Subtle Structural Differences Affect the Inhibitory Potency of RGD-Containing Cyclic Peptide Inhibitors Targeting SPSB Proteins.

The SPRY domain-containing SOCS box proteins SPSB1, SPSB2, and SPSB4 utilize their SPRY/B30.2 domain to interact with a short region in the N-terminus of inducible nitric oxide synthase (iNOS), and recruit an E3 ubiquitin ligase complex to polyubiquitinate iNOS, resulting in the proteasomal degradation of iNOS. Inhibitors that can disrupt the endogenous SPSB-iNOS interactions could be used to augment cellular NO production, and may have antimicrobial and anticancer activities. We previously reported the rational design of a cyclic peptide inhibitor, cR8, cyclo(RGDINNNV), which bound to SPSB2 with moderate affinity. We, therefore, sought to develop SPSB inhibitors with higher affinity. Here, we show that cyclic peptides cR7, cyclo(RGDINNN), and cR9, cyclo(RGDINNNVE), have ~6.5-fold and ~2-fold, respectively, higher SPSB2-bindng affinities than cR8. We determined high-resolution crystal structures of the SPSB2-cR7 and SPSB2-cR9 complexes, which enabled a good understanding of the structure-activity relationships for these cyclic peptide inhibitors. Moreover, we show that these cyclic peptides displace full-length iNOS from SPSB2, SPSB1, and SPSB4, and that their inhibitory potencies correlate well with their SPSB2-binding affinities. The strongest inhibition was observed for cR7 against all three iNOS-binding SPSB proteins.

Exploring the role of CD8+ T cells in clear renal cell carcinoma metastasis.

Clear cell renal cell carcinoma (ccRCC) accounts for approximately 75-80% of all patients with renal cell carcinoma. Despite its prevalence, little is known regarding the key components involved in ccRCC metastasis. In this study, scRNA-seq analysis was employed to classify CD8+ T cells into four sub-clusters based on their genetic profiles and immunofluorescence experiments were used to validate two key clusters. Through gene set enrichment analysis, these newly identified sub-clusters were found to exhibit distinct biological characteristics. Notably, TYMP, TOP2A, CHI3L2, CDKN3, CENPM, and RZH2 were highly expressed in these sub-clusters, indicating a correlation with poor prognosis. Among these sub-clusters, CD8+ T cells (MT-ND4) were identified as potentially playing a critical role in mediating ccRCC metastasis. These results contribute to our understanding of CD8+ T cell heterogeneity in ccRCC and shed light on the mechanisms underlying the loss of immune response against cancer.

Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study.

RATIONALE AND OBJECTIVES: The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC). MATERIALS AND METHODS: In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness. RESULTS: The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI. CONCLUSION: The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

Impact of hyperoxia on the gut during critical illnesses.

Molecular oxygen is typically delivered to patients via oxygen inhalation or extracorporeal membrane oxygenation (ECMO), potentially resulting in systemic hyperoxia from liberal oxygen inhalation or localized hyperoxia in the lower body from peripheral venoarterial (VA) ECMO. Consequently, this exposes the gastrointestinal tract to excessive oxygen levels. Hyperoxia can trigger organ damage due to the overproduction of reactive oxygen species and is associated with increased mortality. The gut and gut microbiome play pivotal roles in critical illnesses and even small variations in oxygen levels can have a dramatic influence on the physiology and ecology of gut microbes. Here, we reviewed the emerging preclinical evidence which highlights how excessive inhaled oxygen can provoke diffuse villous damage, barrier dysfunction in the gut, and gut dysbiosis. The hallmark of this dysbiosis includes the expansion of oxygen-tolerant pathogens (e.g., Enterobacteriaceae) and the depletion of beneficial oxygen-intolerant microbes (e.g., Muribaculaceae). Furthermore, we discussed potential impact of oxygen on the gut in various underlying critical illnesses involving inspiratory oxygen and peripheral VA-ECMO. Currently, the available findings in this area are somewhat controversial, and a consensus has not yet to be reached. It appears that targeting near-physiological oxygenation levels may offer a means to avoid hyperoxia-induced gut injury and hypoxia-induced mesenteric ischemia. However, the optimal oxygenation target may vary depending on special clinical conditions, including acute hypoxia in adults and neonates, as well as particular patients undergoing gastrointestinal surgery or VA-ECMO support. Last, we outlined the current challenges and the need for future studies in this area. Insights into this vital ongoing research can assist clinicians in optimizing oxygenation for critically ill patients.

Association between atherogenic index of plasma control level and incident cardiovascular disease in middle-aged and elderly Chinese individuals with abnormal glucose metabolism.

BACKGROUND: The atherogenic index of plasma (AIP) and cardiovascular disease (CVD) in participants with abnormal glucose metabolism have been linked in previous studies. However, it was unclear whether AIP control level affects the further CVD incidence among with diabetes and pre-diabetes. Therefore, our study aimed to investigate the association between AIP control level with risk of CVD in individuals with abnormal glucose metabolism. METHODS: Participants with abnormal glucose metabolism were included from the China Health and Retirement Longitudinal Study. CVD was defined as self-reporting heart disease and/or stroke. Using k-means clustering analysis, AIP control level, which was the log-transformed ratio of triglyceride to high-density lipoprotein cholesterol in molar concentration, was divided into five classes. The association between AIP control level and incident CVD among individuals with abnormal glucose metabolism was investigated multivariable logistic regression analysis and application of restricted cubic spline analysis. RESULTS: 398 (14.97%) of 2,659 participants eventually progressed to CVD within 3 years. After adjusting for various confounding factors, comparing to class 1 with the best control of the AIP, the OR for class 2 with good control was 1.31 (95% CI, 0.90-1.90), the OR for class 3 with moderate control was 1.38 (95% CI, 0.99-1.93), the OR for class 4 with worse control was 1.46 (95% CI, 1.01-2.10), and the OR for class 5 with consistently high levels was 1.56 (95% CI, 1.03-2.37). In restricted cubic spline regression, the relationship between cumulative AIP index and CVD is linear. Further subgroup analysis demonstrated that the similar results were observed in the individuals with agricultural Hukou, history of smoking, diastolic blood pressure ≥ 80mmHg, and normal body mass index. In addition, there was no interaction between the AIP control level and the subgroup variables. CONCLUSIONS: In middle-aged and elderly participants with abnormal glucose metabolism, constant higher AIP with worst control may have a higher incidence of CVD. Monitoring long-term AIP change will contribute to early identification of high risk of CVD among individuals with abnormal glucose metabolism.

Current status and progress in research on dressing management for diabetic foot ulcer.

Diabetic foot ulcer (DFU) is a major complication of diabetes and is associated with a high risk of lower limb amputation and mortality. During their lifetime, 19%-34% of patients with diabetes can develop DFU. It is estimated that 61% of DFU become infected and 15% of those with DFU require amputation. Furthermore, developing a DFU increases the risk of mortality by 50%-68% at 5 years, higher than some cancers. Current standard management of DFU includes surgical debridement, the use of topical dressings and wound decompression, vascular assessment, and glycemic control. Among these methods, local treatment with dressings builds a protective physical barrier, maintains a moist environment, and drains the exudate from DFU wounds. This review summarizes the development, pathophysiology, and healing mechanisms of DFU. The latest research progress and the main application of dressings in laboratory and clinical stage are also summarized. The dressings discussed in this review include traditional dressings (gauze, oil yarn, traditional Chinese medicine, and others), basic dressings (hydrogel, hydrocolloid, sponge, foam, film agents, and others), bacteriostatic dressings, composite dressings (collagen, nanomaterials, chitosan dressings, and others), bioactive dressings (scaffold dressings with stem cells, decellularized wound matrix, autologous platelet enrichment plasma, and others), and dressings that use modern technology (3D bioprinting, photothermal effects, bioelectric dressings, microneedle dressings, smart bandages, orthopedic prosthetics and regenerative medicine). The dressing management challenges and limitations are also summarized. The purpose of this review is to help readers understand the pathogenesis and healing mechanism of DFU, help physicians select dressings correctly, provide an updated overview of the potential of biomaterials and devices and their application in DFU management, and provide ideas for further exploration and development of dressings. Proper use of dressings can promote DFU healing, reduce the cost of treating DFU, and reduce patient pain.

MicroRNA-210-3p mediates trabecular meshwork extracellular matrix accumulation and ocular hypertension - Implication for novel glaucoma therapy.

Elevation of intraocular pressure (IOP) is a major, controllable risk factor of primary open-angle glaucoma (POAG). Transforming growth factor-ß2 (TGF-ß2)-induced excessive accumulation of extracellular matrix (ECM) in the trabecular meshwork (TM) has been demonstrated to contribute significantly to the development of high IOP. We previously showed that treatment with salidroside (Sal), a plant-derived glucoside, can ameliorate the TGF-ß2-induced ECM expression in cultured human TM cells and reduce TGF-ß2-induced ocular hypertension in mice. In the current study, its underlying molecular mechanism associated with microRNA-210-3p (miR-210-3p) was characterized. We discovered that, in TM tissues of POAG patients, there was an increase in miR-210-3p. And miR-210-3p mediated a portion of the pathological effects of TGF-ß2 in vitro (excessive accumulation of ECM in cultured human TM cells) and in vivo (mouse ocular hypertension and ECM accumulation in the TM). Most interestingly, miR-210-3p was down-regulated by Sal, which appeared to mediate a significant portion of its IOP-lowering effect. Thus, these results shed light on the probable molecular mechanisms of TGF-ß2 and Sal and indicate that manipulation of miR-210-3p level/activity represents a potential new therapeutic strategy for POAG.

Predict DLBCL patients' recurrence within two years with Gaussian mixture model cluster oversampling and multi-kernel learning.

BACKGROUND AND OBJECTIVE: Diffuse large B-cell lymphoma (DLBCL) is common in adults' non-Hodgkin's lymphoma. Relapse mainly occurs within two years after diagnosis and has a poor prognosis. Relapse after two years is less frequent and has a better prognosis. In this work, we constructed a relapse prediction model for diffuse large B-cell lymphoma patients within two years, expecting to provide a reference for Clinicians to implement individualized treatment. METHOD: We propose a secondary-level class imbalance method based on Gaussian mixture model (GMM) clustering resampling to balance the data. Then use a multi-kernel support vector machine(SVM) to inscribe heterogeneous clinical data. Finally, merging them to identify recurrence patients within two years. RESULTS: Among all the class imbalance methods in this work, Inverse Weighted -GMM +SMOTEENN has the best performance. Compared with NO-GMM (Directl use the SMOTEENN without the GMM clustering process), its Area Under the ROC Curve(AUC) increases by 8.75%, and ECE and brier scores decrease 2.07% and 3.09%, respectively. Among the four classification algorithms in this work, Multiple kernel learning (MKL) has the most minimized brier scores and expected calibration error(ECE), the largest AUC, accuracy, Recall, precision and F1, has the best discrimination and calibration. CONCLUSION: Our inverse weighted -GMM+SMOTEENN+MKL (GMM-SENN-MKL) method can handle data class imbalance and clinical heterogeneity data well and can be used to predict recurrence in DLBCL patients.

[Clinical features of intestinal polyps and risk factors for secondary intussusception in children: an analysis of 2 669 cases]. / 2 669ä¾‹å„¿ç«¥è‚ æ¯è‚‰çš„ä¸´åºŠç‰¹å¾åŠç»§å‘è‚ å¥—å çš„å±é™©å› ç´ åˆ†æž.

OBJECTIVES: To study the clinical features of intestinal polyps and the risk factors for secondary intussusception in children. METHODS: A retrospective analysis was performed for the medical data of 2 669 children with intestinal polyps. According to the presence or absence of secondary intussusception, they were divided into two groups: intussusception (n=346) and non-intussusception (n=2 323). Related medical data were compared between the two groups. The multivariate logistic regression analysis was used to identify the risk factors for secondary intussusception. RESULTS: Among the children with intestinal polyps, 62.42% were preschool children, and the male/female ratio was 2.08∶1; 92.66% had hematochezia as disease onset, and 94.34% had left colonic polyps and rectal polyps. There were 346 cases of secondary intussusception, with an incidence rate of 12.96% (346/2 669). Large polyps (OR=1.644, P<0.001), multiple polyps (≥2) (OR=6.034, P<0.001), and lobulated polyps (OR=93.801, P<0.001) were the risk factors for secondary intussusception. CONCLUSIONS: Intestinal polyps in children often occur in preschool age, mostly in boys, and most of the children have hematochezia as disease onset, with the predilection sites of the left colon and the rectum. Larger polyps, multiple polyps, and lobulated polyps may increase the risk of secondary intussusception, and endoscopic intervention is needed as early as possible to improve prognosis.

Medical Image Classification Based on Information Interaction Perception Mechanism.

Colorectal cancer originates from adenomatous polyps. Adenomatous polyps start out as benign, but over time they can become malignant and even lead to complications and death which will spread to adherent and surrounding organs over time, such as lymph nodes, liver, or lungs, eventually leading to complications and death. Factors such as operator's experience shortage and visual fatigue will directly affect the diagnostic accuracy of colonoscopy. To relieve the pressure on medical imaging personnel, this paper proposed a network model for colonic polyp detection using colonoscopy images. Considering the unnoticeable surface texture of colonic polyps, this paper designed a channel information interaction perception (CIIP) module. Based on this module, an information interaction perception network (IIP-Net) is proposed. In order to improve the accuracy of classification and reduce the cost of calculation, the network used three classifiers for classification: fully connected (FC) structure, global average pooling fully connected (GAP-FC) structure, and convolution global average pooling (C-GAP) structure. We evaluated the performance of IIP-Net by randomly selecting colonoscopy images from a gastroscopy database. The experimental results showed that the overall accuracy of IIP-NET54-GAP-FC module is 99.59%, and the accuracy of colonic polyp is 99.40%. By contrast, our IIP-NET54-GAP-FC performed extremely well.

Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma.

BACKGROUND: Although many patients receive good prognoses with standard therapy, 30-50% of diffuse large B-cell lymphoma (DLBCL) cases may relapse after treatment. Statistical or computational intelligent models are powerful tools for assessing prognoses; however, many cannot generate accurate risk (probability) estimates. Thus, probability calibration-based versions of traditional machine learning algorithms are developed in this paper to predict the risk of relapse in patients with DLBCL. METHODS: Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). Performance comparisons were based on the average results of the stratified hold-out test, which was repeated 500 times. We used the AUC to evaluate the discrimination ability (i.e., classification ability) of the model and assessed the model calibration (i.e., risk prediction accuracy) using the H-L goodness-of-fit test, ECE, MCE and BS. RESULTS: Sex, stage, IPI, KPS, GCB, CD10 and rituximab were significant factors predicting the 3-year recurrence rate of patients with DLBCL. For the 5 uncalibrated algorithms, the LR (ECE = 8.517, MCE = 20.100, BS = 0.188) and FFNN (ECE = 8.238, MCE = 20.150, BS = 0.184) models were well-calibrated. The errors of the initial risk estimate of the NB (ECE = 15.711, MCE = 34.350, BS = 0.212), RF (ECE = 12.740, MCE = 27.200, BS = 0.201) and SVM (ECE = 9.872, MCE = 23.800, BS = 0.194) models were large. With probability calibration, the biased NB, RF and SVM models were well-corrected. The calibration errors of the LR and FFNN models were not further improved regardless of the probability calibration method. Among the 3 calibration methods, RPR achieved the best calibration for both the RF and SVM models. The power of IsoReg was not obvious for the NB, RF or SVM models. CONCLUSIONS: Although these algorithms all have good classification ability, several cannot generate accurate risk estimates. Probability calibration is an effective method of improving the accuracy of these poorly calibrated algorithms. Our risk model of DLBCL demonstrates good discrimination and calibration ability and has the potential to help clinicians make optimal therapeutic decisions to achieve precision medicine.

[Clostridium difficile infection and its susceptibility factors in children with inflammatory bowel disease].

OBJECTIVE: To investigate the incidence rates of Clostridium difficile colonization and Clostridium difficile infection (CDI) in children with inflammatory bowel disease (IBD) and the susceptibility factors for CDI in children with IBD. METHODS: A total of 62 children diagnosed with IBD were enrolled as the IBD group. Forty-two children who attended the hospital due to persistent or chronic diarrhea and were excluded from IBD were enrolled as the non-IBD group. The incidence rate of CDI was compared between the two groups. According to the presence or absence of CDI, the IBD group was subdivided into two groups:IBD+CDI (n=12) and non-CDI IBD (n=50), and the clinical data were collected from the two groups to analyze the susceptibility factors for CDI. RESULTS: The IBD group had a significantly higher incidence rate of CDI[19% (12/62) vs 2% (1/42); P < 0.05] than the non-IBD group (P < 0.05). Compared with the non-CDI IBD group, the IBD+CDI group had a significantly longer disease course (P < 0.05), and a significantly higher proportion of children with fever, diarrhea, or abdominal pain (P < 0.05). The IBD+CDI group had significantly higher activity indices of pediatric Crohn's disease, C-reactive protein levels and erythrocyte sedimentation rate than the non-CDI IBD group (P < 0.05). The univariate analysis showed that compared with the non-CDI IBD group, the IBD+CDI group had a significantly higher proportion of children with moderate-to-severe disease, use of glucocorticoids, or treatment with broad-spectrum antibiotics for more than 14 days before diagnosis (P < 0.05). CONCLUSIONS: The children with IBD have a higher incidence of CDI than those without IBD. Severe disease conditions and use of broad-spectrum antibiotics or glucocorticoids may be associated with an increased incidence of CDI in children with IBD.

Evaluation of the Effects of Different Bacteroides vulgatus Strains against DSS-Induced Colitis.

Although the strain-dependent effects of Bacteroides vulgatus on alleviating intestinal inflammatory diseases have been demonstrated, the literature has rarely focused on the underlying causes of this effect. In this study, we selected four B. vulgatus strains (FTJS5K1, FTJS7K1, FSDTA11B14, and FSDLZ51K1) with different genomic characteristics and evaluated their protective roles against dextran sulfate sodium- (DSS-) induced colitis. Compared to the other three tested strains, B. vulgatus 7K1 more strongly ameliorated the DSS-induced weight loss, shortening of the colon length, increased disease activity index scores, colonic tissue injury, and immunomodulatory disorder. In contrast, B. vulgatus 51K1 significantly worsened the DSS-induced alterations in the tumor necrosis factor-alpha (TNF-α) concentration and colonic histopathology. A comparative genomic analysis of B. vulgatus 7K1 and 51K1 showed that the beneficial effects of B. vulgatus 7K1 may be associated with some of its specific genes involved in the production of short-chain fatty acids or capsular polysaccharides and enhancement of its survivability in the gut. In conclusion, these findings indicate that the supplementation of B. vulgatus 7K1 is a potentially efficacious intervention for alleviating colitis and provides scientific support for the screening of probiotics with anticolitis effect.

Structural basis for the regulation of inducible nitric oxide synthase by the SPRY domain-containing SOCS box protein SPSB2, an E3 ubiquitin ligase.

Relatively high concentration of nitric oxide (NO) produced by inducible nitric oxide synthase (iNOS) in response to a variety of stimuli is a source of reactive nitrogen species, an important weapon of host innate immune defense. The SPRY domain-containing SOCS box protein 2 (SPSB2) is an E3 ubiquitin ligase that regulates the lifetime of iNOS. SPSB2 interacts with the N-terminal region of iNOS via a binding site on the SPRY domain of SPSB2, and recruits an E3 ubiquitin ligase complex to polyubiquitinate iNOS, leading to its proteasomal degradation. Although critical residues for the SPSB2-iNOS interaction have been identified, structural basis for the interaction remains to be explicitly determined. In this study, we have determined a crystal structure of the N-terminal region of iNOS in complex with the SPRY domain of SPSB2 at 1.24 Å resolution. We have resolved the roles of some flanking residues, whose contribution to the SPSB2-iNOS interaction was structurally unclear previously. Furthermore, we have evaluated the effects of SPSB2 inhibitors on NO production using transient transfection and cell-penetrating peptide approaches, and found that such inhibitors can elevate NO production in RAW264.7 macrophages. These results thus provide a useful basis for the development of potent SPSB2 inhibitors as well as recruiting ligands for proteolysis targeting chimera (PROTAC) design.

Applying probability calibration to ensemble methods to predict 2-year mortality in patients with DLBCL.

BACKGROUND: Under the influences of chemotherapy regimens, clinical staging, immunologic expressions and other factors, the survival rates of patients with diffuse large B-cell lymphoma (DLBCL) are different. The accurate prediction of mortality hazards is key to precision medicine, which can help clinicians make optimal therapeutic decisions to extend the survival times of individual patients with DLBCL. Thus, we have developed a predictive model to predict the mortality hazard of DLBCL patients within 2 years of treatment. METHODS: We evaluated 406 patients with DLBCL and collected 17 variables from each patient. The predictive variables were selected by the Cox model, the logistic model and the random forest algorithm. Five classifiers were chosen as the base models for ensemble learning: the naïve Bayes, logistic regression, random forest, support vector machine and feedforward neural network models. We first calibrated the biased outputs from the five base models by using probability calibration methods (including shape-restricted polynomial regression, Platt scaling and isotonic regression). Then, we aggregated the outputs from the various base models to predict the 2-year mortality of DLBCL patients by using three strategies (stacking, simple averaging and weighted averaging). Finally, we assessed model performance over 300 hold-out tests. RESULTS: Gender, stage, IPI, KPS and rituximab were significant factors for predicting the deaths of DLBCL patients within 2 years of treatment. The stacking model that first calibrated the base model by shape-restricted polynomial regression performed best (AUC = 0.820, ECE = 8.983, MCE = 21.265) in all methods. In contrast, the performance of the stacking model without undergoing probability calibration is inferior (AUC = 0.806, ECE = 9.866, MCE = 24.850). In the simple averaging model and weighted averaging model, the prediction error of the ensemble model also decreased with probability calibration. CONCLUSIONS: Among all the methods compared, the proposed model has the lowest prediction error when predicting the 2-year mortality of DLBCL patients. These promising results may indicate that our modeling strategy of applying probability calibration to ensemble learning is successful.

A state-of-the-art review of quinoline degradation and technical bottlenecks.

Quinoline is a critical raw material for the dye, metallurgy, pharmaceutical, rubber, and agrochemical industries, and its use poses a serious threat to human health and the ecological environment. Quinoline has carcinogenic, teratogenic and mutagenic effects on the human body through food accumulation. However, due to the steric hindrance of its bicyclic fused structure and its long photooxidation half-life, quinoline is too difficult to decompose naturally. To date, numerous technologies have been used to degrade quinoline, whereas only a few have been reviewed. Therefore, this paper is focused on offering a comprehensive overview of the state of quinoline degradation in an effort to improve its degradation efficiency and fully utilize the carbon and nitrogen within quinoline without causing any damage to the environment. Accordingly, the strains, research progress and mechanisms of various methods for degrading quinoline are explored and elucidated in detail, especially quinoline biodegradation and the combination of these technologies for efficient removal. The state-of-the-art processes and new findings of our team on the biofortification of quinoline degradation are also presented. Finally, research bottlenecks and gaps for future research were identified along with the prospects and resource utilization of quinoline. These discussions facilitate the realization of the zero discharge of quinoline.

An improved deep learning approach and its applications on colonic polyp images detection.

BACKGROUND: Colonic polyps are more likely to be cancerous, especially those with large diameter, large number and atypical hyperplasia. If colonic polyps cannot be treated in early stage, they are likely to develop into colon cancer. Colonoscopy is easily limited by the operator's experience, and factors such as inexperience and visual fatigue will directly affect the accuracy of diagnosis. Cooperating with Hunan children's hospital, we proposed and improved a deep learning approach with global average pooling (GAP) in colonoscopy for assisted diagnosis. Our approach for assisted diagnosis in colonoscopy can prompt endoscopists to pay attention to polyps that may be ignored in real time, improve the detection rate, reduce missed diagnosis, and improve the efficiency of medical diagnosis. METHODS: We selected colonoscopy images from the gastrointestinal endoscopy room of Hunan children's hospital to form the colonic polyp datasets. And we applied the image classification method based on Deep Learning to the classification of Colonic Polyps. The classic networks we used are VGGNets and ResNets. By using global average pooling, we proposed the improved approaches: VGGNets-GAP and ResNets-GAP. RESULTS: The accuracies of all models in datasets exceed 98%. The TPR and TNR are above 96 and 98% respectively. In addition, VGGNets-GAP networks not only have high classification accuracies, but also have much fewer parameters than those of VGGNets. CONCLUSIONS: The experimental results show that the proposed approach has good effect on the automatic detection of colonic polyps. The innovations of our method are in two aspects: (1) the detection accuracy of colonic polyps has been improved. (2) our approach reduces the memory consumption and makes the model lightweight. Compared with the original VGG networks, the parameters of our VGG19-GAP networks are greatly reduced.

CA-PROM: Validation of a general patient-reported outcomes measure for Chinese patients with cancer.

BACKGROUND: Based the important role of patient-reported outcome in measuring patients' QoL, a general PRO instrument was designed for Chinese patients with cancer. METHODS: The instrument was administered in eight hospitals. Based on PRO guidelines, a conceptual framework and item pool were generated after literature review and patients' interviews. Via two-item selection process, the original version of a cancer PRO measure (CA-PROM) was generated. Patients' responsiveness was evaluated in four disease systems by item response theory. The reliability, validity, and feasibility of CA-PROM were assessed. The minimum clinically important differences (MCIDs) and risk thresholds of PRO were calculated. RESULTS: A total of 2213 valid questionnaires were collected. After expert opinions and cognitive tests, 11 items were deleted. In the pre-survey and formal survey, 19 items were deleted based on six methods of classical test theory. In the respiratory, digestive, hematological, and endocrine systems, four items with poor responsiveness were deleted by item response theory. The final CA-PROM included four domains, 13 subdomains, and 49 items. Reliability coefficients of 13 subdomain was > 0.7. The framework of CA-PROM met required criteria by CFA and OBID. The average response time was 14.2 min, indicating feasibility of CA-PROM. The MCIDs were 5.63, 3.42, 4.16 in the physiological, psychological, social domain, respectively. The risk thresholds of PRO for six subdomains were 71.74, 71.28, 66.29, 65.16, 59.56, 66.60, in that order. CONCLUSION: The developed CA-PROM exhibited good reliability, validity, and feasibility, and can be used as an effective evaluation tool in cancer patients.

Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods.

BACKGROUND: Treatments are limited for patients with relapsed/refractory Diffuse large B-cell lymphoma (DLBCL), and their survival rate is low. Prediction of the recurrence hazard for each patient could provide a reference regarding chemotherapy regimens for clinicians to extend patients' period of long-term remission. As current strategies cannot satisfy such need, we have established predictive models to classify patients with DLBCL with complete remission who had recurrences in 2 years from ones who did not. METHODS: We assessed 518 patients with DLBCL and measured 52 variables of each patient. They were treated between January 2011 and July 2016. 17 variables were first selected by variable selection methods (including Lasso, Adaptive Lasso, and Elastic net). Then, we set classifiers and probability models for imbalanced data by combining the SMOTE sampling, cost-sensitive, and ensemble learning (consisting of AdaBoost, voting strategy, and Stacking) methods with the machine learning methods (Support Vector Machine, BackPropagation Artificial Neural Network, Random Forest), respectively. Last, assessed their performance. RESULTS: The disease stage and other 5 variables are significant indicators for recurrence. The SVM with AdaBoost ensemble learning method modeling by SMOTE data performs the best (Sensitivity=97.3%, AUC=96%, RMSE=19.6%, G-mean=96%) in all classifiers. The SVM with AdaBoost method(AUC=98.7%, RMSE=17.7%, MXE=12.7%, Cal mean=3.2%, BS0=2.5%, BS1=4%, BSALL=3.1%) and random forest (AUC=99.5%, RMSE=19.8%, MXE=16.2%, Cal mean=9.1%, BS0=4.8%, BS1=2.9%, BSALL=3.9%) both modeling by SMOTE sampling data perform well in probability models. CONCLUSIONS: This predictive model has high accuracy for almost all DLBCL patients and the six indicators can be recurrence signals.

GC-PROM: validation of a patient-reported outcomes measure for Chinese patients with gastric cancer.

BACKGROUND: There is increasing recognition that PROs are important in the estimation of the burden of long-term survival among patients with gastric cancer. The study aimed to develop a disease-specific instrument to assess patient-reported outcomes for Chinese patients with gastric cancer. METHOD: Following the FDA's draft guidance for patient-reported outcome, conceptual framework and item pool were defined based on relevant existing work. A draft scale was formed after revising some items based on feedback from experts and Chinese patients with gastric cancer. The pre-survey and formal survey were conducted in eight different hospitals in Shanxi Province, and two item-selection process based on classical test theory and item response theory. Finally, the patient-reported outcomes measure for Chinese patients with gastric cancer (GC-PROM) was validated in terms of reliability, validity, and feasibility. The minimal clinically important difference was determined by distribution-based method. RESULTS: The final GC-PROM consisted of 38 items, 13 subdomains, and 4 domains. Reliability was verified by Cronbach's alpha coefficient for four domains and 13 subdomains respectively. The validity results showed that the multidimensional scale fulfilled expectations. In the formal survey, the completion rate was 96.16%, and the average filling time was less than half an hour. The values of the minimal clinically important difference were 4.14, 3.41, 3.37, and 3.28 in the four domains. CONCLUSIONS: The GC-PROM had good reliability, validity, and feasibility and thus can be considered an effective clinical evaluation instrument for Chinese patients with gastric cancer.