Rapid and non-invasive diagnosis of hyperkalemia in patients with systolic myocardial failure using a model based on machine learning algorithms.

Background: Hyperkalemia is a potentially life-threatening electrolyte disturbance that if not diagnosed on time may lead to devastating conditions and sudden cardiac death. Blood sampling for potassium level checks is time-consuming and can delay the treatment of severe hyperkalemia on time. So, we propose a non-invasive method for correct and rapid hyperkalemia detection. Methods: The cardiac signal of patients referred to the Pediatrics Emergency room of Shahid Rejaee Hospital was measured by a 12-lead Philips electrocardiogram (ECG) device. Immediately, the blood samples of the patients were sent to the laboratory for potassium serum level determination. We defined 16 features for each cardiac signal at lead 2 and extracted them automatically using the algorithm developed. With the help of the principal component analysis (PCA) algorithm, the dimension reduction operation was performed. The algorithms of decision tree (DT), random forest (RF), logistic regression, and support vector machine (SVM) were used to classify serum potassium levels. Finally, we used the receiver operation characteristic (ROC) curve to display the results. Results: In the period of 5 months, 126 patients with a serum level above 4.5 (hyperkalemia) and 152 patients with a serum potassium level below 4.5 (normal potassium) were included in the study. Classification with the help of a RF algorithm has the best result. Accuracy, Precision, Recall, F1, and area under the curve (AUC) of this algorithm are 0.71, 0.87, 0.53, 0.66, and 0.69, respectively. Conclusions: A lead2-based RF classification model may help clinicians to rapidly detect severe dyskalemias as a non-invasive method and prevent life-threatening cardiac conditions due to hyperkalemia.

Emerging role of miR-520a in human diseases.

hsa-miR-520a is derived from MIR520A located at 19q13.42 and has a significant part in the development of various disorders, including different types of cancers, recurrent pregnancy loss, cerebral ischemia/reperfusion injury, and sciatica. In relation to cancer, numerous studies have presented diverse findings regarding the function of this particular miRNA. To summarize, it has been observed to be down-regulated in pancreatic cancer, glioma, ovarian cancer, cervical cancer, uterine corpus endometrial carcinoma, lung cancer, and acute myeloid leukemia. The purpose of this review is to offer an inclusive overview of the role of has-miR-520a in these disorders, with a specific focus on its target mRNAs in each setting and the deregulated signaling pathways involved. Additionally, we aimed to summarize the implication of miR-520a as a prognostic factor in malignancies. Finally, we performed comprehensive in-silico analyses to uncover the biological roles of this miRNA and introducing innovative concepts for future research endeavors.

Artificial intelligence-based model for automatic real-time and noninvasive estimation of blood potassium levels in pediatric patients.

Background: An abnormal variation in blood electrolytes, such as potassium, contributes to mortality in children admitted to intensive care units. Continuous and real-time monitoring of potassium serum levels can prevent fatal arrhythmias, but this is not currently practical. The study aims to use machine learning to estimate blood potassium levels with accuracy in real time noninvasively. Methods: Hospitalized patients in the Pediatric Department of the Rajaie Cardiology and Medical Research Center and Tehran Heart Center were recruited from December 2021 to June 2022. The electrocardiographic (ECG) features of patients were evaluated. We defined 16 features for each signal and extracted them automatically. The dimension reduction operation was performed with the assistance of the correlation matrix. Linear regression, polynomials, decision trees, random forests, and support vector machine algorithms have been used to find the relationship between characteristics and serum potassium levels. Finally, we used a scatter plot and mean square error (MSE) to display the results. Results: Of 463 patients (mean age: 8 ± 1 year; 56% boys) hospitalized, 428 patients met the inclusion criteria, with 35 patients having a high noise of ECG were excluded. After the dimension reduction step, 11 features were selected from each cardiac signal. The random forest regression algorithm showed the best performance with an MSE of 0.3. Conclusion: The accurate estimation of serum potassium levels based on ECG signals is possible using machine learning algorithms. This can be potentially useful in predicting serum potassium levels in specific clinical scenarios.

Should I stay or move? Quantifying landscape of fear to enhance environmental management of road networks in a highly transformed landscape.

The development and expansion of road networks pose considerable threats to natural habitats and wildlife, fostering a landscape of fear. In addition to direct mortality caused by road collisions, road construction and maintenance often result in habitat fragmentation and loss, impeding animal movement and gene flow between populations. Mountain ungulates are already confined to fragmented habitat patches and roads can cause substantial disturbances to their critical ecological processes, such as dispersal and migration. In this study, we employed two key mountain ungulates, the wild goat (Capra aegagrus) and mouflon (Ovis gmelini), as functional models to examine how road networks impact the quantity and connectivity of natural habitats in southwestern Iran, where extensive road construction has led to significant landscape changes. We used the MaxEnt method to predict species distribution, the circuit theory to evaluate habitat connectivity, and the Spatial Road Disturbance Index (SPROADI) to assess road impacts. During the modeling process, we selected eleven important variables and employed a model parametrization strategy to identify the optimal configuration for the MaxEnt model. For SPROADI index we used three sub-indices, including traffic intensity, vicinity impact, and fragmentation grade. We then integrated the results of these analyses to identify areas with the most significant environmental impacts of roads on the coherency of the natural habitats. The findings indicate that suitable habitats for wild goats are widely distributed across the study area, while suitable habitats for mouflon are primarily concentrated in the northeastern region. Conservation gap analysis revealed that only 8% of wild goat habitats and 7% of mouflon habitats are covered by protected areas (PAs). The SPROADI map highlighted that 23% of the study area is negatively influenced by road networks. Moreover, 30.4% of highest-probability corridors for mouflon, and 25.7% for wild goat, were highly vulnerable to the impacts of roads. Our combined approach enabled us to quantitatively assess species-specific vulnerability to the impacts of heavy road networks. This study emphasizes the urgent need to address the negative effects of road networks on wildlife habitats and connectivity corridors. Our approach effectively identifies sensitive areas, which can help inform mitigation strategies and support more effective conservation planning in significantly transformed landscapes.

Comprehensive analysis of lncRNA-associated ceRNA network reveals novel potential prognostic regulatory axes in glioblastoma multiforme.

Deciphering the lncRNA-associated competitive endogenous RNA (ceRNA) network is essential in decoding glioblastoma multiforme (GBM) pathogenesis by regulating miRNA availability and controlling mRNA stability. This study aimed to explore novel biomarkers for GBM by constructing a lncRNA-miRNA-mRNA network. A ceRNA network in GBM was constructed using lncRNA, mRNA and miRNA expression profiles from the TCGA and GEO datasets. Seed nodes were identified by protein-protein interaction (PPI) network analysis of deregulated-mRNAs (DEmRNAs) in the ceRNA network. A lncRNA-miRNA-seed network was constructed by mapping the seed nodes into the preliminary ceRNA network. The impact of the seed nodes on the overall survival (OS) of patients was assessed by the GSCA database. Functional enrichment analysis of the deregulated-lncRNAs (DElncRNA) in the ceRNA network and genes interacting with OS-related genes in the PPI network were performed. Finally, the positive correlation between seed nodes and their associated lncRNAs and the expression level of these molecules in GBM tissue compared with normal samples was validated using the GEPIA database. Our analyzes revealed that three novel regulatory axes AL161785.1/miR-139-5p/MS4A6A, LINC02611/miR-139-5p/MS4A6A and PCED1B-AS1/miR-433-3p/MS4A6A may play essential roles in GBM pathogenesis. MS4A6A is upregulated in GBM and closely associated with shorter survival time of patients. We also identified that MS4A6A expression positively correlates with genes related to tumour-associated macrophages, which induce macrophage infiltration and immune suppression. The functional enrichment analysis demonstrated that DElncRNAs are mainly involved in neuroactive ligand-receptor interaction, calcium/MAPK signalling pathway, ribosome, GABAergic/Serotonergic/Glutamatergic synapse and immune system process. In addition, genes related to MS4A6A contribute to immune and inflammatory-related biological processes. Our findings provide novel insights to understand the ceRNA regulation in GBM and identify novel prognostic biomarkers or therapeutic targets.

HLF is a promising prognostic, immunological, and therapeutic biomarker in human tumors.

Despite past research linking HLF mutations to cancer development, no pan-cancer analyses of HLF have been published. As a result, we utilized multiple databases to illustrate the potential roles of HLF in diverse types of cancers. Several databases were used to assess HLF expression in the TCGA cancer samples. Additional assessments were undertaken to investigate the relationship between HLF and overall survival, immune cell infiltration, genetic alterations, promoter methylation, and protein-protein interaction. HLF's putative roles and the relationship between HLF expression and drug reactivity were investigated. HLF expression was shown to be lower in tumor tissues from a variety of malignancies when compared to normal tissues. There was a substantial link found between HLF expression and patient survival, genetic mutations, and immunological infiltration. HLF influenced the pathways of apoptosis, cell cycle, EMT, and PI3K/AKT signaling. Abnormal expression of HLF lowered sensitivity to numerous anti-tumor drugs and small compounds. According to our findings, reduced HLF expression drives cancer growth, and it has the potential to be identified as a vital biomarker for use in prognosis, immunotherapy, and targeted treatment of a range of malignancies.

A deeply supervised adaptable neural network for diagnosis and classification of Alzheimer's severity using multitask feature extraction.

Alzheimer's disease is the most prevalent form of dementia, which is a gradual condition that begins with mild memory loss and progresses to difficulties communicating and responding to the environment. Recent advancements in neuroimaging techniques have resulted in large-scale multimodal neuroimaging data, leading to an increased interest in using deep learning for the early diagnosis and automated classification of Alzheimer's disease. This study uses machine learning (ML) methods to determine the severity level of Alzheimer's disease using MRI images, where the dataset consists of four levels of severity. A hybrid of 12 feature extraction methods is used to diagnose Alzheimer's disease severity, and six traditional machine learning methods are applied, including decision tree, K-nearest neighbor, linear discrimination analysis, Naïve Bayes, support vector machine, and ensemble learning methods. During training, optimization is performed to obtain the best solution for each classifier. Additionally, a CNN model is trained using a machine learning system algorithm to identify specific patterns. The accuracy of the Naïve Bayes, Support Vector Machines, K-nearest neighbor, Linear discrimination classifier, Decision tree, Ensembled learning, and presented CNN architecture are 67.5%, 72.3%, 74.5%, 65.6%, 62.4%, 73.8% and, 95.3%, respectively. Based on the results, the presented CNN approach outperforms other traditional machine learning methods to find Alzheimer severity.

Role of circular RNA/miRNA axes in the pathophysiology of polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is a disorder resulted from interactions between genetic and environmental factors. Based on the importance of epigenetic factors in the pathoetiology of PCOS, the current review focused on identification of circular RNAs (circRNAs) that are involved in PCOS through acting as molecular sponges for microRNAs (miRNAs). The literature search led to identification of circ_0043533/miR-1179, circ_0030018/miR-136, circ_FURIN/miR-423-5p, circ-FURIN/miR-195-5p, circ_0043532/miR-182, circ_RANBP9/miR-136-5p, circRHBG/miR-515-5p, circMTO1/miR-320b, circASPH/miR-375, circPSMC3/miR-296-3p, circLDLR/miR-1294, circPUM1/miR-760, and hsa_circ_0118530/miR-136 as molecular axes contributing to the pathogenesis of PCOS. To set the stage for future research on the role of the ceRNA network in PCOS, in-silico analyses were performed using miRWalk, miRNet, and miRDIP databases. miRWalk identified 80 genes regulated by 5 miRNAs, miRNet revealed 6449 circRNAs potentially controlling 11 miRNAs, and miRDIP identified 11 miRNAs associated with 35 human pathways. These targets can be used in the treatment options, design of personalized medicine and prediction of prognosis of PCOS.

Package delivered: folate receptor-mediated transporters in cancer therapy and diagnosis.

Neoplasias pose a significant threat to aging society, underscoring the urgent need to overcome the limitations of traditional chemotherapy through pioneering strategies. Targeted drug delivery is an evolving frontier in cancer therapy, aiming to enhance treatment efficacy while mitigating undesirable side effects. One promising avenue utilizes cell membrane receptors like the folate receptor to guide drug transporters precisely to malignant cells. Based on the cellular folate receptor as a cancer cell hallmark, targeted nanocarriers and small molecule-drug conjugates have been developed that comprise different (bio) chemistries and/or mechanical properties with individual advantages and challenges. Such modern folic acid-conjugated stimuli-responsive drug transporters provide systemic drug delivery and controlled release, enabling reduced dosages, circumvention of drug resistance, and diminished adverse effects. Since the drug transporters' structure-based de novo design is increasingly relevant for precision cancer remediation and diagnosis, this review seeks to collect and debate the recent approaches to deliver therapeutics or diagnostics based on folic acid conjugated Trojan Horses and to facilitate the understanding of the relevant chemistry and biochemical pathways. Focusing exemplarily on brain and breast cancer, recent advances spanning 2017 to 2023 in conjugated nanocarriers and small molecule drug conjugates were considered, evaluating the chemical and biological aspects in order to improve accessibility to the field and to bridge chemical and biomedical points of view ultimately guiding future research in FR-targeted cancer therapy and diagnosis.

"Hyperglycemic Memory": Observational Evidence to Experimental Inference.

Several epidemiological studies have appreciated the impact of "duration" and "level" of hyperglycemia on the initiation and development of chronic complications of diabetes. However, glycemic profiles could not fully explain the presence/absence and severity of diabetic complications. Genetic issues and concepts of "hyperglycemic memory" have been introduced as additional influential factors involved in the pathobiology of late complications of diabetes. In the extended phase of significant diabetes randomized, controlled clinical trials, including DCCT/EDIC and UKPDS, studies have concluded that the quality of glycemic or metabolic control at the early time around the diabetes onset could maintain its protective or detrimental impact throughout the following diabetes course. There is no reliable indication of the mechanism by which the transient exposure to a given glucose concentration level could evoke a consistent cellular response at target tissues at the molecular levels. Some biological phenomena, such as the production and the concentration of advanced glycation end products (AGEs), reactive oxygen species (ROS) and protein kinase C (PKC) pathway activations, epigenetic changes, and finally, the miRNAs-mediated pathways, may be accountable for the development of hyperglycemic memory. This work summarizes evidence from previous experiments that may substantiate the hyperglycemic memory soundness by its justification in molecular terms.

Carcinogenic roles of MAFG-AS1 in human cancers

The MAF bZIP transcription factor G-antisense RNA 1 (MAFG-AS1) is located on chromosome 17. MAFG-AS1 was upregulated in 15 human cancers. MAFG-AS1 not only suppresses 16 miRNAs but also directly impacts 22 protein-coding genes' expression. Notably, abnormal MAFG-AS1 expression is connected to clinicopathological characteristics and a worse prognosis in a variety of cancers. Moreover, MAFG-AS1 takes its part in the tumorigenesis and progression of various human malignancies by suppressing apoptosis and promoting proliferation, migration, invasion, aerobic glycolysis, ferroptosis, angiogenesis, EMT, and metastasis. Besides, it can predict treatment effectiveness in ER + breast cancer, urothelial bladder carcinoma, and liver cancer by functioning as a trigger of resistance to tamoxifen, sorafenib, and cisplatin. This study systematically presents the functions of MAFG-AS1 in various cancers, as well as the findings of bioinformatics analyses of the MAFG-AS1, which should give clear advice for future research (AU)

The optimal fluoroscopic views to rule out intra-articular screw penetration during acetabular fracture fixation.

PURPOSE: To determine the ideal view(s) and the minimum number of intraoperative fluoroscopic views required to rule out any intra-articular screw violation in acetabular fractures fixation. METHODS: This study was conducted using a series of fluoroscopic examinations of pelvic synthetic models with screws positioned in different planes around the acetabulum. Ten screws were placed in the synthetic pelvis models in different planes of the acetabulum. Seven views were taken for each screw. Radiographic images were evaluated by 14 orthopaedic surgeons who were asked to assess joint violation and the view(s) required for assessment. RESULTS: The observers' accuracy rate in identifying joint violation was 82.1% for the anterior part of the anterior column and the superior part of the posterior column, 89.3% for the posterior part of the anterior column and the inferior part of the posterior column, and 92.9% for the quadrilateral plate. The sensitivity was 100% for the anterior and posterior parts of the anterior column and the inferior part of the posterior column, 87.5% for the superior part of the posterior column, and 85.7% for the quadrilateral plate. The specificity was 100% for the quadrilateral plate, 80% for the superior part of the posterior column and the posterior part of the anterior column, 78.6% for the inferior part of the posterior column, and 66.7% for the anterior part of the anterior column. There was a strong overall interobserver and intra-observer agreement with intraclass correlation coefficient (ICC) of 0.709 and 0.86, respectively. CONCLUSIONS: This study confirms the hypothesis that in a concave surface/joint fixation, such as the acetabulum, the probability of joint violation is unlikely if there is no evidence of it within a single fluoroscopic view. In acetabulum fracture fixation with a screw violating the joint, the screw's presence was evident within the joint space in all fluoroscopic views. However, the absence of joint violation in one fluoroscopic view was adequate to rule out joint penetration.

High-permittivity Solvents Increase MXene Stability and Stacking Order Enabling Ultraefficient Terahertz Shielding.

2D transition metal carbides and nitrides (MXenes) suggest an uncommonly broad combination of important functionalities amongst 2D materials. Nevertheless, MXene suffers from facile oxidation and colloidal instability upon conventional water-based processing, thus limiting applicability. By experiments and theory, It is suggested that for stability and dispersibility, it is critical to select uncommonly high permittivity solvents such as N-methylformamide (NMF) and formamide (FA) (εr  = 171, 109), unlike the classical solvents characterized by high dipole moment and polarity index. They also allow high MXene stacking order within thin films on carbon nanotube (CNT) substrates, showing very high Terahertz (THz) shielding effectiveness (SE) of 40-60 dB at 0.3-1.6 THz in spite of the film thinness < 2 µm. The stacking order and mesoscopic porosity turn relevant for THz-shielding as characterized by small-angle X-ray scattering (SAXS). The mechanistic understanding of stability and structural order allows guidance for generic MXene applications, in particular in telecommunication, and more generally processing of 2D materials.

Carcinogenic roles of MAFG-AS1 in human cancers.

The MAF bZIP transcription factor G-antisense RNA 1 (MAFG-AS1) is located on chromosome 17. MAFG-AS1 was upregulated in 15 human cancers. MAFG-AS1 not only suppresses 16 miRNAs but also directly impacts 22 protein-coding genes' expression. Notably, abnormal MAFG-AS1 expression is connected to clinicopathological characteristics and a worse prognosis in a variety of cancers. Moreover, MAFG-AS1 takes its part in the tumorigenesis and progression of various human malignancies by suppressing apoptosis and promoting proliferation, migration, invasion, aerobic glycolysis, ferroptosis, angiogenesis, EMT, and metastasis. Besides, it can predict treatment effectiveness in ER + breast cancer, urothelial bladder carcinoma, and liver cancer by functioning as a trigger of resistance to tamoxifen, sorafenib, and cisplatin. This study systematically presents the functions of MAFG-AS1 in various cancers, as well as the findings of bioinformatics analyses of the MAFG-AS1, which should give clear advice for future research.

An Update on the Application of CRISPR Technology in Clinical Practice.

The CRISPR/Cas system, an innovative gene-editing tool, is emerging as a promising technique for genome modifications. This straightforward technique was created based on the prokaryotic adaptive immune defense mechanism and employed in the studies on human diseases that proved enormous therapeutic potential. A genetically unique patient mutation in the process of gene therapy can be corrected by the CRISPR method to treat diseases that traditional methods were unable to cure. However, introduction of CRISPR/Cas9 into the clinic will be challenging because we still need to improve the technology's effectiveness, precision, and applications. In this review, we first describe the function and applications of the CRISPR-Cas9 system. We next delineate how this technology could be utilized for gene therapy of various human disorders, including cancer and infectious diseases and highlight the promising examples in the field. Finally, we document current challenges and the potential solutions to overcome these obstacles for the effective use of CRISPR-Cas9 in clinical practice.

Circular RNA_0000285: A novel double-edged sword circular RNA in human malignancies.

Circular RNAs (circRNAs) are a class of RNA molecules that are characterized by their covalently closed structure, which is formed through a process of back splicing of the precursor mRNA. Abnormal expression of circRNAs has been shown to indirectly affect their interaction with microRNAs (miRNAs), thereby modulating gene transcription. One such circRNA, circ_0000285, is known to be dysregulated in various cancers and human diseases. This circRNA is derived from the HIPK3 gene on chromosome 11 and acts as a competing endogenous RNA for several miRNAs, including miR-654-3p, miR-197-3p, miR-1278, miR-582-3p, and miR-599. Notably, circ_0000285 has been linked to poor overall survival and several clinicopathological features in multiple human cancers. In this review, we present a comprehensive summary of the oncogenic effect of circ_0000285 in various cancers, drawing on experiment performed on cell lines, animals, and human tissues. Furthermore, we predicted potential miRNAs and RNA-binding proteins that may interact with circ_0000285, thereby providing new insights for further studies.

The hsa-miR-3613-5p, a potential oncogene correlated with diagnostic and prognostic merits in kidney renal clear cell carcinoma.

MicroRNA-3613 (hsa-miR-3613-5p), a biomarker with a dual role as an oncogenic or tumor suppressor, is associated with different types of cancer. This study aimed to determine the correlation between the hsa-miR-3613-5p gene expression and Kidney renal clear cell carcinoma (KIRC). Utilizing several bioinformatics tools, we examined the expression level and clinicopathological value of hsa-miR-3613-5p in patients with KIRC compared to normal tissues. Other bioinformatic measures, including survival analysis, diagnostic merit of hsa-miR-3613-5p, downstream target prediction, potential upstream lncRNAs, network construction, and functional enrichment analysis of hsa-miR-3613-5p, were performed. We observed that overexpression of hsa-miR-3613-5p in KIRC tissues had valuable diagnostic merit and was significantly correlated with the poor overall survival of KIRC patients. We also realized a correlation between abnormal expression of hsa-miR-3613-5p and several clinical parameters such as pathological stage, race, age, and histological grades in patients with KIRC. Moreover, we constructed the most potential regulatory network of hsa-miR-3613-5p in KIRC with 17 different axes, including four pseudogenes, two lncRNAs, and three mRNAs. Besides, we uncovered six variants in the mature form of hsa-miR-3613-5p. Finally, pathway enrichment analysis demonstrated that the top-ranked pathways for hsa-miR-3613-5p are cell cycle, cell adhesion molecules (CAMs), and hepatocellular carcinoma pathways. The present report suggests that the higher expression of hsa-miR-3613-5p is associated with the progression of KIRC. Therefore, it may be considered a valuable indicator for the early detection, risk stratification, and targeted treatment of patients with KIRC.

Phytochemicals, Essential Oils Composition and Antioxidant Activity of Astragalus spp., Phlomis olivieri and Daphne mucronata in Habitats of Central Iran.

This study evaluated several secondary metabolites, essential oils (EOs) compositions, and antioxidant activity in four medicinal plants that originated in Isfahan rangelands. The species were Astragalus verus, Astragalus adscendens, Daphne mucronata, and Phlomis olivieri. Thirty-two genotypes of these species were evaluated for different biochemical traits. Based on the evaluation of EOs compounds, GC/MS analysis revealed the total number of identified compounds. These compounds were 25, 22, 12, and 22 for A. adscendens, A. verus, D. mucronata, and P. olivieri, respectively. The dominant compounds were phthalate (59.88 %) in A. adscendens, phytol (38.02 %) in A. verus, hexanoic acid (32.05 %) in D. mucronata and ß-cubebene (30.94 %) in P. olivieri. Phytochemical analysis showed that D. mucronata, A. adscendens, and P. olivieri had the highest total phenolics content (TPC) (18.24 mg gallic acid equivalent/g dry weight), total flavonoids content (5.57 mg QE/g DW), and total anthocyanins content (0.23 mg/g DW), respectively. The highest total chlorophyll (0.27 mg/g DW), total carotenoids (0.03 mg/g DW), and antioxidant activity (71.36 %) were observed in A. adscendens, A. adscendens and A. verus, respectively. Among all genotypes, the highest TPC (20.1 mg GAE/g DW) was observed in genotype 5 of D. mucronata. This study provided new information on the chemical compounds within the distribution range of these ecologically dominant rangeland species in Isfahan province, Iran. The data revealed that superior genotypes from these species are rich in natural antioxidants and bioactive compounds. Thus, they can be used in ethno pharmacological fields, food, and industrial applications.

Cold Physical Plasma-Mediated Fenretinide Prodrug Activation Confers Additive Cytotoxicity in Epithelial Cells.

Cold physical plasma is a partially ionized gas operated at body temperature and utilized for heat-sensitive technical and medical purposes. Physical plasma is a multi-component system consisting of, e.g., reactive species, ions and electrons, electric fields, and UV light. Therefore, cold plasma technology is an interesting tool for introducing biomolecule oxidative modifications. This concept can be extended to anticancer drugs, including prodrugs, which could be activated in situ to enhance local anticancer effects. To this end, we performed a proof-of-concept study on the oxidative prodrug activation of a tailor-made boronic pinacol ester fenretinide treated with the atmospheric pressure argon plasma jet kINPen operated with either argon, argon-hydrogen, or argon-oxygen feed gas. Fenretinide release from the prodrug was triggered via Baeyer-Villiger-type oxidation of the boron-carbon bond based on hydrogen peroxide and peroxynitrite, which were generated by plasma processes and chemical addition using mass spectrometry. Fenretinide activation led to additive cytotoxic effects in three epithelial cell lines in vitro compared to the effects of cold plasma treatment alone regarding metabolic activity reduction and an increase in terminal cell death, suggesting that cold physical plasma-mediated prodrug activation is a new direction for combination cancer treatment studies.

MaxEnt brings comparable results when the input data are being completed; Model parameterization of four species distribution models.

Species distribution models (SDMs) are practical tools to assess the habitat suitability of species with numerous applications in environmental management and conservation planning. The manipulation of the input data to deal with their spatial bias is one of the advantageous methods to enhance the performance of SDMs. However, the development of a model parameterization approach covering different SDMs to achieve well-performing models has rarely been implemented. We integrated input data manipulation and model tuning for four commonly-used SDMs: generalized linear model (GLM), gradient boosted model (GBM), random forest (RF), and maximum entropy (MaxEnt), and compared their predictive performance to model geographically imbalanced-biased data of a rare species complex of mountain vipers. Models were tuned up based on a range of model-specific parameters considering two background selection methods: random and background weighting schemes. The performance of the fine-tuned models was assessed based on recently identified localities of the species. The results indicated that although the fine-tuned version of all models shows great performance in predicting training data (AUC > 0.9 and TSS > 0.5), they produce different results in classifying out-of-bag data. The GBM and RF with higher sensitivity of training data showed more different performances. The GLM, despite having high predictive performance for test data, showed lower specificity. It was only the MaxEnt model that showed high predictive performance and comparable results for identifying test data in both random and background weighting procedures. Our results highlight that while GBM and RF are prone to overfitting training data and GLM over-predict nonsampled areas MaxEnt is capable of producing results that are both predictable (extrapolative) and complex (interpolative). We discuss the assumptions of each model and conclude that MaxEnt could be considered as a practical method to cope with imbalanced-biased data in species distribution modeling approaches.