a novel method to dynamically estimate survival for PDTC patients. Methods: A total of 913 patients diagnosed with PDTC between 2014 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database, was recruited in our study. Kaplan-Meier method was used to estimate the overall survival (OS). The conditional survival (CS) outcomes of PDTC were analyzed and CS rates were calculated using the formula CS(y/x) = OS(y+x)/OS(x), whereby CS(y/x) denotes the probability of a patient enduring an additional y years subsequent to surviving x years following the diagnosis of PDTC. The least absolute shrinkage and selection operator (LASSO) regression was employed to identify prognostic predicters and multivariate Cox regression was utilized to develop a CS-nomogram. Finally, the performance of this model was evaluated and validated. Results: Kaplan-Meier survival analysis unveiled patient outcomes demonstrating an OS rate of 83%, 75%, and 60% respectively at the end of 3, 5, and 10 years. The novel CS analysis highlighted a progressive enhancement in survival over time, with the 10-year cumulative survival rate progressively augmenting from its initiation of 60% to 66%, 69%, 73%, 77%, 81%, 83%, 88%, 93%, and finally 97% (after surviving for 1-9 years, respectively) each year. And then 11 (11/15) predictors including age at diagnosis, sex, histology type, SEER stage, T stage, N stage, M stage, tumor size, coexistence with other malignancy, radiotherapy and marital status, were selected by LASSO analysis under the condition of lambda.min. Multivariate Cox regression analysis further highlighted the significant impact of all these predictors on the OS of PDTC and we successfully established and validated a novel CS-nomogram for real-time and dynamic survival prediction. Conclusions: This was the first study to analyze the CS pattern and demonstrate a gradual improvement in CS over time in long-term PDTC survivors. We then successfully developed and validated a novel CS-nomogram for individualized, dynamic, and real-time survival forecasting, empowering clinicians to adapt and refine the patient-tailored treatment strategy promptly with consideration of evolving risks.

The impact of coexisting Hashimoto's thyroiditis on the feasibility of endoscopic thyroidectomy in papillary thyroid carcinoma.

Objective: ：The safety of endoscopic thyroidectomy in patients with Hashimoto's thyroiditis (HT) is a matter of concern. This study aimed to assess the effect of concomitant HT on the feasibility of endoscopic thyroidectomy in patients with papillary thyroid carcinoma (PTC). Methods: This study is an observational, retrospective study. All patients were histopathologically diagnosed with HT. The study group consisted of 44 patients (40 %) with PTC who also had HT, whereas the remaining 66 patients (60%) without HT were assigned to the control group. The number of dissected lymph nodes, mean operation time, thyroid volume, blood loss, TSH level, and postoperative complications were recorded and statistically analysed. Results: One patient underwent conversion to open thyroidectomy because of recurrent laryngeal nerve (RLN) transection. Another patient required reoperation owing to postoperative haemorrhage. Statistically significant differences were observed in mean operation time (105.4 ± 10.7 vs 98.2 ± 7.4 min, P = 0.0001),mean thyroid lobe volume (12.2 ± 5.8 vs 9.6 ± 3.5 mL [mL], P = 0.0041), TSH level(4.1 ± 1.5 mIU/L vs 3.4 ± 0.9 mIU/L, P ＝ 0.0028), and the number of dissected lymph nodes between groups (4.1 ± 1.5 vs 3.4 ± 0.9,P = 0.0028). The estimated mean blood loss (31.5 ± 6.8 vs 29.5 ± 3.9 mL, P = 0.0529) and rate of complications (15.9% vs 10.6%, P = 0.4136) did not show statistically significant differences between groups. Conclusion: The coexistence of PTC and HT increases the operation time and difficulties in endoscopic thyroidectomy but does not affect postoperative outcomes. Endoscopic thyroidectomy can be safely performed with acceptable complication rates.

Predictive efficacy of the preoperative neutrophil-lymphocyte ratio in lymph node metastasis of cN0 hormone receptor-positive breast cancer.

Breast cancer, as the most common cancer, has surpassed lung cancer worldwide. The neutrophil-to-lymphocyte ratio (NLR) has been linked to the onset of cancer and its prognosis in recent studies. However, quite a few studies have shown that there is a link between NLR and lymph node metastases in cN0 hormone receptor-positive (HR(+)) breast cancer. The purpose of this study was to evaluate the correlation between NLR and lymph node metastases in cN0 HR(+) breast cancer patients. From January 2012 to January 2022, 220 patients with cN0 HR(+) invasive breast cancers were enrolled in this study. The relationship between NLR and pathological data was statistically examined. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff of NLR, a chi-squared test was used for the univariate analysis, and logistic analysis was used for the multivariate analysis. The NLR had an optimal cutoff of 2.4 when the Jorden index was at a maximum. Patients with axillary lymph node metastases had a higher NLR (P < 0.05). A Univariate analysis showed that there were significant differences in cN0 HR(+) breast cancer with axillary lymph node metastasis among different clinical stages, histological grades, Ki-67 levels, tumor sizes, and NLR levels (P < 0.05). Clinical stage, tumor size, and NLR were found to be independent risk factors for lymph node metastases in multifactorial analysis. In cN0 HR(+) breast cancer, NLR is an independent risk factor for lymph node metastases. An NLR ≥ 2.4 indicates an increased probability of lymph node metastases. An elevated preoperative NLR has a high predictive value for axillary lymph node metastases.

AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration.

Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium-dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.

Delivery of mRNA Vaccine with 1, 2-Diesters-Derived Lipids Elicits Fast Liver Clearance for Safe and Effective Cancer Immunotherapy.

Messenger RNA (mRNA) vaccine is explored as a promising strategy for cancer immunotherapy, but the side effects, especially the liver-related damage caused by LNP, raise concerns about its safety. In this study, a novel library of 248 ionizable lipids comprising 1,2-diesters is designed via a two-step process involving the epoxide ring-opening reaction with carboxyl group-containing alkyl chains followed by an esterification reaction with the tertiary amines. Owing to the special chemical structure of 1,2-diesters, the top-performing lipids and formulations exhibit a faster clearance rate in the liver, contributing to increased stability and higher safety compared with DLin-MC3-DMA. Moreover, the LNP shows superior intramuscular mRNA delivery and elicits robust antigen-specific immune activation. The vaccinations delivered by the LNP system suppress tumor growth and prolong survival in both model human papillomavirus E7 and ovalbumin antigen-expressing tumor models. Finally, the structure of lipids which enhances the protein expression in the spleen and draining lymph nodes compared with ALC-0315 lipid in Comirnaty is further optimized. In conclusion, the 1, 2-diester-derived lipids exhibit rapid liver clearance and effective anticancer efficiency to different types of antigens-expressing tumor models, which may be a safe and universal platform for mRNA vaccines.

α-Lactalbumin mRNA-LNP Evokes an Anti-Tumor Effect Combined with Surgery in Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC) has been considered a huge clinical unmet need due to its aggressive progression and highly frequent metastasis. mRNA therapeutics supply a potential and versatile immunotherapy of oncology treatment. Here, we developed α-lactalbumin mRNA-lipid nanoparticles (α-LNP) as a potential therapeutical strategy for TNBC. The α-LNP induced the specific IgG antibodies and activated IFN γ-secreting-T cells in vivo. Additionally, the safety of α-LNP also had been demonstrated in vivo. When vaccinated prior to tumor implantation, α-LNP showed a preventive effect against 4T1 tumor growth and extended the survival of the tumor model by activating the memory immune responses. Furthermore, α-LNP administration in combination with surgical removal of neoplasm effectively inhibited the progression and metastasis in the TNBC model. Taken together, our results indicate that the α-LNP vaccine is a promising novel treatment for both therapeutics and prophylactics in TNBC.

Novel mRNA adjuvant ImmunER enhances prostate cancer tumor-associated antigen mRNA therapy via augmenting T cell activity.

Prostate cancer (PCa) is characterized as a "cold tumor" with limited immune responses, rendering the tumor resistant to immune checkpoint inhibitors (ICI). Therapeutic messenger RNA (mRNA) vaccines have emerged as a promising strategy to overcome this challenge by enhancing immune reactivity and significantly boosting anti-tumor efficacy. In our study, we synthesized Tetra, an mRNA vaccine mixed with multiple tumor-associated antigens, and ImmunER, an immune-enhancing adjuvant, aiming to induce potent anti-tumor immunity. ImmunER exhibited the capacity to promote dendritic cells (DCs) maturation, enhance DCs migration, and improve antigen presentation at both cellular and animal levels. Moreover, Tetra, in combination with ImmunER, induced a transformation of bone marrow-derived dendritic cells (BMDCs) to cDC1-CCL22 and up-regulated the JAK-STAT1 pathway, promoting the release of IL-12, TNF-α, and other cytokines. This cascade led to enhanced proliferation and activation of T cells, resulting in effective killing of tumor cells. In vivo experiments further revealed that Tetra + ImmunER increased CD8+T cell infiltration and activation in RM-1-PSMA tumor tissues. In summary, our findings underscore the promising potential of the integrated Tetra and ImmunER mRNA-LNP therapy for robust anti-tumor immunity in PCa.

Mpox virus mRNA-lipid nanoparticle vaccine candidates evoke antibody responses and drive protection against the Vaccinia virus challenge in mice.

In response to the human Mpox (hMPX) epidemic that began in 2022, there is an urgent need for a monkeypox vaccine. Here, we have developed a series of mRNA-lipid nanoparticle (mRNA-LNP)-based vaccine candidates that encode a collection of four highly conserved Mpox virus (MPXV) surface proteins involved in virus attachment, entry, and transmission, namely A29L, A35R, B6R, and M1R, which are homologs to Vaccinia virus (VACV) A27, A33, B5, and L1, respectively. Despite possible differences in immunogenicity among the four antigenic mRNA-LNPs, administering these antigenic mRNA-LNPs individually (5 µg each) or an average mixture of these mRNA-LNPs at a low dose (0.5 µg each) twice elicited MPXV-specific IgG antibodies and potent VACV-specific neutralizing antibodies. Furthermore, two doses of 5 µg of A27, B5, and L1 mRNA-LNPs or a 2 µg average mixture of the four antigenic mRNA-LNPs protected mice against weight loss and death after the VACV challenge. Overall, our data suggest that these antigenic mRNA-LNP vaccine candidates are both safe and efficacious against MPXV, as well as diseases caused by other orthopoxviruses.

Investigation on dioxins emission characteristic during complete maintenance operating period of municipal solid waste incineration.

The dioxins (DXN) are a set of pollutants encompass polychlorinated dibenzo-p-dioxin/dibenzofuran (PCDD/F), their emissions from municipal waste incineration processes (MSWI) are normally detected under steady operating conditions. However, limited studies have focused on the PCDD/F emission characteristics under a complete maintenance operating period (CMOP), which includes shut-down, cooling, maintenance, heating, startup, and normal operations. In this article, the shutdown process (SDP) starts from the normal operation, followed by shutdown, and then cooling; while the startup process (SUP) commences from heating, followed by startup, and then normal operation. The detection and analysis were conducted at the SDP and SUP stages. The PCDD/F mass and total toxic equivalent quantity (TEQ) concentrations were measured in the flue gas and bag filter fly ash (BF-FA) during a CMOP of Beijing MSWI plant. The highest PCDD/F concentrations in the flue gas were found in the "cooling" and "startup" phases; in the FA, this condition occurred in the "startup" phase. Further, the results show that the most heightened concentrations were observed for 5-6 chlorinated PCDF and 4-5 chlorinated PCDD among the 17 PCDD/F congeners in most cases. More importantly, the air pollution control devices (APCDs) which include activated carbon, lime, and BF, have high removal efficiency for PCDD/F (especially PCDD) during the "startup" phase. APCDs also easily release a considerable amount of PCDD/F because of the memory effect, which emits more PCDD/F at the "shutdown" phase than at the "startup" one. Besides, the annual PCDD/F emission in the flue gas of the MSWI plant was estimated to be 67.72 mg I-TEQ, of which the emission accounts for approx. 20% during the CMOP. Moreover, the experiment shows that the PCDD/F emissions of the MSWI plant in Beijing under unsteady conditions are more miniature than those reported earlier in other areas.

Assessment of PCDD/Fs formation and emission characteristics at a municipal solid waste incinerator for one year.

Municipal solid waste incineration (MSWI) has become a predominant emission source of polychlorinated dibenzo-p-dioxins and furans (PCDD/Fs). Research focusing on the impact of operating conditions, environmental changes, and operating time on the generation and emissions of PCDD/Fs has not been resolved. To this end, this study tracked and investigated the PCDD/Fs and 17 congener emissions of a typical grate incinerator (800 t/d) continuously for one year. Results showed that the PCDD/Fs concentration at the boiler outlet, stack inlet, and bag filter, including normal and abnormal operation conditions, ranges from 2.11E-02-41.86 ng I-TEQ/Nm3, 7.00E-04-6.76 ng I-TEQ/Nm3, and 1.12-2.90E+03 ng I-TEQ/Nm3, respectively. The 2,3,4,7,8-P5CDF has the highest contribution in all samples, in which a proportion of TEQ ranged from 30 % to 77.73 %. Moreover, by applying the correlation analysis between PCDD/Fs and operating parameters, the emission characteristic is mainly affected by incinerators and boilers during the normal period, and it is affected by the whole MSWI process under abnormal conditions. In addition, the PCDD/Fs emission from the MSWI plant gradually increases from spring to winter. This study is beneficial for supporting the control of PCDD/Fs emission reduction and assisting the operators to optimize the relevant operating parameters of the MSWI plant to achieve a stable and up-to-substandard emissions during the operation period.

Dioxin emission modeling using feature selection and simplified DFR with residual error fitting for the grate-based MSWI process.

Municipal solid waste incineration (MSWI) with grate technology is a widely applied waste-to-energy process in various cities in China. Meanwhile, dioxins (DXN) are emitted at the stack and are the critical environmental indicator for operation optimization control in the MSWI process. However, constructing a high-precision and fast emission model for DXN emission operation optimization control becomes an immediate difficulty. To address the above problem, this research utilizes a novel DXN emission measurement method using simplified deep forest regression (DFR) with residual error fitting (SDFR-ref). First, the high-dimensional process variables are optimally reduced following the mutual information and significance test. Then, a simplified DFR algorithm is established to infer or predict the nonlinearity between the selected process variables and the DXN emission concentration. Moreover, a gradient enhancement strategy in terms of residual error fitting with a step factor is designed to improve the measurement performance in the layer-by-layer learning process. Finally, an actual DXN dataset from 2009 to 2020 of the MSWI plant in Beijing is utilized to verify the SDFR-ref method. Comparison experiments demonstrate the superiority of the proposed method over other methods in terms of measurement accuracy and time consumption.

Advantage of log odds of positive lymph nodes in prognostic evaluation of patients with early-onset colon cancer.

BACKGROUND: Colon cancer (CC) is one of the most common cancers of the digestive tract, the third most common cancer worldwide, and the second most common cause of cancer-related deaths. Previous studies have demonstrated a higher risk of lymph node metastasis (LNM) in young patients with CC. It might be reasonable to treat patients with early-onset locally advanced CC with extended lymph node dissection. However, few studies have focused on early-onset CC (ECC) patients with LNM. At present, the methods of predicting and evaluating the prognosis of ECC patients with LNM are controversial. AIM: To compare the prognostic values of four lymph node staging indices and establish the best nomogram for patients with ECC. METHODS: From the data of patients with CC obtained from the Surveillance, Epidemiology, and End Results (SEER) database, data of young patients with ECC (≤ 50 years old) was screened. Patients with unknown data were excluded from the study, while the remaining patients were included. The patients were randomly divided into a training group (train) and a testing group (test) in the ratio of 7:3, while building the model. The model was constructed by the training group and verified by the testing group. Using multiple Cox regression models to compare the prediction efficiency of LNM indicators, nomograms were built based on the best model selected for overall survival (OS) and cause-specific survival (CSS). In the two groups, the performance of the nomogram was evaluated by constructing a calibration plot, time-dependent area under the curve (AUC), and decision curve analysis. Finally, the patients were grouped based on the risk score predicted by the prognosis model, and the survival curve was constructed after comparing the survival status of the high and low-risk groups. RESULTS: Records of 26922 ECC patients were screened from the SEER database. N classification, positive lymph nodes (PLN), lymph node ratio (LNR) and log odds of PLN (LODDS) were considered to be independent predictors of OS and CSS. In addition, independent risk factors for OS included gender, race, marital status, primary site, histology, grade, T, and M classification, while the independent prognostic factors for CSS included race, marital status, primary site, grade, T, and M classification. The prediction model including LODDS is composed of minimal Akaike information criterion, maximal concordance indexes, and AUCs. Factors including gender, race, marital status, primary site, histology, grade, T, M classification, and LODDS were integrated into the OS nomogram, while race, marital status, primary site, grade, T, M classification, and LODDS were included into the CSS nomogram. The nomogram representing both cohorts had been successfully verified in terms of prediction accuracy and clinical practicability. CONCLUSION: LODDS is superior to N-stage, PLN, and LNR of ECC. The nomogram containing LODDS might be helpful in tumor evaluation and clinical decision-making, since it provides an appropriate prediction of ECC.

Nomogram established using risk factors of early gastric cancer for predicting the lymph node metastasis.

BACKGROUND: For the prognosis of patients with early gastric cancer (EGC), lymph node metastasis (LNM) plays a crucial role. A thorough and precise evaluation of the patient for LNM is now required. AIM: To determine the factors influencing LNM and to construct a prediction model of LNM for EGC patients. METHODS: Clinical information and pathology data of 2217 EGC patients downloaded from the Surveillance, Epidemiology, and End Results database were collected and analyzed. Based on a 7:3 ratio, 1550 people were categorized into training sets and 667 people were assigned to testing sets, randomly. Based on the factors influencing LNM determined by the training sets, the nomogram was drawn and verified. RESULTS: Based on multivariate analysis, age at diagnosis, histology type, grade, T-stage, and size were risk factors of LNM for EGC. Besides, nomogram was drawn to predict the risk of LNM for EGC patients. Among the categorical variables, the effect of grade (well, moderate, and poor) was the most significant prognosis factor. For training sets and testing sets, respectively, area under the receiver-operating characteristic curve of nomograms were 0.751 [95% confidence interval (CI): 0.721-0.782] and 0.786 (95%CI: 0.742-0.830). In addition, the calibration curves showed that the prediction model of LNM had good consistency. CONCLUSION: Age at diagnosis, histology type, grade, T-stage, and tumor size were independent variables for LNM in EGC. Based on the above risk factors, prediction model may offer some guiding implications for the choice of subsequent therapeutic approaches for EGC.

Efficacy and outcome of extensive intraoperative peritoneal lavage plus surgery vs surgery alone with advanced gastric cancer patients.

BACKGROUND: Gastric cancer (GC) is one of the most common malignant tumors. After resection, one of the major problems is its peritoneal dissemination and recurrence. Some free cancer cells may still exist after resection. In addition, the surgery itself may lead to the dissemination of tumor cells. Therefore, it is necessary to remove residual tumor cells. Recently, some researchers found that extensive intraoperative peritoneal lavage (EIPL) plus intraperitoneal chemotherapy can improve the prognosis of patients and eradicate peritoneal free cancer for GC patients. However, few studies explored the safety and long-term outcome of EIPL after curative gastrectomy. AIM: To evaluate the efficacy and long-term outcome of advanced GC patients treated with EIPL. METHODS: According to the inclusion and exclusion criteria, a total of 150 patients with advanced GC were enrolled in this study. The patients were randomly allocated to two groups. All patients received laparotomy. For the non-EIPL group, peritoneal lavage was washed using no more than 3 L of warm saline. In the EIPL group, patients received 10 L or more of saline (1 L at a time) before the closure of the abdomen. The surviving rate analysis was compared by the Kaplan-Meier method. The prognostic factors were carried out using the Cox appropriate hazard pattern. RESULTS: The basic information in the EIPL group and the non-EIPL group had no significant difference. The median follow-up time was 30 mo (range: 0-45 mo). The 1- and 3-year overall survival (OS) rates were 71.0% and 26.5%, respectively. The symptoms of ileus and abdominal abscess appeared more frequently in the non-EIPL group (P < 0.05). For the OS of patients, the EIPL, Borrmann classification, tumor size, N stage, T stage and vascular invasion were significant indicators. Then multivariate analysis revealed that EIPL, tumor size, vascular invasion, N stage and T stage were independent prognostic factors. The prognosis of the EIPL group was better than the non-EIPL group (P < 0.001). The 3-year survival rate of the EIPL group (38.4%) was higher than the non-EIPL group (21.7%). For the recurrence-free survival (RFS) of patients, the risk factor of RFS included EIPL, N stage, vascular invasion, type of surgery, tumor location, Borrmann classification, and tumor size. EIPL and tumor size were independent risk factors. The RFS curve of the EIPL group was better than the non-EIPL group (P = 0.004), and the recurrence rate of the EIPL group (24.7%) was lower than the non-EIPL group (46.4%). The overall recurrence rate and peritoneum recurrence rate in the EIPL group was lower than the non-EIPL group (P < 0.05). CONCLUSION: EIPL can reduce the possibility of perioperative complications including ileus and abdominal abscess. In addition, the overall survival curve and RFS curve were better in the EIPL group.

Dioxin emission prediction based on improved deep forest regression for municipal solid waste incineration process.

Dioxin (DXN) emission concentration is an important environmental indicator in the municipal solid waste incineration (MSWI) process. The prediction model of DXN emission can be used for pollution control to realize actual requirements of operation optimization. Therefore, a DXN emission concentration prediction model based on improved deep forest regression (ImDFR) is proposed in this study. A feature reduction layer based on out-of-bagging error is first introduced into the ImDFR to eliminate redundant variables and feed all confidence information on DXN emission into the feature enhancement layer of the MSWI process. A deep ensemble stacking model is subsequently built to depict deep features and increase diversity and accuracy using random forests, completely random forests, GBDT, and XGBoost as subforests. Finally, the predicted value of the DXN prediction model is determined in the decision layer. The DXN emission prediction model is verified using actual historical data of two incinerators operated with a daily processing capacity of 800 tons. The experimental results showed that the proposed prediction model presents higher accuracy and better generalization ability than state-of-the-art models.