[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.

The openVA Toolkit for Verbal Autopsies

Verbal autopsy (VA) is a survey-based tool widely used to infer cause of death (COD) in regions without complete-coverage civil registration and vital statistics systems. In such settings, many deaths happen outside of medical facilities and are not officially documented by a medical professional. VA surveys, consisting of signs and symptoms reported by a person close to the decedent, are used to infer the COD for an individual, and to estimate and monitor the COD distribution in the population. Several classification algorithms have been developed and widely used to assign causes of death using VA data. However, the incompatibility between different idiosyncratic model implementations and required data structure makes it difficult to systematically apply and compare different methods. The openVA package provides the first standardized framework for analyzing VA data that is compatible with all openly available methods and data structure. It provides an open-source, R implementation of several most widely used VA methods. It supports different data input and output formats, and customizable information about the associations between causes and symptoms. The paper discusses the relevant algorithms, their implementations in R packages under the openVA suite, and demonstrates the pipeline of model fitting, summary, comparison, and visualization in the R environment.

Impact of close interpersonal contact on COVID-19 incidence: Evidence from 1 year of mobile device data

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We quantified interpersonal contact at the population level using mobile device geolocation data. We computed the frequency of contact (within 6 feet) between people in Connecticut during February 2020 to January 2021 and aggregated counts of contact events by area of residence. When incorporated into a SEIR-type model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns. Contact in Connecticut explains the initial wave of infections during March to April, the drop in cases during June to August, local outbreaks during August to September, broad statewide resurgence during September to December, and decline in January 2021. The transmission model fits COVID-19 transmission dynamics better using the contact rate than other mobility metrics. Contact rate data can help guide social distancing and testing resource allocation.

Prevalence of current and past COVID-19 in Ohio adults

PURPOSE

To estimate the prevalence of current and past COVID-19 in Ohio adults.

METHODS

We used stratified, probability-proportionate-to-size cluster sampling. During July 2020, we enrolled 727 randomly-sampled adult English- and Spanish-speaking participants through a household survey. Participants provided nasopharyngeal swabs and blood samples to detect current and past COVID-19. We used Bayesian latent class models with multilevel regression and poststratification to calculate the adjusted prevalence of current and past COVID-19. We accounted for the potential effects of non-ignorable non-response bias.

RESULTS

The estimated statewide prevalence of current COVID-19 was 0.9% (95% credible interval: 0.1-2.0%), corresponding to ∼85,000 prevalent infections (95% credible interval: 6,300-177,000) in Ohio adults during the study period. The estimated statewide prevalence of past COVID-19 was 1.3% (95% credible interval: 0.2-2.7%), corresponding to ∼118,000 Ohio adults (95% credible interval: 22,000-240,000). Estimates did not change meaningfully due to non-response bias.

CONCLUSIONS

Total COVID-19 cases in Ohio in July 2020 were approximately 3.5 times as high as diagnosed cases. The lack of broad COVID-19 screening in the United States early in the pandemic resulted in a paucity of population-representative prevalence data, limiting the ability to measure the effects of statewide control efforts.

One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut

To support public health policymakers in Connecticut, we developed a flexible county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, and estimates of important features of disease transmission and clinical progression. In this paper, we outline the model design, implementation and calibration, and describe how projections and estimates were used to meet the changing requirements of policymakers and officials in Connecticut from March 2020 to February 2021. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We calibrated this model to data on deaths and hospitalizations and developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

One year of modeling and forecasting COVID-19 transmission to support policymakers in Connecticut

To support public health policymakers in Connecticut, we developed a county-structured compartmental SEIR-type model of SARS-CoV-2 transmission and COVID-19 disease progression. Our goals were to provide projections of infections, hospitalizations, and deaths, as well as estimates of important features of disease transmission, public behavior, healthcare response, and clinical progression of disease. In this paper, we describe a transmission model developed to meet the changing requirements of public health policymakers and officials in Connecticut from March 2020 to February 2021. We outline the model design, implementation and calibration, and describe how projections and estimates were used to support decision-making in Connecticut throughout the first year of the pandemic. We calibrated this model to data on deaths and hospitalizations, developed a novel measure of close interpersonal contact frequency to capture changes in transmission risk over time and used multiple local data sources to infer dynamics of time-varying model inputs. Estimated time-varying epidemiologic features of the COVID-19 epidemic in Connecticut include the effective reproduction number, cumulative incidence of infection, infection hospitalization and fatality ratios, and the case detection ratio. We describe methodology for producing projections of epidemic evolution under uncertain future scenarios, as well as analytical tools for estimating epidemic features that are difficult to measure directly, such as cumulative incidence and the effects of non-pharmaceutical interventions. The approach takes advantage of our unique access to Connecticut public health surveillance and hospital data and our direct connection to state officials and policymakers. We conclude with a discussion of the limitations inherent in predicting uncertain epidemic trajectories and lessons learned from one year of providing COVID-19 projections in Connecticut.

Impact of close interpersonal contact on COVID-19 incidence: evidence from one year of mobile device data

Close contact between people is the primary route for transmission of SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19). We sought to quantify interpersonal contact at the population-level by using anonymized mobile device geolocation data. We computed the frequency of contact (within six feet) between people in Connecticut during February 2020 - January 2021. Then we aggregated counts of contact events by area of residence to obtain an estimate of the total intensity of interpersonal contact experienced by residents of each town for each day. When incorporated into a susceptible-exposed-infective-removed (SEIR) model of COVID-19 transmission, the contact rate accurately predicted COVID-19 cases in Connecticut towns during the timespan. The pattern of contact rate in Connecticut explains the large initial wave of infections during March-April, the subsequent drop in cases during June-August, local outbreaks during August-September, broad statewide resurgence during September-December, and decline in January 2021. Contact rate data can help guide public health messaging campaigns to encourage social distancing and in the allocation of testing resources to detect or prevent emerging local outbreaks more quickly than traditional case investigation.

ONE SENTENCE SUMMARY

Close interpersonal contact measured using mobile device location data explains dynamics of COVID-19 transmission in Connecticut during the first year of the pandemic.

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.

Using Bayesian Latent Gaussian Graphical Models to Infer Symptom Associations in Verbal Autopsies

Learning dependence relationships among variables of mixed types provides insights in a variety of scientific settings and is a well-studied problem in statistics. Existing methods, however, typically rely on copious, high quality data to accurately learn associations. In this paper, we develop a method for scientific settings where learning dependence structure is essential, but data are sparse and have a high fraction of missing values. Specifically, our work is motivated by survey-based cause of death assessments known as verbal autopsies (VAs). We propose a Bayesian approach to characterize dependence relationships using a latent Gaussian graphical model that incorporates informative priors on the marginal distributions of the variables. We demonstrate such information can improve estimation of the dependence structure, especially in settings with little training data. We show that our method can be integrated into existing probabilistic cause-of-death assignment algorithms and improves model performance while recovering dependence patterns between symptoms that can inform efficient questionnaire design in future data collection.

COVID-19 projections for reopening Connecticut

Key Points

Closure of schools and the statewide “Stay Safe, Stay Home” order have effectively reduced COVID-19 transmission in Connecticut, with model projections estimating incidence at about 1,300 new infections per day.

If close interpersonal contact increases quickly in Connecticut following reopening on May 20, the state is at risk of a substantial increase of COVID-19 infections, hospitalizations, and deaths by late Summer 2020.

Real-time metrics including case counts, hospitalizations, and deaths may fail to provide enough advance warning to avoid resurgence.

Substantial uncertainty remains in our knowledge of cumulative COVID-19 incidence, the proportion of infected individuals who are asymptomatic, infectiousness of children, the effects of testing and contact tracing on isolation of infected individuals, and how contact patterns may change following reopening.

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。

Non-confirming replication of "Performance of InSilicoVA for assigning causes of death to verbal autopsies: multisite validation study using clinical diagnostic gold standards," by Flaxman et al

BACKGROUND

A verbal autopsy (VA) is an interview conducted with the caregivers of someone who has recently died to describe the circumstances of the death. In recent years, several algorithmic methods have been developed to classify cause of death using VA data. The performance of one method-InSilicoVA-was evaluated in a study by Flaxman et al., published in BMC Medicine in 2018. The results of that study are different from those previously published by our group.

METHODS

Based on the description of methods in the Flaxman et al. study, we attempt to replicate the analysis to understand why the published results differ from those of our previous work.

RESULTS

We failed to reproduce the results published in Flaxman et al. Most of the discrepancies we find likely result from undocumented differences in data pre-processing, and/or values assigned to key parameters governing the behavior of the algorithm.

CONCLUSION

This finding highlights the importance of making replication code available along with published results. All code necessary to replicate the work described here is freely available on GitHub.

BAYESIAN FACTOR MODELS FOR PROBABILISTIC CAUSE OF DEATH ASSESSMENT WITH VERBAL AUTOPSIES

The distribution of deaths by cause provides crucial information for public health planning, response and evaluation. About 60% of deaths globally are not registered or given a cause, limiting our ability to understand disease epidemiology. Verbal autopsy (VA) surveys are increasingly used in such settings to collect information on the signs, symptoms and medical history of people who have recently died. This article develops a novel Bayesian method for estimation of population distributions of deaths by cause using verbal autopsy data. The proposed approach is based on a multivariate probit model where associations among items in questionnaires are flexibly induced by latent factors. Using the Population Health Metrics Research Consortium labeled data that include both VA and medically certified causes of death, we assess performance of the proposed method. Further, we estimate important questionnaire items that are highly associated with causes of death. This framework provides insights that will simplify future data.

[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.

Suspected heroin-related overdoses incidents in Cincinnati, Ohio: A spatiotemporal analysis

BACKGROUND

Opioid misuse and deaths are increasing in the United States. In 2017, Ohio had the second highest overdose rates in the US, with the city of Cincinnati experiencing a 50% rise in opioid overdoses since 2015. Understanding the temporal and geographic variation in overdose emergencies may help guide public policy responses to the opioid epidemic.

METHODS AND FINDINGS

We used a publicly available data set of suspected heroin-related emergency calls (n = 6,246) to map overdose incidents to 280 census block groups in Cincinnati between August 1, 2015, and January 30, 2019. We used a Bayesian space-time Poisson regression model to examine the relationship between demographic and environmental characteristics and the number of calls within block groups. Higher numbers of heroin-related incidents were found to be associated with features of the built environment, including the proportion of parks (relative risk [RR] = 2.233; 95% credible interval [CI]: [1.075-4.643]), commercial (RR = 13.200; 95% CI: [4.584-38.169]), manufacturing (RR = 4.775; 95% CI: [1.958-11.683]), and downtown development zones (RR = 11.362; 95% CI: [3.796-34.015]). The number of suspected heroin-related emergency calls was also positively associated with the proportion of male population, the population aged 35-49 years, and distance to pharmacies and was negatively associated with the proportion aged 18-24 years, the proportion of the population with a bachelor's degree or higher, median household income, the number of fast food restaurants, distance to hospitals, and distance to opioid treatment programs. Significant spatial and temporal heterogeneity in the risks of incidents remained after adjusting for covariates. Limitations of this study include lack of information about the nature of incidents after dispatch, which may differ from the initial classification of being related to heroin, and lack of information on local policy changes and interventions.

CONCLUSIONS

We identified areas with high numbers of reported heroin-related incidents and features of the built environment and demographic characteristics that are associated with these events in the city of Cincinnati. Publicly available information about opiate overdoses, combined with data on spatiotemporal risk factors, may help municipalities plan, implement, and target harm-reduction measures. In the US, more work is necessary to improve data availability in other cities and states and the compatibility of data from different sources in order to adequately measure and monitor the risk of overdose and inform health policies.

Automated versus physician assignment of cause of death for verbal autopsies: randomized trial of 9374 deaths in 117 villages in India

BACKGROUND

Verbal autopsies with physician assignment of cause of death (COD) are commonly used in settings where medical certification of deaths is uncommon. It remains unanswered if automated algorithms can replace physician assignment.

METHODS

We randomized verbal autopsy interviews for deaths in 117 villages in rural India to either physician or automated COD assignment. Twenty-four trained lay (non-medical) surveyors applied the allocated method using a laptop-based electronic system. Two of 25 physicians were allocated randomly to independently code the deaths in the physician assignment arm. Six algorithms (Naïve Bayes Classifier (NBC), King-Lu, InSilicoVA, InSilicoVA-NT, InterVA-4, and SmartVA) coded each death in the automated arm. The primary outcome was concordance with the COD distribution in the standard physician-assigned arm. Four thousand six hundred fifty-one (4651) deaths were allocated to physician (standard), and 4723 to automated arms.

RESULTS

The two arms were nearly identical in demographics and key symptom patterns. The average concordances of automated algorithms with the standard were 62%, 56%, and 59% for adult, child, and neonatal deaths, respectively. Automated algorithms showed inconsistent results, even for causes that are relatively easy to identify such as road traffic injuries. Automated algorithms underestimated the number of cancer and suicide deaths in adults and overestimated other injuries in adults and children. Across all ages, average weighted concordance with the standard was 62% (range 79-45%) with the best to worst ranking automated algorithms being InterVA-4, InSilicoVA-NT, InSilicoVA, SmartVA, NBC, and King-Lu. Individual-level sensitivity for causes of adult deaths in the automated arm was low between the algorithms but high between two independent physicians in the physician arm.

CONCLUSIONS

While desirable, automated algorithms require further development and rigorous evaluation. Lay reporting of deaths paired with physician COD assignment of verbal autopsies, despite some limitations, remains a practicable method to document the patterns of mortality reliably for unattended deaths.

TRIAL REGISTRATION

ClinicalTrials.gov , NCT02810366. Submitted on 11 April 2016.

An Expectation Conditional Maximization approach for Gaussian graphical models

Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In high-dimensional settings, the space of possible graphs becomes enormous, rendering even state-of-the-art Bayesian stochastic search computationally infeasible. We propose a deterministic alternative to estimate Gaussian and Gaussian copula graphical models using an Expectation Conditional Maximization (ECM) algorithm, extending the EM approach from Bayesian variable selection to graphical model estimation. We show that the ECM approach enables fast posterior exploration under a sequence of mixture priors, and can incorporate multiple sources of information.

Bayesian Joint Spike-and-Slab Graphical Lasso

In this article, we propose a new class of priors for Bayesian inference with multiple Gaussian graphical models. We introduce Bayesian treatments of two popular procedures, the group graphical lasso and the fused graphical lasso, and extend them to a continuous spike-and-slab framework to allow self-adaptive shrinkage and model selection simultaneously. We develop an EM algorithm that performs fast and dynamic explorations of posterior modes. Our approach selects sparse models efficiently and automatically with substantially smaller bias than would be induced by alternative regularization procedures. The performance of the proposed methods are demonstrated through simulation and two real data examples.

Probabilistic Cause-of-death Assignment using Verbal Autopsies

In regions without complete-coverage civil registration and vital statistics systems there is uncertainty about even the most basic demographic indicators. In such regions the majority of deaths occur outside hospitals and are not recorded. Worldwide, fewer than one-third of deaths are assigned a cause, with the least information available from the most impoverished nations. In populations like this, verbal autopsy (VA) is a commonly used tool to assess cause of death and estimate cause-specific mortality rates and the distribution of deaths by cause. VA uses an interview with caregivers of the decedent to elicit data describing the signs and symptoms leading up to the death. This paper develops a new statistical tool known as InSilicoVA to classify cause of death using information acquired through VA. InSilicoVA shares uncertainty between cause of death assignments for specific individuals and the distribution of deaths by cause across the population. Using side-by-side comparisons with both observed and simulated data, we demonstrate that InSilicoVA has distinct advantages compared to currently available methods.

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.

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.