[Incidence of postoperative complications in Chinese patients with gastric or colorectal cancer based on a national, multicenter, prospective, cohort study]

Objective: To investigate the incidence of postoperative complications in Chinese patients with gastric or colorectal cancer, and to evaluate the risk factors for postoperative complications. Methods: This was a national, multicenter, prospective, registry-based, cohort study of data obtained from the database of the Prevalence of Abdominal Complications After Gastro- enterological Surgery (PACAGE) study sponsored by the China Gastrointestinal Cancer Surgical Union. The PACAGE database prospectively collected general demographic characteristics, protocols for perioperative treatment, and variables associated with postoperative complications in patients treated for gastric or colorectal cancer in 20 medical centers from December 2018 to December 2020. The patients were grouped according to the presence or absence of postoperative complications. Postoperative complications were categorized and graded in accordance with the expert consensus on postoperative complications in gastrointestinal oncology surgery and Clavien-Dindo grading criteria. The incidence of postoperative complications of different grades are presented as bar charts. Independent risk factors for occurrence of postoperative complications were identified by multifactorial unconditional logistic regression. Results: The study cohort comprised 3926 patients with gastric or colorectal cancer, 657 (16.7%) of whom had a total of 876 postoperative complications. Serious complications (Grade III and above) occurred in 4.0% of patients (156/3926). The rate of Grade V complications was 0.2% (7/3926). The cohort included 2271 patients with gastric cancer with a postoperative complication rate of 18.1% (412/2271) and serious complication rate of 4.7% (106/2271); and 1655 with colorectal cancer, with a postoperative complication rate of 14.8% (245/1655) and serious complication rate of 3.0% (50/1655). The incidences of anastomotic leakage in patients with gastric and colorectal cancer were 3.3% (74/2271) and 3.4% (56/1655), respectively. Abdominal infection was the most frequently occurring complication, accounting for 28.7% (164/572) and 39.5% (120/304) of postoperative complications in patients with gastric and colorectal cancer, respectively. The most frequently occurring grade of postoperative complication was Grade II, accounting for 65.4% (374/572) and 56.6% (172/304) of complications in patients with gastric and colorectal cancers, respectively. Multifactorial analysis identified (1) the following independent risk factors for postoperative complications in patients in the gastric cancer group: preoperative comorbidities (OR=2.54, 95%CI: 1.51-4.28, P<0.001), neoadjuvant therapy (OR=1.42, 95%CI:1.06-1.89, P=0.020), high American Society of Anesthesiologists (ASA) scores (ASA score 2 points:OR=1.60, 95% CI: 1.23-2.07, P<0.001, ASA score ≥3 points:OR=0.43, 95% CI: 0.25-0.73, P=0.002), operative time >180 minutes (OR=1.81, 95% CI: 1.42-2.31, P<0.001), intraoperative bleeding >50 mL (OR=1.29,95%CI: 1.01-1.63, P=0.038), and distal gastrectomy compared with total gastrectomy (OR=0.65,95%CI: 0.51-0.83, P<0.001); and (2) the following independent risk factors for postoperative complications in patients in the colorectal cancer group: female (OR=0.60, 95%CI: 0.44-0.80, P<0.001), preoperative comorbidities (OR=2.73, 95%CI: 1.25-5.99, P=0.030), neoadjuvant therapy (OR=1.83, 95%CI:1.23-2.72, P=0.008), laparoscopic surgery (OR=0.47, 95%CI: 0.30-0.72, P=0.022), and abdominoperineal resection compared with low anterior resection (OR=2.74, 95%CI: 1.71-4.41, P<0.001). Conclusion: Postoperative complications associated with various types of infection were the most frequent complications in patients with gastric or colorectal cancer. Although the risk factors for postoperative complications differed between patients with gastric cancer and those with colorectal cancer, the presence of preoperative comorbidities, administration of neoadjuvant therapy, and extent of surgical resection, were the commonest factors associated with postoperative complications in patients of both categories.

Objective Boundary Generation for Gross Target Volume and Organs at Risk Using 3D Multi-Modal Medical Images

PURPOSE/OBJECTIVE(S)

Accurate delineation of Gross Target Volume (GTV) and Organs at Risk (OARs) in medical images is an essential but challenging step in radiotherapy. Deep-learning based automated delineation methods, which learn from manual annotations, are currently prevalent in academic research. However, the limited resolution of medical images and the fuzzy boundaries of lesions and organs present a challenge to the precision of manual annotations. By leveraging the complementary information from multi-modal medical images, this study proposed a novel method to generate objective boundaries of GTV and OARs.

MATERIALS/METHODS

We present a novel method of objective boundary generation, inspired by image matting primarily used for 2D RGB natural images, to process 3D grayscale medical images. The proposed method has the following advantages. 1) It allows for flexible input modalities and assigns weights to each modality according to their relative significance when computing information flows in the matting algorithm. 2) It computes 3D spatial information flow among voxels, which has more advantages over its 2D counterpart. 3) It has a closed-form solution that generates deterministic results. To evaluate the characteristics of the generated boundaries, patients with stage I nasopharyngeal carcinoma (NPC) were studied, with CT images and multi-modal MR images (T1, T1C, T2) aligned using deformable registration. Region of Interests (ROIs), i.e., GTV and parotid gland, were used, with a rough trimap marking extremely few foreground voxels, many background voxels, and a large unknown region. The proposed algorithm leverages the connection between each voxel and its nearest neighbors in the feature space, to propagate the opacity information.

RESULTS

We evaluated the results by employing both qualitative and quantitative methods. Using qualitative evaluation, experienced clinicians confirmed that the results were in agreement with the input data, especially for areas where borders were visible in most modalities (e.g., between air and tumor). For more challenging regions, where boundaries were unclear in the images, the results displayed fine-grained opacity transitions indicating the confidence of each voxel belonging to the ROI. When compared to the delineations made by clinicians, we found our results are usually more compact. We define a precision metric that evaluates the ratio of the matted foreground inside clinicians' delineations versus the entire matted foreground. Using a threshold of 0.4, our binarized result scored 0.95 for GTV and 0.92 for parotid gland.

CONCLUSION

The proposed method demonstrated satisfactory results on challenging ROIs. The objective boundaries generated by this method have advantages in many aspects, including improvement of delineation protocols, enhancement of manual annotation consistency, and increase of deep-learning based automated delineation accuracy.

Early Prediction of Radiation Treatment Response via Longitudinal Analysis of CBCT Radiomic Features for Locally Advanced Rectal Cancer

PURPOSE/OBJECTIVE(S)

Patients respond to the same radiation treatment course differently due to inter- and intra- patient variability in radiosensitivity. Despite widespread use of AI/ML in radiation oncology, there is a lack of monitoring strategies used during treatment courses to evaluate early predictors of treatment response in a systematic fashion. This work advances a straightforward, yet effective, method for the early detection of treatment response through systematically analyzing daily CBCT radiomic features. The goal is to aid clinicians in assessing the treatment efficacy routinely with a view towards optimizing personalized treatment.

MATERIALS/METHODS

We included a cohort of 30 patients diagnosed with locally advanced rectal cancer who underwent neo-adjuvant fractionated radiation treatment (RT) with a prescription dose of 50.4 Gy (28 fractions), followed by total mesorectal excision surgery after completion of ChemoRT. Daily IGRT imaging was acquired prior to each fraction resulting in a total of 840 CBCTs. Patients were divided into responder (14 patients) and non-responder (16 patients) groups based on post-RT pathological response. Mutual information algorithms were utilized to rigorously register daily CBCT images to the planning CT, and longitudinal radiomic features of the target were extracted from the daily CBCTs during the entire treatment course. All longitudinal features for a given patient were standardized with Z-Score normalization, followed by linear fitting using the least square method, resulting in radiomic feature trends (RFT) represented by slope values. Statistical significance was established via a two-sample U test and P-value with a threshold of 0.05. Logistic regression was performed to eliminate RFT with accuracy rates lower than 0.5. The final trending model was developed using random forest. For each patient at fraction N, our investigation involved independent 27 group experiments, where each experiment considered image group from fraction #1 to N, to confirm the effectiveness and stability of the model.

RESULTS

The proposed RFT demonstrated a high level of precision and consistency for post-RT response based on longitudinal CBCT images for LARC patients. The trending model yielded an accuracy of 0.9556, 95% CI (0.94, 0.972) when each daily image was considered, the prediction consistency was 0.964. Given the first 14 experiments (considering group images of fraction #1-15), the prediction accuracy was 0.9357, 95% CI (0.915, 0.956) and the prediction consistency was 0.952.

CONCLUSION

A strategy for monitoring and early prediction of LARC patients' radioresponse was evaluated via longitudinal CBCT assessment. Our trending models demonstrate a significant difference between the responder vs non-responder groups as early as the 15th fraction. Our strategy achieved superior accuracy and consistency to predict post-RT response of LARC patients.

The Lyman Normal Tissue Complication Probability Model and Risk Prediction for Temporal Lobe Injury after Re-Irradiation in Patients with Recurrent Nasopharyngeal Carcinoma

PURPOSE/OBJECTIVE(S)

The risk of temporal lobe injury (TLI) in recurrent nasopharyngeal carcinoma (rNPC) patients with intensity-modulated radiation therapy (IMRT) is high. We aimed to construct the normal tissue complication probability (NTCP) model for TLI of rNPC and establish a risk predictive model.

MATERIALS/METHODS

We retrospectively analyzed 103 patients with rNPC who had received two courses of IMRT in our institution. The 206 temporal lobes (TLs) of these patients were randomly divided into a training (n = 144) and validation group (n = 62). We determined the mean value of the following parameters to construct the Lyman NTCP model: TD50(1) (the dose with a 50% probability of complications to an organ when all volumes are irradiated), m [steepness of the dose-response at TD50(1)], and n (the parameter related to volume effect). The most predictive dosimetric parameter and clinical variables were integrated in Cox proportional hazards models. A nomogram was developed for predicting risk of TLs.

RESULTS

The parameters of the fitted NTCP model were TD50(1) = 107.84 Gy (95% confidence interval (CI), [97.15, 118.54]), m = 0.16 (95% CI, [0.14, 0.19]), and n = 0.04 (95% CI, [0.01, 0.06]). The cumulative dose delivered to 0.1 cm3 of temporal lobe volume (D0.1cc-c) was the most predictive dosimetric parameter for TLI. The Kaplan-Meier curves showed a significant difference in 2-year TLI-free survival among different risk groups according to the total score of nomograms.

CONCLUSION

The TD50(1) of TLI in patients with rNPC is 107.84 Gy in Lyman NTCP model. The nomogram model can accurately predict the risk of TLI for individual.

[Effect of jejunal feeding tube placement on complications after laparoscopic radical surgery in patients with incomplete pyloric obstruction by gastric antrum cancer]

Objective: To assess the effect of jejunal feeding tube placement on early complications of laparoscopic radical gastrectomy in patients with incomplete pyloric obstruction by gastric cancer. Methods: This was a retrospective cohort study. Perioperative clinical data of 151 patients with gastric antrum cancer complicated by incomplete pyloric obstruction who had undergone laparoscopic distal radical gastrectomy from May 2020 to May 2022 in the First Affiliated Hospital of Nanchang University were collected. Intraoperative jejunal feeding tubes had been inserted in 69 patients (nutrition tube group) and not in the remaining 82 patients (conventional group). There were no statistically significant differences in baseline characteristics between the two groups (all P>0.05). The operating time, intraoperative bleeding, time to first intake of solid food, time to passing first flatus, time to drainage tube removal, and postoperative hospital stay, and early postoperative complications (occurded within 30 days after surgery) were compared between the two groups. Results: Patients in both groups completed the surgery successfully and there were no deaths in the perioperative period. The operative time was longer in the nutritional tube group than in the conventional group [(209.2±4.7) minutes vs. (188.5±5.7) minutes, t=2.737, P=0.007], whereas the time to first postoperative intake of food [(2.7±0.1) days vs. (4.1±0.4) days, t=3.535, P<0.001], time to passing first flatus [(2.3±0.1) days vs. (2.8±0.1) days, t=3.999, P<0.001], time to drainage tube removal [(6.3±0.2) days vs. (6.9±0.2) days, t=2.123, P=0.035], and postoperative hospital stay [(7.8±0.2) days vs. (9.7±0.5) days, t=3.282, P=0.001] were shorter in the nutritional tube group than in the conventional group. There was no significant difference between the two groups in intraoperative bleeding [(101.1±9.0) mL vs. (111.4±8.7) mL, t=0.826, P=0.410]. The overall incidence of short-term postoperative complications was 16.6% (25/151). Postoperative complications did not differ significantly between the two groups (all P>0.05). Conclusion: It is safe and feasible to insert a jejunal feeding tube in patients with incomplete outlet obstruction by gastric antrum cancer during laparoscopic radical gastrectomy. Such tubes confer some advantages in postoperative recovery.

Development and preliminary application of novel genomewide SSR markers for genetic diversity analysis of an economically important bio-control agent Platygaster robiniae (Hymenoptera: Platygastridae)

Platygaster robiniae Buhl and Duso (Hymenoptera: Platygastridae) is an egg-larvae parasitoid of the black locust gall midge (Obolodiplosis robiniae) (Haldeman) (Diptera: Cecidomyiidae) which is a serious invasive pest in China, where it attacks an important hardwood species, the black locust tree, Robini pseudoacacia L. (Fabales: Fabaceae). Despite the use of P. robiniae as an effective biocontrol agent, the absence of sequence data and other molecular markers have limited its genetic applications for pest management in forests. Simple-sequence repeats (SSRs) are valuable molecular markers for population genetic structure studies. In the present study, we identified 14,123 SSRs, of which 7799 SSR primer pairs were successfully designed. Subsequently, 240 SSR were chosen and tested with 48 P. robiniae accessions from two geographically separated populations in north and south China. Of these, 34 were polymorphic, with an average of three alleles (N_a) and four genotypes (N_G) each. The average values of observed heterozygosity (H_o) was 0.3514, expected heterozygosity (H_e) 0.4167, Shannon's information index (I) 0.7143, and polymorphism information content (PIC) 0.3558, respectively. Neighbour joining analysis (bootstrap 1000) revealed that Chengdu (CD) and Dangdong (DD) popluations clustered into two main divisions, and some individuals from two popluations clustered together as the third devision, which indicated the gene flow and genetic differentiation were present between two populations. Our finding indicates that these SSR markers will be useful for further studies on the genotype identification and genetic mapping of the genus Platygaster.

[Genetic expression differences of 11 beta-hydroxysteroid dehydrogenase in the bone microvascular endothelial cells derived from different regions of the human femoral head]

Objective: To investigate the expression levels and activation differences of 11beta-hydroxysteroid dehydrogenase (11beta-HSD) gene in bone microvascular endothelial cells (BMECs) in different regions of human femoral head. Methods: Tissue specimens of femoral heads were obtained from hip arthroplasty carried out in China-Japan Friendship Hospital from January 2017 to June 2018. And the BMECs we isolated, purified, identified and cultured from different regions of the human femoral head: in the subchondral and cancellous bone regions. The BMECs from the two regions were intervened by hydrocortisone with a series of low concentration gradients (0, 0.03, 0.06, 0.10 mg/ml) respectively. The cell phenotype and functional status of BMECs and cell migration were detected by scratch experiments, and the angiogenesis in different regions of the femoral head was observed. The mRNA and protein expression of 11beta-HSD1, 11beta-HSD2 in BMECs were detected by real-time fluorescence quantitative polymerase chain reaction (RT-PCR) and Western-blot method, respectively. Results: With the increase of the concentration of hydrocortisone, the 11beta-HSD1 mRNA and protein expression of BMECs in the subchondral and cancellous bone regions of the femoral head increased significantly, and the 11beta-HSD1 mRNA and protein expression of BMECs in the subchondral bone region was significantly lower than those in cancellous bone region (all P<0.05). The 11beta-HSD2 mRNA and protein expression of BMECs in the cancellous bone region showed a slow decrease first and then increased slightly at 0.10 mg/ml, while the expression in the subchondral bone region was the opposite. The 11beta-HSD2 mRNA and protein expression of BMECs in subchondral bone region was slightly higher than those in cancellous bone region (all P<0.05), but there was no significant statistical difference between the two regions at 0.10 mg/ml (0.123±0.018 vs 0.126±0.021, 0.577±0.231 vs 0.609±0.174, t=1.380, 0.409, both P>0.05). At different times of the 0.06 mg/ml hydrocortisone intervention, there was no significant differences in scratch closure rate, the number of BMECs lumen, the number of buds and the length of tubule branches in different regions of the femoral head (all P>0.05). Conclusion: The 11beta-HSD expression of BMECs in different regions of human femoral head is significantly different. The 11beta-HSD1 is high-expressed, but 11beta-HSD2 is low-expressed in BMECs of the cancellous bone region, and those are opposite in the subchondral bone region, which helps to explain the pathological characteristics and pathogenesis of hormonal osteonecrosis.

Relationship between Postmortem Interval and FTIR Spectroscopy Changes of the Rat Skin

Abstract

Objective To infer postmortem interval （PMI） based on spectral changes of the dorsal skin of rats within 15 days postmortem using Fourier transform infrared （FTIR） spectroscopy. Methods The rats were sacrificed by cervical dislocation after anesthesia, and then placed at 25 ℃ and relative humidity of 50%. The FTIR spectral data collected from the dorsal skin at PMI points were modeled with machine learning technique. Results There was no significant difference of absorption peak location among all the PMI groups but their peak intensities changed as a function of PMIs. The model for PMI estimation was constructed using partial least squares （PLS） regression, reaching a R² of 0.92 and a root mean square error （RMSE） of 1.30 d. As shown in variable importance for projection （VIP）, four spectral bands including 1 760-1 700 cm^-1, 1 660-1 640 cm^-1, 1 580-1 540 cm^-1 and 1 460-1 420 cm^-1 were determined as important contributions to model prediction. Conclusion Application of the FTIR technique to detect postmortem spectral changes of the rat skin provides a novel proposal for PMI estimation.

[Minimal residual disease in adults with Philadelphia chromosome negative acute lymphoblastic leukemia in high-risk]

目的

探讨高危Ph阴性急性淋巴细胞白血病（Ph⁻ALL）中微小残留病（MRD）对预后和治疗策略的影响。

方法

回顾性分析2008年1月1日至2017年12月31日收治的初治成人高危Ph⁻ALL并获得完全缓解（CR）患者的临床资料，通过Cox回归模型和Landmark分析，寻找预后相关因素。

结果

177例患者纳入研究，其中男性99例（56%），中位年龄40（16~65）岁，95例（54%）在第1次完全缓解（CR₁）后接受异基因造血干细胞移植（移植组）。多因素分析显示，巩固治疗1个疗程后MRD阴性（HR=0.52，95%CI 0.30~0.89，P=0.017）、诱导化疗4周达到CR（HR=0.43，95%CI 0.24~0.79，P=0.006）是影响患者无病生存（DFS）的有利因素，CR₁移植是影响患者DFS（HR=0.13，95%CI 0.08~0.22，P<0.001）和总生存（OS）（HR=0.24，95%CI 0.15~0.41，P<0.001）的共同有利因素。121例患者进入Landmark分析，在巩固治疗1个疗程后MRD阴性的85例患者中进行多因素分析显示，巩固治疗3个疗程后MRD阴性是影响患者DFS（HR=0.18，95%CI 0.05~0.64，P=0.008）和OS（HR=0.14，95%CI 0.04~0.50，P=0.003）的有利因素。在巩固治疗1个疗程和3个疗程后MRD均阴性的患者中，移植组患者3年DFS率有高于化疗组的趋势（75.2%对51.3%，P=0.082），但3年OS率相近（72.7%对68.7%，P=0.992）。巩固治疗1个疗程和3个疗程后MRD至少1次阳性的患者中，移植组的3年DFS率（64.8%对33.3%，P=0.006）和3年OS率（77.0%对33.3%，P=0.028）均显著高于化疗组，与这两个时间点MRD均阴性的移植患者的预后差异无统计学意义（P>0.05）。

结论

在高危成人Ph⁻ALL患者中，巩固治疗1个疗程后MRD阴性是预后良好的独立影响因素。巩固治疗1个疗程和3个疗程MRD均阴性的患者，接受移植或化疗的生存率相似。移植显著改善了巩固治疗1个疗程和3个疗程后MRD至少一次阳性患者的预后。

[Morphological changes of the central sulcus in children with complete growth hormone deficiency: a 3.0 T MRI study]

Objective: To analyze morphological changes in central sulcus of the cerebral cortex in children with complete growth hormone deficiency (CGHD). Methods: Patients attending the Shandong Provincial Hospital who were diagnosed with CGHD or idiopathic short stature were recruited from January 2015 to January 2019. Thirty children with CGHD (18 males and 12 females, 5 to 14 years old) and 30 children with idiopathic short stature (22 males and 8 females, 5 to 14 years old) were included. Measurements of the central sulcus, including the average width, maximum depth, average depth, top length, bottom length and depth position-based profiles (DPP), were obtained using Brain VISA software. The significant differences between groups were statistically analyzed. Results: The average width of bilateral central sulci in children with CGHD (left: (2.26±0.41) mm; right: (2.19±0.34) mm) were significantly higher than those in children with idiopathic short stature (left: (2.10±0.27) mm; right: (2.02±0.18) mm) (P<0.05) ; The maximum depth of the left central sulcus ((19.67±1.29) mm) and the average depth of the right central sulcus ((14.18±1.41) mm) were significantly lower than those in children with idiopathic short stature (left maximum depth: (20.69±1.43) mm; right average depth: (14.92±1.21) mm) (P<0.05) . Children with CGHD had significantly lower DPP at the middle part of the left central sulcus (sites: 46-54) and the inferior part of the right central sulcus(sites: 91-98). Conclusion: There are significant morphological changes of the central sulcus in children with CGHD, which may represent the structural basis of their relatively slower development in motor, cognitive and linguistic functional performance.

Decrease of miR-622 expression promoted the proliferation, migration and invasion of cholangiocarcinoma cells by targeting regulation of c-Myc

OBJECTIVE

To explore the mechanism of miR-622 in regulating the proliferation, migration and invasion of cholangiocarcinoma (CCA) cells.

MATERIALS AND METHODS

Quantitative real-time PCR was conducted to measure the expression of miR-622 and c-Myc in CCA tissues and cell lines. Protein level of c-Myc was measured by Western blot. The effect of miR-622 on cell proliferation, migration and invasion was analyzed by MTT assay and Transwell chamber migration assay. Luciferase reporter assay was performed to measure the effect of miR-622 on c-Myc.

RESULTS

miR-622 expression was downregulated in both CCA tissues and cell lines, while c-Myc expression was uregulated. Overexpression of miR-622 in CCA cells was statistically correlated with a decrease of cell proliferation, migration and invasion, while inhibition of miR-622 made an inverse result. We also proved c-Myc was identified as a target gene of miR-622 in CCA. Moreover, we found overexpression of c-Myc can strengthen the effects of miR-622 on the proliferation, migration and invasion of CCA cells.

CONCLUSION

Decrease of miR-622 promotes the proliferation, migration and invasion of CCA cells by directly targeting c-Myc.

Up-regulation of RNF187 induces hepatocellular carcinoma cell epithelial to mesenchymal transitions

Ring finger protein 187 (RNF187) has been identified to be a co-activator linking c Jun to Ras signaling. However, the expression and function of RNF187 in hepatocellular carcinomas (HCC) remains unclear. Here, we tried to determine the expression and roles of RNF187 in hepatocellular carcinomas (HCC).The expression of RNF187 was determined in HCC tissues and cell lines, and we found that RNF187 expressed highly in HCC tissues compared with the corresponding adjacent liver tissues both in mRNA and protein level, which was consistent with the result of immunohistochemistry on HCC tissue microarrays. In HCC cell lines, the level of RNF187 was positively associated with the HCC cells metastatic potential. By the RNF187 interference and cDNA transfection, we showed that the high level of RNF187 induced the HCC cells invasion and metastasis both in vitro and in vivo, as well as the high ability of colony formation.Mechanistically, we detected the high level of RNF187 promoted cell scatter by inducing epithelial-mesenchymal transition (EMT). Clinically, the high level of RNA187 was significantly correlated with a malignant phenotype, including larger tumor size, multiple tumors, and microvascular invasion. Importantly, high level of RNF187 correlated with HCC patients' shorter OS and lower disease free survival rates than those with low level of RNF187. Our results revealed that elevated expression of RNF187 induced hepatocellular carcinoma cell epithelial to mesenchymal transitions, and represented a novel marker for predicting the poor prognosis of HCC.

Association of DENND1A Gene Polymorphisms with Polycystic Ovary Syndrome: A Meta-Analysis

OBJECTIVE

The rs2479106 and rs10818854 polymorphisms in the DENND1A gene have been reported to be extensively associated with risk of polycystic ovary syndrome (PCOS). However, the results from these studies remained inconclusive and conflicting. To detect a true association of rs2479106 and rs10818854 polymorphisms with PCOS risk, a single study may be underpowered, particularly for those studies with inadequate sample size. Therefore, we performed a meta-analysis of all available studies to explore this association.

METHODS

All studies published up to March 2015 on the association were identified by searching electronic databases PubMed, EMBASE, Web of Science, and China National Knowledge Infrastructure. Studies containing available genotype frequencies of those 2 polymorphisms were chosen, and the odds ratios and associated 95% confidence intervals were calculated using fixed- or random- effects models.

RESULTS

A total of 8 studies about s2479106 polymorphism (8185 cases and 28675 controls) and 5 studies about rs10818854 polymorphism (6638 cases and 27443 controls) met the inclusion criteria for the meta-analysis. Overall, significant increase of PCOS risk was found between DENND1A-rs10818854 and PCOS susceptibility. In addition, we also found an increased risk of PCOS in rs2479106 allele model, heterozygote variant genetic model, and dominant genetic model.

CONCLUSION

This meta-analysis suggested that rs2479106 and rs10818854 polymorphisms in the DENND1A gene were associated with increased risk of PCOS. To validate the association between these polymorphisms and PCOS susceptibility, further large and well-designed studies are needed.

Retroperitoneal laparoscopy management for ureteral fibroepithelial polyps causing hydronephrosis in children: a report of five cases

INTRODUCTION

Hydronephrosis is a common disease in children and may be caused by ureteral fibroepithelial polyps (UFP). Ureteral fibroepithelial polyps are rare in children and are difficult to precisely diagnose before surgery. Surgical treatment for symptomatic UFP is recommended. At the present institution, retroperitoneal laparoscopy has been used to treat five boys with UFP since 2006.

OBJECTIVE

To highlight the significance of UFP as an etiological factor of hydronephrosis in children and evaluate the applicative value of retroperitoneal laparoscopy in the treatment of children with UFP.

METHODS

Between 2006 and 2013 five boys underwent retroperitoneal laparoscopy at the present institution. They were identified with UFP by review of the clinical database. Detailed data were collected, including: radiographic studies, gross anatomical pathology, and pathology and radiology reports. All boys had been followed up at least every 6 months.

RESULTS

All of the boys were aged between 7 and 16 years (mean 9.8 years). The main symptoms were flank pain (all five) and hematuria (three). Radiographic examination showed that all of the boys presented with incomplete ureteral obstruction and hydronephrosis. The ureteral fibroepithelial polyps were located near the left UPJ or the left proximal ureter. All of the boys had the UFP removed: three underwent retroperitoneal laparoscopic dismembered Anderson-Hynes pyeloplasty and polypectomy, and two had retroperitoneal laparoscopic ureteral anastomosis. These polyps were all on the left side and between 15 and 35 mm in length (mean 22 mm) (Figure). All of the boys recovered well and were discharged from hospital. The postoperative histological report confirmed that the specimens were UFP. Hydronephrosis was periodically assessed by ultrasonography (using the same method as pre-surgical ultrasonography) after surgery. Mean follow-up was 33 months (range 6-58 months) and no complications were found afterwards.

CONCLUSIONS

Ureteral fibroepithelial polyps are rare but rather important as they can cause UPJ obstruction, which often manifests as hydronephrosis. It is most important to confirm the site of ureteral obstruction before surgery as this may have an effect on the surgical management. It is recommended that UFP be successfully managed in children with retroperitoneal laparoscopy.

Effect of umbilical cord mesenchymal stem cell in peri-implant bone defect after immediate implant: an experiment study in beagle dogs

BACKGROUND

For the sake of reducing post extraction resorption, getting optimal positioning of the implant and shortening treatment time, immediate implant placement following tooth extraction has been proposed as a treatment option. However, the large bone defect peri-implant has a negative influence on the process of bone healing. In this study, umbilical cord mesenchymal stem cells (UCMSCs) were transplanted into the bone defect peri-implant in beagle dogs and the effect of UCMSCs on bone regeneration in peri-implant were assessed.

METHODS

The mandibular second, third and fourth premolars of 8 beagle dogs were extracted bilaterally. The defects in one side were filled with platelet-rich fibrin (PRF) and then UCMSCs were injected into the defect area, while the defects in the other side were filled with PRF only as control group. The titanium implant was placed into the distal root socket of each extracted tooth. The animals were sacrificed at week 2, 4 and 8 post operation. The bone defects adjacent to the implant which are 4 mm in height, 4 mm in the mesio-distal direction and 3.5 mm in the bucco-lingual direction were made after immediate implant. Histomorphometric analysis was performed using methylene blue-fuchsin acid staining and hematoxylin and eosin (HE) staining to evaluate bone regeneration.

RESULTS

The direct bone-to-implant contact (BIC) in the experiment after 4 and 8 weeks was 56.47 ± 1.18% and 76.23 ± 2.08%; and in the control group was40.79 ± 0.65% and 61.17 ± 2.79%, respectively. The percentage of newly formed bone after 2, 4 and 8 weeks was 17.60 ± 1.5%, 49.82 ± 4.02% and 67.16 ± 2.1% in experiment group; and in control group 14.30 ± 1.25%, 37.04 ± 2.29% and 58.83 ± 3.36%, respectively. These results represented significant differences statistically.

CONCLUSION

Intra-bone marrow injection of UCMSCs can promote new bone formation. UCMSCs can be used to as excellent seed cells to repair the large defect peri-implant after immediate implant.

Effect of umbilical cord mesenchymal stem cell in peri-implant bone defect after immediate implant: an experiment study in beagle dogs

BACKGROUND

For the sake of reducing post extraction resorption, getting optimal positioning of the implant and shortening treatment time, immediate implant placement following tooth extraction has been proposed as a treatment option. However, the large bone defect peri-implant has a negative influence on the process of bone healing. In this study, umbilical cord mesenchymal stem cells (UCMSCs) were transplanted into the bone defect peri-implant inbeagle dogs and the effect of UCMSCs on bone regeneration in peri-implant were assessed.

METHODS

The mandibular second, third and fourth premolars of 8 beagle dogs were extracted bilaterally. The defects in one side were filled with platelet-rich fibrin (PRF) and then UCMSCs were injected into the defect area, while the defects in the other side were filled with PRF only as control group. The titanium implant was placed into the distal root socket of each extracted tooth. The animals were sacrificed at week 2, 4 and 8 post operative. The bone defects adjacent to the implant which are 4 mm in height, 4 mm in the mesio-distal direction and 3.5 mm in the bucco-lingual direction were made after immediate implant. Histomorphometric analysis was performed using methylene blue-fuchsin acid staining and hematoxylin and eosin (HE) staining to evaluate bone regeneration.

RESULTS

The direct bone-to-implant contact (BIC) in the experiment after 4 and 8 weeks was 56.47±1.18% and 76.23±2.08%; and in the control group was40.79±0.65% and 61.17±2.79%, respectively. The percentage of newly formed bone after 2, 4 and 8 weeks was 17.60±1.5%, 49.82±4.02% and 67.16±2.1% in experiment group; and in control group 14.30±1.25%, 37.04±2.29% and 58.83±3.36%, respectively. These results represented significant differences statistically.

CONCLUSION

Intra-bone marrow injection of UCMSCs can promote new bone formation. UCMSCs can be used to as excellent seed cells to repair the large defect peri-implant after immediate implant.

Genomic aberrations in the HTPAP promoter affect tumor metastasis and clinical prognosis of hepatocellular carcinoma

We previously reported that the intronic tagSNP +357G/C in the metastasis suppressor HTPAP is associated with metastasis and prognosis of hepatocellular carcinoma (HCC). The aim of this study was to investigate whether SNPs in the HTPAP promoter modulate HTPAP expression and prognosis of HCC. Genomic DNA from 572 microdissected HCCs were genotyped by pyrosequencing and verified by direct sequencing. Haplotype blocks were analyzed. Reporter plasmids were constructed and transfected into HCC cell lines. Transcriptional activities of plasmids were analyzed by dual-luciferase reporter systems. HTPAP expression was measured by real-time quantitative PCR, western blots, and tissue microarrays. Invasion was assessed by Matrigel assays. The prognostic values of HTPAP promoter SNPs in HCC were evaluated by Kaplan-Meier and Cox regression analyses. We identified six SNPs, including -1053A/G and +64G/C, in the HTPAP promoter. The SNPs were in complete linkage disequilibrium, resulting in three promoter haplotypes (promoter I:-1053AA/+64GG, promoter II: -1053AG/+64GC, and promoter III: -1053GG/+64CC). Promoter I manifested the highest luciferase index (p<0.005). However, no significant difference was observed between promoters II and III. We consistently found that HTPAP mRNA and protein levels were significantly higher in promoter I than that of promoter II+III (p<0.001). Invasion was increased in HCC cells transfected with promoters II+III compared to those transfected with promoter I (p<0.05). The HTPAP promoter II+III haplotype was associated with significantly increased metastasis compared to that of promoter I (p = 0.023). The postoperative five-year overall survival of patients with promoters II+III was lower than that of patients with promoter I (p = 0.006). Multivariate analysis showed that the promoter II+III haplotype was an adverse prognostic marker in HCC. The genetic variants at loci –1053 and +64 of the HTPAP promoter affect the expression of HTPAP, which might be a novel determinant and target for HCC prognosis.

Estrogen up-regulates MMP2/9 expression in endometrial epithelial cell via VEGF-ERK1/2 pathway

OBJECTIVE

To study the effect of estrogen on anovulatory dysfunctional uterine bleeding (ADUB).

METHODS

Primary endometrial epithelial cells of Hainan Lizu female was cultured and hydrolytic activity of gelatinase was determined by gelatin zymography analysis. Cellular mRNA and protein synthesis was blocked respectively to determine whether the increased expression of MMP-2/9 was induced by estrogen. The expression of VEGF was blocked by siRNA. After treatment with various factors, MMP-9, VEGF, total Erk and phosphorylated Erk expression in primary uterine epithelial cells was detected by Western blotting analysis. Cell MMP-2/9mRNA levels was measured by real-time RT-PCR.

RESULTS

The activity and expression of MMP2/9 was increased in the endometrium of patients with ADUB. Estrogen could up-regulate the expression of VEGF and activate Erk 1/2-Elk1 signal path. After interference by siRNA, ERK1/2 pathway was blocked in cells, and the expression of MMP-2/9 was down-regulated. ERK1/2 specific blocker U0126 blocked ERK phosphorylation, and it could down-regulate the expression of MMP-2/9.

CONCLUSIONS

The results showed that the estrogen can increase the expression of VEGF, and thus activate ERK1/2 pathway to induce MMP-2/9 expression.

Mucins 1-shRNA inhibit the proliferation and HIF-1alpha protein expression on human cholangiocarcinoma cells

To explore the effects of Mucins (MUC)1-shRNA on the proliferation and hypoxia inducible factor (HIF)-1alpha expression of human cholangiocarcinoma (CCA) QBC939 cells in vitro. MUC1-shRNA was constructed and transfected with Lipofectamine™ 2000 into cultured CCA cells. MUC1 mRNA and protein expression levels were determined by RT-PCR and Western blot, respectively. The cellular proliferation and HIF-1alpha expression of QBC939 cells were evaluated by the MTT assay and Western blot, respectively. After transfection, the expression levels of MUC1 mRNA and protein in the experimental group decreased significantly in QBC939 (P < 0.01). The proliferation of MUC1 shRNA-transfected group was 0.30 ± 0.05, 38.32 ± 1.43%, 15.18 ± 1.32%, and there were remarkable differences when compared with the control groups (P < 0.05). Significant inhibition of HIF-1alpha protein expression in MUC1 shRNA-transfected group was also discovered (P < 0.05). MUC1-shRNA could inhibit proliferation and significantly weaken HIF-1alpha protein expression of QBC939 cells, suggesting its potential as a therapeutic target of CCA.

Comparison on serum biomarkers for anovulatory and ovulatory dysfunctional uterine bleeding in Lizu females

OBJECTIVE

To screen, identify, and compare the serum biomarkers between anovulatory dysfunctional uterine bleeding (ADUB) and ovulatory dysfunctional uterine bleeding (ODUB) in Lizu females.

METHODS

The subjects included 128 ADUB patients, 63 ODUB patients, and 93 controls. The serum and supernate of the subjects' mense were collected and stored at -80 °C until use. Differential proteins in the sera of three groups were screened using surface-enhanced laser desorption ionization time-of-flight mass spectrometry. The screened proteins were then identified by tricine-SDS-PAGE gel and spectrometry. Protein expression levels in the menses of ADUB, ODUB, and control subjects were determined using ELISA, RT-PCR, and Western blotting. SPSS 14.1 was used for statistical analysis and chart drawing (α = 0.05).

RESULTS

Three differential protein peaks with peak values of 11.80, 13.59, and 14.68 km/z were screened and identified as serum amyploid protein A (SAA), vascular endothelial growth factor, and vitamin K epoxide reductase, respectively. The SAA was highly expressed in the menses of ADUB and ODUB patients but poorly expressed in the controls. The vascular endothelial growth factor was highly expressed in the menses of ODUB and controls but poorly expressed in ADUB patients. Meanwhile, the vitamin K epoxide reductase was highly expressed in the menses of ADUB and control subjects but poorly expressed in ODUB patients.

CONCLUSIONS

The SAA is the common serum biomarker of ADUB and ODUB. ADUB may be related to angiogenesis impairment, whereas ODUB may be associated with blood coagulation disruption.

Clinical significance of expression of fascin and MMP-9 in hilar cholangiocarcinoma

AIM: To detect the expression of fascin and matrix metalloproteinase-9 (MMP-9) in hilar cholangiocarcinoma and to analyze their clinical significance.

METHODS: The expression of fascin and MMP-9 was examined by immunohistochemistry in 56 cases of hilar cholangiocarcinoma and 14 cases of normal tissue of the bile duct.

RESULTS: The positive rates of fascin and MMP-9 expression in hilar cholangiocarcinoma were obviously higher than those in normal tissue of the bile duct (58.9% vs 0%, 73.2% vs 14.3%, both P < 0.05). Both fascin and MMP-9 expression were associated with histological grade, lymph node metastasis, and portal vein invasion (all P < 0.05). In hair cholangiocarcinoma, the expression of fascin was positively correlative with that of MMP-9 (P < 0.05).

CONCLUSION: The expression of fascin and MMP-9 in hilar cholangiocarcinoma plays important roles in tumor progression and metastasis.

Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

Sequence-derived structural and physicochemical features have been extensively used for analyzing and predicting structural, functional, expression and interaction profiles of proteins and peptides. PROFEAT has been developed as a web server for computing commonly used features of proteins and peptides from amino acid sequence. To facilitate more extensive studies of protein and peptides, numerous improvements and updates have been made to PROFEAT. We added new functions for computing descriptors of protein–protein and protein–small molecule interactions, segment descriptors for local properties of protein sequences, topological descriptors for peptide sequences and small molecule structures. We also added new feature groups for proteins and peptides (pseudo-amino acid composition, amphiphilic pseudo-amino acid composition, total amino acid properties and atomic-level topological descriptors) as well as for small molecules (atomic-level topological descriptors). Overall, PROFEAT computes 11 feature groups of descriptors for proteins and peptides, and a feature group of more than 400 descriptors for small molecules plus the derived features for protein–protein and protein–small molecule interactions. Our computational algorithms have been extensively tested and used in a number of published works for predicting proteins of specific structural or functional classes, protein–protein interactions, peptides of specific functions and quantitative structure activity relationships of small molecules. PROFEAT is accessible free of charge at http://bidd.cz3.nus.edu.sg/cgi-bin/prof/protein/profnew.cgi.

Prediction of chemical carcinogenicity by machine learning approaches

In this paper we report a successful application of machine learning approaches to the prediction of chemical carcinogenicity. Two different approaches, namely a support vector machine (SVM) and artificial neural network (ANN), were evaluated for predicting chemical carcinogenicity from molecular structure descriptors. A diverse set of 844 compounds, including 600 carcinogenic (CG+) and 244 noncarcinogenic (CG-) molecules, was used to estimate the accuracies of these approaches. The database was divided into two sets: the model construction set and the independent test set. Relevant molecular descriptors were selected by a hybrid feature selection method combining Fischer's score and Monte Carlo simulated annealing from a wide set of molecular descriptors, including physiochemical properties, constitutional, topological, and geometrical descriptors. The first model validation method was based a five-fold cross-validation method, in which the model construction set is split into five subsets. The five-fold cross-validation was used to select descriptors and optimise the model parameters by maximising the averaged overall accuracy. The final SVM model gave an averaged prediction accuracy of 90.7% for CG+ compounds, 81.6% for CG- compounds and 88.1% for the overall accuracy, while the corresponding ANN model provided an averaged prediction accuracy of 86.1% for CG+ compounds, 79.3% for CG- compounds and 84.2% for the overall accuracy. These results indicate that the hybrid feature selection method is very efficient and the selected descriptors are truly relevant to the carcinogenicity of compounds. Another model validation method, i.e. a hold-out method, was used to build the classification model using the selected descriptors and the optimised model parameters, in which the whole model construction set was used to build the classification model and the independent test set was used to test the predictive ability of the model. The SVM model gave a prediction accuracy of 87.6% for CG+ compounds, 79.1% for CG- compounds and 85.0% for the overall accuracy. The ANN model gave a prediction accuracy of 85.6% for CG+ compounds, 79.1% for CG- compounds and 83.6% for the overall accuracy. The results indicate that the built models are potentially useful for facilitating the prediction of chemical carcinogenicity of untested compounds.

Steroid-induced osteonecrosis: the number of lesions is related to the dosage

Severe acute respiratory syndrome (SARS) is a newly described infectious disease caused by the SARS coronavirus which attacks the immune system and pulmonary epithelium. It is treated with regular high doses of corticosteroids. Our aim was to determine the relationship between the dosage of steroids and the number and distribution of osteonecrotic lesions in patients treated with steroids during the SARS epidemic in Beijing, China in 2003. We identified 114 patients for inclusion in the study. Of these, 43 with osteonecrosis received a significantly higher cumulative and peak methylprednisolone-equivalent dose than 71 patients with no osteonecrosis identified by MRI. We confirmed that the number of osteonecrotic lesions was directly related to the dosage of steroids and that a very high dose, a peak dose of more than 200 mg or a cumulative methylprednisolone-equivalent dose of more than 4000 mg, is a significant risk factor for multifocal osteonecrosis with both epiphyseal and diaphyseal lesions. Patients with diaphyseal osteonecrosis received a significantly higher cumulative methylprednisolone-equivalent dose than those with epiphyseal osteonecrosis. Multifocal osteonecrosis should be suspected if a patient is diagnosed with osteonecrosis in the shaft of a long bone.

Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds

Virtual screening performance of support vector machines (SVM) depends on the diversity of training active and inactive compounds. While diverse inactive compounds can be routinely generated, the number and diversity of known actives are typically low. We evaluated the performance of SVM trained by sparsely distributed actives in six MDDR biological target classes composed of a high number of known actives (983-1645) of high, intermediate, and low structural diversity (muscarinic M1 receptor agonists, NMDA receptor antagonists, thrombin inhibitors, HIV protease inhibitors, cephalosporins, and renin inhibitors). SVM trained by regularly sparse data sets of 100 actives show improved yields at substantially reduced false-hit rates compared to those of published studies and those of Tanimoto-based similarity searching method based on the same data sets and molecular descriptors. SVM trained by very sparse data sets of 40 actives (2.4%-4.1% of the known actives) predicted 17.5-39.5%, 23.0-48.1%, and 70.2-92.4% of the remaining 943-1605 actives in the high, intermediate, and low diversity classes, respectively, 13.8-68.7% of which are outside the training compound families. SVM predicted 99.97% and 97.1% of the 9.997 M PUBCHEM and 167K remaining MDDR compounds as inactive and 2.6%-8.3% of the 19,495-38,483 MDDR compounds similar to the known actives as active. These suggest that SVM has substantial capability in identifying novel active compounds from sparse active data sets at low false-hit rates.

A support vector machines approach for virtual screening of active compounds of single and multiple mechanisms from large libraries at an improved hit-rate and enrichment factor

Support vector machines (SVM) and other machine-learning (ML) methods have been explored as ligand-based virtual screening (VS) tools for facilitating lead discovery. While exhibiting good hit selection performance, in screening large compound libraries, these methods tend to produce lower hit-rate than those of the best performing VS tools, partly because their training-sets contain limited spectrum of inactive compounds. We tested whether the performance of SVM can be improved by using training-sets of diverse inactive compounds. In retrospective database screening of active compounds of single mechanism (HIV protease inhibitors, DHFR inhibitors, dopamine antagonists) and multiple mechanisms (CNS active agents) from large libraries of 2.986 million compounds, the yields, hit-rates, and enrichment factors of our SVM models are 52.4-78.0%, 4.7-73.8%, and 214-10,543, respectively, compared to those of 62-95%, 0.65-35%, and 20-1200 by structure-based VS and 55-81%, 0.2-0.7%, and 110-795 by other ligand-based VS tools in screening libraries of >or=1 million compounds. The hit-rates are comparable and the enrichment factors are substantially better than the best results of other VS tools. 24.3-87.6% of the predicted hits are outside the known hit families. SVM appears to be potentially useful for facilitating lead discovery in VS of large compound libraries.

Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins

Computational methods for predicting compounds of specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) property are useful for facilitating drug discovery and evaluation. Recently, machine learning methods such as neural networks and support vector machines have been explored for predicting inhibitors, antagonists, blockers, agonists, activators and substrates of proteins related to specific therapeutic and ADMET property. These methods are particularly useful for compounds of diverse structures to complement QSAR methods, and for cases of unavailable receptor 3D structure to complement structure-based methods. A number of studies have demonstrated the potential of these methods for predicting such compounds as substrates of P-glycoprotein and cytochrome P450 CYP isoenzymes, inhibitors of protein kinases and CYP isoenzymes, and agonists of serotonin receptor and estrogen receptor. This article is intended to review the strategies, current progresses and underlying difficulties in using machine learning methods for predicting these protein binders and as potential virtual screening tools. Algorithms for proper representation of the structural and physicochemical properties of compounds are also evaluated.

Application of support vector machines to in silico prediction of cytochrome p450 enzyme substrates and inhibitors

Cytochrome P450 enzymes are responsible for phase I metabolism of the majority of drugs and xenobiotics. Identification of the substrates and inhibitors of these enzymes is important for the analysis of drug metabolism, prediction of drug-drug interactions and drug toxicity, and the design of drugs that modulate cytochrome P450 mediated metabolism. The substrates and inhibitors of these enzymes are structurally diverse. It is thus desirable to explore methods capable of predicting compounds of diverse structures without over-fitting. Support vector machine is an attractive method with these qualities, which has been employed for predicting the substrates and inhibitors of several cytochrome P450 isoenzymes as well as compounds of various other pharmacodynamic, pharmacokinetic, and toxicological properties. This article introduces the methodology, evaluates the performance, and discusses the underlying difficulties and future prospects of the application of support vector machines to in silico prediction of cytochrome P450 substrates and inhibitors.

MODEL—molecular descriptor lab: A web-based server for computing structural and physicochemical features of compounds

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods

Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.

Relationships between the folding rate constant and the topological parameters of small two-state proteins based on general random walk model

In this paper, we propose an analytically tractable model of protein folding based on one-dimensional general random walk. A second-order differential equation for the mean folding time of a single protein is constructed which can be used to derive the observed relationship between the folding rate constant and the number of native contacts. The parameters appearing in the model can be determined by fitting the theoretical prediction to the experimental result. In addition, taking into account the fact that the number of native contacts is almost proportional to the relative contact order, we can also explain the observed relationship between the folding rate constant and the relative contact order.

Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods

Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.

PEARLS: Program for Energetic Analysis of Receptor−Ligand System

Analysis of the energetics of small molecule ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions facilitates the quantitative understanding of molecular interactions that regulate the function and conformation of proteins. It has also been extensively used for ranking potential new ligands in virtual drug screening. We developed a Web-based software, PEARLS (Program for Energetic Analysis of Ligand-Receptor Systems), for computing interaction energies of ligand-protein, ligand-nucleic acid, protein-nucleic acid, and ligand-protein-nucleic acid complexes from their 3D structures. AMBER molecular force field, Morse potential, and empirical energy functions are used to compute the van der Waals, electrostatic, hydrogen bond, metal-ligand bonding, and water-mediated hydrogen bond energies between the binding molecules. The change in the solvation free energy of molecular binding is estimated by using an empirical solvation free energy model. Contribution from ligand conformational entropy change is also estimated by a simple model. The computed free energy for a number of PDB ligand-receptor complexes were studied and compared to experimental binding affinity. A substantial degree of correlation between the computed free energy and experimental binding affinity was found, which suggests that PEARLS may be useful in facilitating energetic analysis of ligand-protein, ligand-nucleic acid, and protein-nucleic acid interactions. PEARLS can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/rune.pl.

Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods

Quantitative structure-pharmacokinetic relationships (QSPkR) have increasingly been used for the prediction of the pharmacokinetic properties of drug leads. Several QSPkR models have been developed to predict the total clearance (CL(tot)) of a compound. These models give good prediction accuracy but they are primarily based on a limited number of related compounds which are significantly lesser in number and diversity than the 503 compounds with known CL(tot) described in the literature. It is desirable to examine whether these and other statistical learning methods can be used for predicting the CL(tot) of a more diverse set of compounds. In this work, three statistical learning methods, general regression neural network (GRNN), support vector regression (SVR) and k-nearest neighbour (KNN) were explored for modeling the CL(tot) of all of the 503 known compounds. Six different sets of molecular descriptors, DS-MIXED, DS-3DMoRSE, DS-ATS, DS-GETAWAY, DS-RDF and DS-WHIM, were evaluated for their usefulness in the prediction of CL(tot). GRNN-, SVR- and KNN-developed models have average-fold errors in the range of 1.63 to 1.96, 1.66-1.95 and 1.90-2.23, respectively. For the best GRNN-, SVR- and KNN-developed models, the percentage of compounds with predicted CL(tot) within two-fold error of actual values are in the range of 61.9-74.3% and are comparable or slightly better than those of earlier studies. QSPkR models developed by using DS-MIXED, which is a collection of constitutional, geometrical, topological and electrotopological descriptors, generally give better prediction accuracies than those developed by using other descriptor sets. These results suggest that GRNN, SVR, and their consensus model are potentially useful for predicting QSPkR properties of drug leads.

Effect of molecular descriptor feature selection in support vector machine classification of pharmacokinetic and toxicological properties of chemical agents

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

Assessment of approximate string matching in a biomedical text retrieval problem

Text-based search is widely used for biomedical data mining and knowledge discovery. Character errors in literatures affect the accuracy of data mining. Methods for solving this problem are being explored. This work tests the usefulness of the Smith-Waterman algorithm with affine gap penalty as a method for biomedical literature retrieval. Names of medicinal herbs collected from herbal medicine literatures are matched with those from medicinal chemistry literatures by using this algorithm at different string identity levels (80-100%). The optimum performance is at string identity of 88%, at which the recall and precision are 96.9% and 97.3%, respectively. Our study suggests that the Smith-Waterman algorithm is useful for improving the success rate of biomedical text retrieval.

Prediction of Genotoxicity of Chemical Compounds by Statistical Learning Methods

Various toxicological profiles, such as genotoxic potential, need to be studied in drug discovery processes and submitted to the drug regulatory authorities for drug safety evaluation. As part of the effort for developing low cost and efficient adverse drug reaction testing tools, several statistical learning methods have been used for developing genotoxicity prediction systems with an accuracy of up to 73.8% for genotoxic (GT+) and 92.8% for nongenotoxic (GT-) agents. These systems have been developed and tested by using less than 400 known GT+ and GT- agents, which is significantly less in number and diversity than the 860 GT+ and GT- agents known at present. There is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules and to evaluate other statistical learning methods not yet applied to genotoxicity prediction. This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (k-NN), and C4.5 decision tree (DT). A feature selection method, recursive feature elimination, is used for selecting molecular descriptors relevant to genotoxicity study. The overall accuracies of SVM, k-NN, and PNN are comparable to and those of DT lower than the results from earlier studies, with SVM giving the highest accuracies of 77.8% for GT+ and 92.7% for GT- agents. Our study suggests that statistical learning methods, particularly SVM, k-NN, and PNN, are useful for facilitating the prediction of genotoxic potential of a diverse set of molecules.

Protein designability analysis in sequence principal component space using 2D lattice model

The number of proteins that fold into a certain structure differs drastically. The designability of a protein structure, which is defined as the number of sequences that have that structure as their unique lowest energy state, is studied in this paper using a simplified lattice model. The two-letter (HP) code and the pair-contact energy model are employed in the formulation of the relationship between the protein sequences and the compact structures. Due to the correlations between different dimensions, principal component analysis (PCA) is carried out to remove these correlations and develop reliable approximations of probability density functions of the protein sequences and the compact structures. An estimation of designability is derived using these probability density functions. Good correlation between estimated designabilities and those obtained through enumerative calculations is successfully achieved.

Upgrading clinical decision support with published evidence: what can make the biggest difference?

BACKGROUND

To enhance clinical decision support, presented messages are increasingly supplemented with information from the medical literature. The goal of this study was to identify types of evidence that can lead to the biggest difference.

METHODS

Seven versions of a questionnaire were mailed to randomly selected active family practice physicians and internists across the United States. They were asked about the perceived values of evidence from randomized controlled trials, locally developed recommendations, no evidence, cost-effectiveness studies, expert opinion, epidemiologic studies, and clinical studies. Analysis of variance and pairwise comparisons were used for statistical testing.

RESULTS

Seventy-six (52%) physicians responded. On a Likert scale from one to six, randomized controlled clinical trial was the highest rated evidence (mean 5.07, SD +/- 1.14). Such evidence was significantly superior to locally developed recommendations and no evidence at all (P < .05). The interaction was also strong between the types of evidence and clinical areas (P = .0001).

CONCLUSION

While most health care organizations present data without interpretation or simply try to enforce locally developed recommendations, such approaches appear to be inferior to techniques of reporting data with pertinent controlled evidence from the literature. Investigating physicians' perceptions is likely to benefit the design of computer generated messages.

Preparing for the third millennium: the views of life informatics

The chief aspects of this paper are the condition of the birth of life informatics and its tasks, basic concepts, principles, and structure. There are three phases of combining informatics with medicine: product, technological, and theoretic application of which the goals are respectively the informatization of numerical and word processing, data of medical treatment, and the knowledge of medicine. While reached the third phase we have dealt with two types of biological information, physical and nonphysical, i.e., body information (i.e., the information about body's components and structure), and life information (i.e., the information about life codes and life programs). Life informatics is a main branch of bioinformatics. It is a new member of the medical informatics family, and as such is younger than health informatics, nursing informatics, and dental informatics. It's task is to assist biologists and medical doctors to recognize and interfere the human life information procedure just as they are doing well with human body's matter and energy system. Its basic concepts are life information, life information medicine, and life information therapy. Its most important principles are information materialism, general informatics, and information determinism. Its main branches are biomolecule, cellular, organic, individual, and social informatics. In the third millennium, the life informatics will be a leading discipline in biology, medicine and informatics, which will gradually influence modern philosophy and other humanities.

Tumor suppressor proteins as regulators of cell differentiation

The products of the tumor suppressor genes are considered to function as specific inhibitors of tumor cell growth. In this communication, we present evidence to show that these proteins inhibit tumor cell proliferation by participating in the activation of tumor cell differentiation. The ML-1 human myeloblastic leukemia cells used in this study proliferate when treated with insulin-like growth factor I and transferrin but differentiate to monocytes when exposed to tumor necrosis factor alpha or transforming growth factor beta1, or to macrophage-like cells when treated with both these cytokines. Initiation of proliferation but not of differentiation was followed by a 20- to 25-fold increase in the nuclear level of the DNA polymerase-associated processivity factor PCNA and of the proliferation-specific transcription factor E2F1. In contrast, induction of differentiation but not of proliferation was followed by a 25- to 30-fold increase in the nuclear level of the tumor suppressor proteins p53 (wild type), pRb, and p130/Rb2 and of the p53-dependent cyclin kinase inhibitor p21/Cip1. p53 and p21/Cip1, respectively, inhibit the expression and activation of PCNA, whereas p130 and pRb, respectively, inhibit the expression and activation of E2F1. As a result, G1-S-associated DNA and mRNA synthesis is inhibited, growth uncoupled from differentiation, and maturation enabled to proceed. Where this function of the tumor suppressor proteins is impaired, the capacity for differentiation is lost, which leads to the sustained proliferation that is characteristic of the cancer cell.

Melatonin reduces the increase in 8-hydroxy-deoxyguanosine levels in the brain and liver of kainic acid-treated rats

In the present study, the effect of melatonin on oxidative DNA damage induced by kainic acid (KA) treatment was investigated. 8-hydroxy-deoxyguanosine (8-OH-dG) is a main product of oxidatively damaged DNA and was used as the endpoint in these studies. The levels of 8-OH-dG were found to be elevated in the hippocampus and frontal cortex of rats treated with KA. These elevated levels were significantly reduced in animals that were co-treated with melatonin. Thus, there was no difference in 8-OH-dG levels in the brain of control rats compared to those treated with KA (10 mg/kg) plus melatonin (10 mg/kg). The levels of 8-OH-dG also increased in the liver of rats treated with KA. This rise in oxidatively damaged DNA was also prevented by melatonin administration. Melatonin's ability to reduce KA-induced increases in neural and hepatic 8-OH-dG levels presumably relates to its direct free radical scavenging ability and possibly to other antioxidative actions of melatonin.

Cataractogenesis and lipid peroxidation in newborn rats treated with buthionine sulfoximine: preventive actions of melatonin

The purpose of this study was to examine the influence of exogenously administered melatonin on cataract formation and lipid peroxidation in newborn rats treated with buthionine sulfoximine (BSO), a drug which inhibits the rate-limiting enzyme in glutathione (GSH) synthesis, gamma-glutamylcysteine synthase, thereby depleting animals of their stores of the important intracellular antioxidant, GSH. BSO (3 mmol/kg BW) was given for three consecutive days beginning on postnatal day 2; melatonin (4 mg/kg) was injected daily beginning on postnatal day 2 and continuing until the animals were killed (either day 9 or day 17 after birth). None of the control animals (rats treated with neither BSO nor with melatonin) developed lenticular opacification during the observation period. In the BSO-treated rats, 16 of 18 animals (89%) had observable cataracts when they were examined. In rats that received both BSO and melatonin, the incidence of cataracts was highly significantly decreased, i.e., only 3 of 18 rats (7%) had observable cataracts. In addition to cataracts, the level of lipid peroxidation products (malondialdehyde (MDA) and 4-hydroxyalkenals (4-HDA)) was examined in the lens, brain, liver, lung, and kidney of control and experimental animals. In BSO-treated rats, the lens, kidney, and lung exhibited increased levels of MDA plus 4-HDA relative to those measured in the control rats; these increases were reversed in the BSO-treated rats who were injected with melatonin daily. While BSO administration did not increase basal levels of MDA plus 4-HDA in either the brain or liver, melatonin reduced levels of lipid peroxidation products below those measured in the control rats (at 17 days after birth). The changes induced by melatonin are consistent with the free-radical scavenging and antioxidative properties of this indole.

An expert system for performance-based direct delivery of published clinical evidence

OBJECTIVE

To develop a system for clinical performance improvement through rule-based analysis of medical practice patterns and individualized distribution of published scientific evidence.

METHODS

The Quality Feedback Expert System (QFES) was developed by applying a Level-5 expert system shell to generate clinical direct reports for performance improvement. The system comprises three data and knowledge bases: 1) a knowledge base of measurable clinical practice parameters; 2) a practice pattern database of provider-specific numbers of patients and clinical activities; and 3) a management rule base comprising "redline rules" that identify providers whose practice styles vary significantly. Clinical direct reports consist of a table of practice data highlighting individual utilization vs recommendation and selected pertinent statements from medical literature.

RESULTS

The QFES supports integration of recommendations from several guidelines into a comprehensive and measurable quality improvement plan, analysis of actual practice patterns and comparison with accepted recommendations, and generation of a confidential individualized direct report to those who significantly deviate from clinical recommendations. The feasibility of the practice pattern analysis by the QFES was demonstrated in a sample of 182 urinary tract infection cases from a primary care clinic. In a set of clinical activities, four questions/procedures were associated with significant (p < 0.001) and unexplained variation.

CONCLUSION

The QFES provides a flexible tool for the implementation of clinical practice guidelines in diverse and changing clinical areas without the need for special program development. Preliminary studies indicate utility in the analysis of clinical practice variation and deviations. Using data obtained through a retrospective chart audit, the QFES was able to detect overutilization, and to identify nonrandom differences in practice patterns.

Multipurpose practice guideline modules for clinical decision analysis and quality improvement

This study aimed to measure the loss of information in communication between clinical researchers and information system developers, to design a tool for sharing clinical practice guidelines, and to test the feasibility of this tool in knowledge base development. The analysis of a sample of 101 randomized controlled clinical trials indicated that exact numeric results were published in only 64 percent of the trials. While structured abstracts were associated with improved total quality scores (p0.001), there was no significant improvement in reporting numeric results (p=0.31). Based on the documented loss of information and the needs of various parties involved, the concept of a multipurpose knowledge module was developed for sharing clinical practice recommendations. Such clinical practice guideline modules were applied in developing the knowledge base of a quality feedback expert system (QFES).

Differential binding of nuclear c-ets-1 protein to an intron I fragment of the c-myb gene in growth versus differentiation

For growth, ML-1 human myeloblastic leukemia cells require insulin-like growth factor 1 together with transferrin, whereas for differentiation they depend upon transforming growth factor beta in combination with transferrin. As shown in this study, growth stimulation is accompanied by c-myb expression, whereas initiation of differentiation results in the cessation of c-myb expression through premature termination of transcription in the first intron of the myb gene. Growth factor-stimulated c-myb elongation was found to correlate with an elevated level of nuclear c-ets-1 protein and with increased binding of this protein to an 18-base pair sequence in intron 1 of the c-myb gene containing the putative regulatory element PEA 3. In contrast, differentiation factor-initiated ML-1 cell maturation was accompanied by a very low level of nuclear c-ets-1 protein, by the inability to detect binding of the protein to the 18-base pair sequence, and by the cessation of c-myb expression. These results show a correlation to exist between c-ets-1 binding to intron 1 of the c-myb gene and c-myb expression. The mechanism underlying this correlation is under further study.

Down-regulation of c-myc gene expression with induction of high molecular weight DNA fragments by fluorodeoxyuridine

5-Fluoro-2'-deoxyuridine (FdUrd), a potent inhibitor of thymidylate synthase, induces extensive bulk DNA damage at drug concentrations that produce significant in vitro growth inhibition of human ileocecal carcinoma (HCT-8) cells. Constant- and pulsed-field gel electrophoresis (CFGE and PFGE), to detect size distribution of DNA double-strand breaks and repair kinetics, in parallel with northern and western blot analyses, to quantitate c-myc gene and protein expression, were utilized to analyze drug effects. At 24-hr post in vitro drug treatment, when maximum bulk DNA damage was detected, FdUrd produced a broad range of high molecular weight DNA fragments, clustering between 0.1 and 5.7 megabases in size, and resulted in a decrease in the level of c-myc transcripts and protein with no significant effect on the level of v-myc and H-ras. These effects preceded the observed cellular growth inhibition. Addition of the reduced folate leucovorin potentiated the effects induced by FdUrd, indicating that thymidylate synthase inhibition is an important initial step in drug effect followed by DNA fragmentation and suppression of c-myc expression. Changes in the integrity of the genetic materials and regulatory genes occurred prior to the observed cell growth inhibition by FdUrd, suggesting that these molecular alterations by FdUrd may be associated with subsequent FdUrd-induced cell growth inhibition.