Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion.

Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix-matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.

Feasibility of monitoring Global Breast Cancer Initiative Framework key performance indicators in 21 Asian National Cancer Centers Alliance member countries.

Background: The Global Breast Cancer Initiative (GBCI) Framework, launched by the World Health Organisation (WHO) in 2023, emphasises assessing, strengthening, and scaling up services for the early detection and management of breast cancer. This study aims to determine the feasibility of monitoring the status of breast cancer control in the 21 Asian National Cancer Centers Alliance (ANCCA) countries based on the three GBCI Framework key performance indicators (KPIs): stage at diagnosis, time to diagnosis, and treatment completion. Methods: We reviewed published literature on breast cancer control among 21 ANCCA countries from May to July 2023 to establish data availability and compiled the latest descriptive statistics and sources of the indicators using a standardised data collection form. We performed bivariate Pearson's correlation analysis to measure the strength of correlation between stage at diagnosis, mortality and survival rates, and universal health coverage. Findings: Only 12 (57%) ANCCA member countries published national cancer registry reports on breast cancer age-standardised incidence rate (ASIR) and age-standardised mortality rate (ASMR). Indonesia, Myanmar, and Nepal had provincial data and others relied on WHO's Global Cancer Observatory (GLOBOCAN) estimates. GLOBOCAN data differed from the reported national statistics by 5-10% in Bhutan, Indonesia, Iran, the Republic of Korea, Singapore, and Thailand and >10% in China, India, Malaysia, Mongolia, and Sri Lanka. The proportion of patients diagnosed in stages I and II strongly correlated with the five-year survival rate and with the universal health coverage (UHC) index. Three countries (14%) reported national data with >60% of invasive breast cancer patients diagnosed at stages I and II, and a five-year survival rate of >80%. Over 60% of the ANCCA countries had no published national data on breast cancer staging, the time interval from presentation to diagnosis, and diagnosis to treatment. Five (24%) countries reported data on treatment completion. The definition of delayed diagnosis and treatment completion varied across countries. Interpretation: GBCI's Pillar 1 KPI correlates strongly with five-year survival rate and with the UHC index. Most ANCCA countries lacked national data on cancer staging, timely diagnosis, and treatment completion KPIs. While institutional-level data were available in some countries, they may not represent the nationwide status. Strengthening cancer surveillance is crucial for effective breast cancer control. The GBCI Framework indicators warrant more detailed definitions for standardised data collection. Surrogate indicators which are measurable and manageable in country-specific settings, could be considered for monitoring GBCI indicators. Ensuring UHC and addressing health inequalities are essential to early diagnosis and treatment of breast cancer. Funding: Funding for this research article's processing fee (APC) will be provided by the affiliated institution to support the open-access publication of this work. The funding body is not involved in the study design; collection, management, analysis and interpretation of data; or the decision to submit for publication. The funding body will be informed of any planned publications, and documentation provided.

Retrospective Evaluation of Hairy Cell Leukemia Patients: Analysis of a Long-Term Single Center Data.

Background: Hairy cell leukemia (HCL) is a distinct lymphoproliferative disorder with unique circulating lymphocyte morphology. It is now regarded as an indolent disease yet treatable with purine analogs. We are going to present a complete long-term clinical and prognostic report of our HCL patients as a large cohort in Iran. Materials and Methods: All patients diagnosed with HCL, according to the World Health Organization (WHO) criteria, were enrolled in this study. They were referred to our academic center between 1995 and 2020. Treatment with a daily cladribine regimen was initiated as indicated and patients were followed. Survival data and clinical outcomes of patients were calculated. Results: A total of 50 patients were studied (76% male). The median time to treatment was 4.8 months and complete remission was achieved in 92% of patients. Nine patients (18%) experienced relapse with a median time to relapse of 47 months. After a median follow-up of 51 months, the median OS was not reached and after 234 months, the overall survival rate was 86%. Survival was worse in patients with non-classic HCL (vHCL) compared to classic HCL. Conclusion: Our long-term follow-up data confirmed the favorable outcomes of Iranian HCL patients with cladribine and provide a useful viewpoint of the disease.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug-target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions.

Predicting the interactions between drugs and targets plays an important role in the process of new drug discovery, drug repurposing (also known as drug repositioning). There is a need to develop novel and efficient prediction approaches in order to avoid the costly and laborious process of determining drug-target interactions (DTIs) based on experiments alone. These computational prediction approaches should be capable of identifying the potential DTIs in a timely manner. Matrix factorization methods have been proven to be the most reliable group of methods. Here, we first propose a matrix factorization-based method termed 'Coupled Matrix-Matrix Completion' (CMMC). Next, in order to utilize more comprehensive information provided in different databases and incorporate multiple types of scores for drug-drug similarities and target-target relationship, we then extend CMMC to 'Coupled Tensor-Matrix Completion' (CTMC) by considering drug-drug and target-target similarity/interaction tensors. Results: Evaluation on two benchmark datasets, DrugBank and TTD, shows that CTMC outperforms the matrix-factorization-based methods: GRMF, $L_{2,1}$-GRMF, NRLMF and NRLMF$\beta $. Based on the evaluation, CMMC and CTMC outperform the above three methods in term of area under the curve, F1 score, sensitivity and specificity in a considerably shorter run time.

An alternative test for determining autologous stem cell transplantation cell harvesting outcome: Comparison between predictive values of two tests.

Daily CD34+ cells enumeration as a success indicator of stem cell pheresis procedure using flow cytometry is costly, lengthy, and labor-intensive. Thus, finding a simpler method to achieve the optimum time for harvesting the minimum required stem cells for transplantation could be helpful. The aim of this study was to evaluate the predictive value of reticulocytes fractions and their sensesivity and specificity in guiding CD34+ cell harvesting by G-CSF mobilization strategy. In this study, 49 candidates for autologous peripheral blood stem cell transplantation were enrolled. Before leukapheresis, the immature reticulocytes fraction (IRF) and CD34+ cell count were measured. Moreover, patients were evaluated for leukapheresis outcomes in two MNC and cMNC groups. Here we demonstrated that IRF, LFR, and MFR with the associated criterion of >17.3, ≤82.5, and >15.9, respectively, earned 100 % specificity and 47.2 %, 47.22 %, and 41.46 % sensitivity to predict the minimum required CD34+ cell count. Furthermore, IRF-V (Value) and MFR-V with the associated criterion of >0.77 and >0.55, respectively, earned 58.33 %, 66.67 % sensitivity and 84.62 %, 69.23 % of specificity, separately. As only MFR-V was able to predict the platelet engraftment (P-value = 0.014), none of the other above mentioned factors were not able to predict the neutrophil engraftment. Likewise, it was shown that patients who underwent MNC leukapheresis had a statistically significantly higher total WBC, harvested CD34+ cells, MNCs/ kg, and lower apheresis durations (P-values<0.05). Taken together, using IRF and its maturity stages seems to be a compelling predictor of minimal required CD34+ cells in autologous peripheral blood stem cell transplantation.