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Determining the occurrence of disinfection byproducts (DBPs) in drinking water distribution system (DWDS) remains challenging. Predicting DBPs using readily available water quality parameters can help to understand DBPs associated risks and capture the complex interrelationships between water quality and DBP occurrence. In this study, we collected drinking water samples from a distribution network throughout a year and measured the related water quality parameters (WQPs) and haloacetic acids (HAAs). 12 machine learning (ML) algorithms were evaluated. Random Forest (RF) achieved the best performance (i.e., R 2 of 0.78 and RMSE of 7.74) for predicting HAAs concentration. Instead of using cytotoxicity or genotoxicity separately as the surrogate for evaluating toxicity associated with HAAs, we created a health risk index (HRI) that was calculated as the sum of cytotoxicity and genotoxicity of HAAs following the widely used Tic-Tox approach. Similarly, ML models were developed to predict the HRI, and RF model was found to perform the best, obtaining R 2 of 0.69 and RMSE of 0.38. To further explore advanced ML approaches, we developed 3 models using uncertainty-based active learning. Our findings revealed that Categorical Boosting Regression (CAT) model developed through active learning substantially outperformed other models, achieving R 2 of 0.87 and 0.82 for predicting concentration and the HRI, respectively. Feature importance analysis with the CAT model revealed that temperature, ions (e.g., chloride and nitrate), and DOC concentration in the distribution network had a significant impact on the occurrence of HAAs. Meanwhile, chloride ion, pH, ORP, and free chlorine were found as the most important features for HRI prediction. This study demonstrates that ML has the potential in the prediction of HAA occurrence and toxicity. By identifying key WQPs impacting HAA occurrence and toxicity, this research offers valuable insights for targeted DBP mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Cancer remains a critical global health challenge, with limited progress in reducing mortality despite advancements in diagnosis and treatment. The growing resistance of tumors to existing chemotherapy exacerbates this burden. In response, the search for new anticancer compounds from plants has intensified, given their historical success in yielding effective treatments. This review focuses on α-solanine, a glycoalkaloid primarily derived from potato tubers and nightshade family plants, recognized for its diverse biological activities, including anti-allergic, antipyretic, anti-inflammatory, anti-diabetic, and antibiotic properties. Recently, α-solanine has gained attention as a potential anticancer agent. Utilizing resources like PubMed/MedLine, ScienceDirect, Web of Science, Scopus, the American Chemical Society, Google Scholar, Springer Link, Wiley, and various commercial websites, this review consolidates two decades of research on α-solanine's anticancer effects and mechanisms against nine different cancers, highlighting its role in modulating various signaling pathways. It also discusses α-solanine's potential as a lead compound in cancer therapy. The abundant availability of potato peel, often discarded as waste or sold cheaply, is suggested as a sustainable source for large-scale α-solanine extraction. The study concludes that α-solanine holds promise as a standalone or adjunctive cancer treatment. However, further research is necessary to optimize this lead compound and mitigate its toxicity through various strategies., Competing Interests: None., (© 2024 The Authors. Food Science & Nutrition published by Wiley Periodicals LLC.)

Drinking water disinfection can result in the formation disinfection byproducts (DBPs, > 700 have been identified to date), many of them are reportedly cytotoxic, genotoxic, or developmentally toxic. Analyzing the toxicity levels of these contaminants experimentally is challenging, however, a predictive model could rapidly and effectively assess their toxicity. In this study, machine learning models were developed to predict DBP cytotoxicity based on their chemical information and exposure experiments. The Random Forest model achieved the best performance (coefficient of determination of 0.62 and root mean square error of 0.63) among all the algorithms screened. Also, the results of a probabilistic model demonstrated reliable model predictions. According to the model interpretation, halogen atoms are the most prominent features for DBP cytotoxicity compared to other chemical substructures. The presence of iodine and bromine is associated with increased cytotoxicity levels, while the presence of chlorine is linked to a reduction in cytotoxicity levels. Other factors including chemical substructures (CC, N, CN, and 6-member ring), cell line, and exposure duration can significantly affect the cytotoxicity of DBPs. The similarity calculation indicated that the model has a large applicability domain and can provide reliable predictions for DBPs with unknown cytotoxicity. Finally, this study showed the effectiveness of data augmentation in the scenario of data scarcity., Competing Interests: Declaration of Competing Interest The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Resistance to apoptosis stands as a roadblock to the successful pharmacological execution of anticancer drug effect. A comprehensive insight into apoptotic signaling pathways and an understanding of the mechanisms of apoptosis resistance are crucial to unveil new drug targets. At this juncture, researchers are heading towards natural sources in particular, mushroom as their potential drugs leads to being the reliable source of potent bioactive compounds. Given the continuous increase in cancer cases, the potent anticancer efficacy of mushrooms has inevitably become a fascinating object to researchers due to their higher safety margin and multitarget. This review aimed to collect and summarize all the available scientific data on mushrooms from their extracts to bioactive molecules in order to suggest their anticancer attributes via a mitochondrion -mediated intrinsic signaling mechanism. Compiled data revealed that bioactive components of mushrooms including polysaccharides, sterols and terpenoids as well as extracts prepared using 15 different solvents from 53 species could be effective in the supportive treatment of 20 various cancers. The underlying therapeutic mechanisms of the studied mushrooms are explored in this review through diverse and complementary investigations: in vitro assays, pre-clinical studies and clinical randomized controlled trials. The processes mainly involved were ROS production, mitochondrial membrane dysfunction, and action of caspase 3, caspase 9, XIAP, cIAP, p53, Bax, and Bcl-2. In summary, the study provides facts pertaining to the potential beneficial effect of mushroom extracts and their active compounds against various types of cancer and is shedding light on the underlying targeted signaling pathways., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Bifenthrin (BF) is a pyrethroid insecticide used in urban and agricultural applications. Previous studies in early life stages of fish have indicated anti-estrogenic activity; however, estrogenic activity has been observed in adults. To test the hypothesis that BF impairs sex differentiation, larval Japanese Medaka were exposed to BF during a critical developmental window for phenotypic sexual differentiation. Fish were exposed to environmentally relevant concentrations of BF (0.15 μg/L and 1.5 μg/L), a single concentration (0.3 mg/L) of an estrogen receptor (ER) antagonist (ICI 182,780), and an ER agonist (0.2 ug/L) (17β-estradiol). Fish were exposed at 8 days post hatch (dph) larvae for 30 days. Phenotypic sex, secondary sexual characteristics (SSC) and genotypic sex were investigated at sexual maturity (8 weeks). A trend towards masculinization (p = 0.06) based on the presence of papillary processes in anal fin rays of Japanese Medaka was observed in fish exposed to the lowest concentration of BF. However, genotypic gender ratios were not altered. These results show sex differentiation was not significantly altered by larval exposure to BF in Japanese medaka., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Mushrooms are customary influential sources of pharmaceutically active metabolites. Usually lanostane-type triterpenoids from mushrooms had prospective for cancer disease treatments. Recently, a triterpenoid, astrakurkurol obtained from the fresh basidiocarps of the edible mushroom Astraeus hygrometricus , drew attention as a new cytotoxic therapeutic. The structural stability of this triterpenoid had been established with the amalgamation of density functional theory (DFT) calculations and study of single-crystal X-ray diffraction. To successfully manifest astrakurkurol as a potent cytotoxic therapeutics, a wide apprehension on the molecular and cellular mechanisms underlying their action is prerequisite. On this account, our study was directed to scrutinize the influence of this triterpenoid on human hepatocellular cancer cell model Hep3B. Encapsulating all experimental facts revealed that astrakurkurol had significantly decreased cell viability in a concentration-dependent manner. This effect was unveiled to be apoptosis, documented by DNA fragmentation, chromatin condensation, nuclear shrinkage, membrane blebing, and imbalance of cell cycle distribution. Astrakurkurol persuaded the expression of death receptor associated proteins (Fas), which triggered caspase-8 activation following tBid cleavage. Moreover, tBid mediated ROS generation, which triggered mitochondrial dysfunction and activated the mitochondrial apoptotic events. Astrakurkurol cytotoxicity was based on caspase-8-mediated intrinsic apoptotic pathway and was associated with inhibition at Akt and NF-κB pathway. Astrakurkurol had also inhibited the migration of Hep3B cells, indicating its antimigratory potential. These findings led us to introduce astrakurkurol as a feasible and natural source for a safer cytotoxic drug against hepatocellular carcinoma.

The cancer-predisposing Lynch Syndrome (LS) arises from germline mutations in DNA mismatch repair (MMR) genes, predominantly MLH1, MSH2, MSH6, and PMS2. A major challenge for clinical diagnosis of LS is the frequent identification of variants of uncertain significance (VUS) in these genes, as it is often difficult to determine variant pathogenicity, particularly for missense variants. Generic programs such as SIFT and PolyPhen-2, and MMR gene-specific programs such as PON-MMR and MAPP-MMR, are often used to predict deleterious or neutral effects of VUS in MMR genes. We evaluated the performance of multiple predictive programs in the context of functional biologic data for 15 VUS in MLH1, MSH2, and PMS2. Using cell line models, we characterized VUS predicted to range from neutral to pathogenic on mRNA and protein expression, basal cellular viability, viability following treatment with a panel of DNA-damaging agents, and functionality in DNA damage response (DDR) signaling, benchmarking to wild-type MMR proteins. Our results suggest that the MMR gene-specific classifiers do not always align with the experimental phenotypes related to DDR. Our study highlights the importance of complementary experimental and computational assessment to develop future predictors for the assessment of VUS.

Desalination is a promising sustainable solution to meet growing water needs of cities across the United States. However, the environmental impacts of the resulting filtrate (brine) discharged to surface water need to be evaluated before large-scale desalination can be successful in the United States. Developing fish are especially sensitive to changes in salinity and varying ionic composition. Limited research is available on the impacts of hypersalinity on chronic vertebrate embryonic development, particularly on sublethal effects. To investigate this, Japanese medaka (Oryzias latipes) embryos were treated with: (1) graphite filtered freshwater; (2) artificial seawater [17, 35, 42, 56, and 70 parts per thousand (ppt)]; (3) effluent from a desalination facility at Monterey Bay Aquarium, CA, diluted to 75, 50, and 25% with 35 ppt artificial seawater to simulate mixing (39, 42, 46, and 50 ppt); (4) artificial San Joaquin River water (CA, USA) (9, 13, and 17 ppt); and (5) artificial San Joaquin River water diluted to 75, 50, and 25% with artificial seawater to simulate estuarine mixing in the San Francisco Bay (13, 19, 24, and 30 ppt). Percent hatch, survival post hatch, deformities, swim bladder inflation, and median day to hatch were recorded to calculate EC 50 (50% effect concentration) and NOEC (no observable effect concentration) values. No significant difference was observed between artificial seawater and Monterey Bay aquarium effluent (EC 50  = 45-55 ppt). However, San Joaquin River water decreased survival post hatch and increased deformities in comparison to artificial seawater and San Joaquin River water mixed with seawater, suggesting that unique ion compositions may play a role in embryo and larval toxicity.