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Objectives: Identifying stable co-occurring symptoms in breast cancer (BC) patients during chemotherapy can improve symptom management and the treatment process. This study examines symptom cluster stability in Iranian BC patients receiving chemotherapy and evaluates stability across three-time points within each cluster., Methods: This study collected data from three-time points: initial chemotherapy commencement, 2½ months postdiagnosis, and 5 months postdiagnosis. The research used exploratory factor analysis (EFA) in combination with hierarchical cluster analysis (HCA) and K means clustering to address research questions., Results: In the initial clustering step, EFA identified five clusters with high consistency across three-time points. The first cluster showed depression, anxiety and irritability, while the second cluster was characterised by sexual interest and pain. The third cluster was associated with diarrhoea and vomiting. In the second step, we obtained the HCA item output and two clusters of K means clustering that recorded depression and anxiety symptoms over time. Vomiting, dry mouth, sexual interest, worrying and numbness were observed during the first and second points, but the frequency has decreased since then., Conclusions: Cancer's psychological and physiological symptoms, including depression, anxiety, digestive and hormonal issues, remain stable throughout the disease. Palliative care centres can improve patients' quality of life and treatment process by addressing persistent symptoms., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology., (© 2024. The Author(s).)

Climate change is one of the biggest environmental challenges that significantly impact water resources and the quantity and quality of agricultural products. Assessment of these impacts during the historical period and under future climate is essential for achieving a sustainable agricultural system in the face of climate change threats and water scarcity. In this research, we evaluated the yield and water footprint of rainfed and irrigated wheat during the historical period (1986-2015) and two future periods (2016 to 2055) in a semi-arid environment in Fars province, Iran. The future climate data was selected from the CanESM2 model outputs (bias-corrected and downscaled using the SDSM model) under the RCP4.5 scenario, and the yield projection was made using the AquaCrop model. Our result showed that for both irrigated and rainfed wheat, the yield significantly increases in southern parts of the study area in future climates, primarily because of an increase in effective precipitation. Other regions will experience a marginal yield decrease or no yield changes (in the case of irrigated wheat). Our assessments of the water footprint of wheat production showed a significant reduction in green and blue water footprints in the southern regions. In other regions, various patterns emerged for irrigated and rainfed wheat, but an overall increase was observed. The southern regions of the study area will be more suitable for wheat production owing to the higher yield and lower water footprint., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets., Supplementary Information: The online version contains supplementary material available at 10.1007/s00521-021-06459-9., Competing Interests: Conflict of interestThe authors declare no conflicts of interest., (© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2021.)

Objective: Forecasting the time of future outbreaks would minimize the impact of diseases by taking preventive steps including public health messaging and raising awareness of clinicians for timely treatment and diagnosis. The present study investigated the accuracy of support vector machine, artificial neural-network, and random-forest time series models in influenza like illness (ILI) modeling and outbreaks detection. The models were applied to a data set of weekly ILI frequencies in Iran. The root mean square errors (RMSE), mean absolute errors (MAE), and intra-class correlation coefficient (ICC) statistics were employed as evaluation criteria., Results: It was indicated that the random-forest time series model outperformed other three methods in modeling weekly ILI frequencies (RMSE = 22.78, MAE = 14.99 and ICC = 0.88 for the test set). In addition neural-network was better in outbreaks detection with total accuracy of 0.889 for the test set. The results showed that the used time series models had promising performances suggesting they could be effectively applied for predicting weekly ILI frequencies and outbreaks.

Purpose: This work aims to develop a unified methodology for the false positives reduction in lung nodules computer-aided detection schemes., Methods: The 3D region of each detected nodule candidate is first reconstructed using the sparse field method for accurately segmenting the objects. This technique enhances the level set modeling by restricting the computations to a narrow band near the evolving curve. Then, a set of 2D and 3D relevant features are extracted for each segmented candidate. Subsequently, a hybrid undersampling/boosting algorithm called RUSBoost is applied to analyze the features and discriminate real nodules from non-nodules., Results: The performance of the proposed scheme was evaluated by using 70 CT images, randomly selected from the Lung Image Database Consortium and containing 198 nodules. Applying RUSBoost classifier exhibited a better performance than some commonly used classifiers. It effectively reduced the average number of FPs to only 3.9 per scan based on a fivefold cross-validation., Conclusion: The practical implementation, applicability for different nodule types and adaptability in handling the imbalanced data classification insure the improvement in lung nodules detection by utilizing this new approach.

Background: According to the national strategy plan on monitoring of insecticides resistance, this study was carried out to determine the base line susceptibility of the Culicidae mosquitoes to the WHO-recommended insecticides in an endemic focus of malaria in southeastern Iran., Methods: Larval collection was carried out by dipping method and adult collection occurred by suction tube from January to December 2010. The susceptibility test was assessed to DDT 4 %, malathion 5 %, propoxur 0.1 %, deltamethrin 0.05 %, lambda-cyhalothrin 0.05 %, and cyfluthrin 0.15 % at different interval times (discriminative dose) followed by 24 h recovery period. The LT50 and LT90 values were calculated for plotting the regression line using Microsoft office Excel software ver. 2007., Results: Anopheles stephensi was quite resistant to DDT and showed susceptible or tolerant to other insecticides. The LT50 and LT90 values to DDT in this species were 29.07, and 98.26 minutes, respectively. Anopheles culicifacies and Anopheles dthali were found susceptible or tolerant to insecticides. Culex pipiens was found resistance to DDT, propoxur, lambda-cyhalothrin and cyfluthrin whereas observed susceptible to malathion and tolerant to deltamethrin. Ochlerotatus caspius sl. was resistant to DDT, whereas found susceptible to other insecticides. Culisita longiareolata was susceptible to deltamethrin, whereas tolerant to other insecticides. The LT50 and LT90 values of Cs. longiareolata to DDT were 17.82, and 51.26 minutes., Conclusion: We suggested the same study in different parts of the country for monitoring and evaluation of control measures.

Background: Mosquito-borne diseases are a major public health threat in Iran. The objective of this study was to determine the fauna of culicinae mosquitoes for future mosquito control programs., Methods: THREE GENERA AND ELEVEN SPECIES OF THE SUBFAMILY CULICINAE (DIPTERA: Culicidae) were collected by dipping technique and identified in Chabahar County, Sistan and Baluchistan Province, southeastern Iran, during January, February, and March 2007., Results: THE COLLECTED SPECIES INCLUDED: Aedes vexans (new occurrence record for the province), Culex arbieeni, Cx. bitaeniorhynchus, Cx. deserticola, Cx. hortensis, Cx. perexiguus, Cx. pipiens, Cx. pseudovishnui, Cx. pusillus, Cx. quinquefasciatus, Cx. sinaiticus, Cx. theileri, Cx. tritaeniorhynchus, Culiseta longiareolata, Ochlerotatus caballus, Oc. caspius, and Uranotaenia unguiculata., Conclusion: Our observations indicate that, in South of Iran hot and wet climatic conditions support the persistence of culicinae mosquitoes. As our study, regular monitoring of culicinae mosquitoes in this area could be the most useful for mosquito control and mosquito-borne disease prevention.

Purpose: The purpose of this study was to report survival times and problems encountered with laboratory made space maintainers placed over 7 years by one pediatric dentist., Methods: Charts were reviewed for 235 patients with fixed space maintainers placed between January 1, 1997, and December 31, 2003, and followed to December 31, 2005. Problems encountered and appliance lifetimes were recorded and assessed. Failures were recorded as: (1) cement loss; (2) solder breakage; (3) split bands; (4) eruption interference; (5) bent wire; (6) complete loss; or (7) not specified. Appliance outcomes, transferred patients, and those lost to follow-up were recorded., Results: A total of 323 appliances were followed, with: (a) 93 (29%) successes; (b) 110 (34%) still in service; and (c) 104 (32%) known failures. Of the latter, most (62%) were from cement loss (60%) and 13 (12%) were totally lost. No statistically significant differences were noted between types of appliances, gender, and types of failure, except for the fact that bands and loops exhibited more cement loss (P=.045). Mean pooled survival times were between 26 and 27 months.. Of the 104 failures: (a) 34 (33%) were no longer needed (being considered clinically successful); (b) 57 (55%) were recemented; and (c) 13 (12%) were remade. Sixteen appliances were lost to follow-up or transferred., Conclusion: A total of 63% of all space maintainers lasted their anticipated lifetimes or were still in use.