Biology subjects

Mohammadi, R.

Publications and source records attributed to Mohammadi, R..

5 recordsLinked to original sources

bioRxiv2026

Comparing morphological and molecular methods for stream macroinvertebrate biodiversity assessment across seven watersheds in the southern United States

Freshwater biodiversity is declining at alarming rates globally, yet our capacity to monitor it depends heavily on the methods used for biodiversity assessment. Stream benthic macroinvertebrates are widely used as bioindicators, but traditional morphological identification is labor-intensive and limited by taxonomic expertise, while DNA metabarcoding offers a promising but incompletely evaluated alternative. Here, we compared biodiversity patterns detected by four macroinvertebrate sampling and identification methods (morphological identification of benthic samples, morphological identification of combined benthic and stream-edge samples, eDNA metabarcoding of bulk benthic samples, and eDNA metabarcoding of streamwater) across 62 sites in seven watersheds spanning the southern continental United States. We compared methods across three facets of biodiversity (gamma diversity, alpha diversity, and taxonomic composition) and three taxonomic levels (family, genus, and species). We found that which method detected more taxa shifted with taxonomic resolution: morphological methods detected more families and genera, while metabarcoding methods detected more species. Bulk benthic metabarcoding detected more families and genera than water eDNA metabarcoding, consistent with the "watered-down biodiversity" effect of DNA dilution in water samples, but the two metabarcoding methods did not differ significantly in species richness. All four methods detected largely distinct suites of taxa, with metabarcoding methods yielding more unique taxa than morphological methods. Notably, taxa uniquely detected by water eDNA were dominated by soft-bodied organisms (oligochaetes, leeches, earthworms) and water-column-associated taxa, while benthic eDNA uniquely detected hard-bodied EPT insects. We attribute this pattern to fundamental differences in eDNA shedding, transport, and deposition dynamics across organism types. These results demonstrate that no single method captures a complete picture of stream macroinvertebrate biodiversity, and that method choice should be guided by the taxonomic resolution and community components most relevant to the goals of the bioassessment program.

ecology↗

bioRxiv2025

Implementation and Evaluation of Support Vector Machine-Based Models for Cancer Detection Using Multi-Omic Data: A Systematic Review

IntroductionCancer is a major source of mortality and morbidity all over the world that has caused more than 19 million new cases and nearly 10 million deaths in 2020. Although there are so many advances in cancer diagnosis, previous methods such as imaging and serum biomarkers more often lack the necessary sensitivity and specificity, particularly for early-stage detection. However, most of the studies depend on internal validation that increases concerns about the generalizability of these findings. To improve the dependability of SVM applications in clinical fields, the review emphasizes the necessity of external validation and established techniques. Due to all the things mixing AI with omics technology suggests a hopeful way to improve cancer detection, that could end up in better results and more affordable medical treatments. MethodThis systematic review was conducted using the PRISMA2020 principles and registered on The Open Science Framework. A comprehensive search of several databases was conducted, including PubMed/MEDLINE, Scopus, Google Scholar, and Web Of Science. Data was screened using RAYYAN.ai, which uses artificial intelligence methods to help with decision-making and screening. All original English-language studies that employed SVM to build a model for diagnosing a type human malignancy were included. The full text of the articles was extracted, and the quality of the articles and risk of bias were assessed using the PROBAST tool. ResultA total of 104 studies were identified, of which 99 articles have been included after 5 were excluded because full text was unavailable. The studies covered various types of omics, such as proteomics (41 studies), transcriptomics (30 studies), genomics (19 studies), metabolomics (11 studies), epigenomics (4 studies), radiomics (2 studies), immunomics (1 study), and multi-omics (8 studies). 63 studies were internally validated, and 29 were externally validated; however, 2 studies were both internally and externally validated. ConclusionThe review of 99 studies on Support Vector Machine-based models highlights their potential in improving cancer diagnosis. The study emphasizes the importance of proteomics studies in understanding tumor biology and developing effective diagnostic methods. However, concerns about their generalizability and trustworthiness in medical settings persist.

cell biology↗

bioRxiv2025

Early gonadotoxic effects of cyclophosphamide on the prepubertal testis and the feasibility of reducing toxicity through combined antioxidant therapy

One Sentence SummaryEarly gonadotoxic effects of cyclophosphamide (CPA) on prepubertal testes were identified, with molecular damage detectable within 16 hours and histological changes by 32 hours, while antioxidant co-treatment showed potential for protection. CPA induces early molecular damage in prepubertal testes, with protective antioxidant effects observed within the first 16 hours, highlighting the potential for mitigating testicular toxicity without compromising anticancer efficacy. This study demonstrates that CPA-induced testicular toxicity in prepubertal mice occurs early at the transcriptional level, with antioxidants showing protective effects, underscoring the need for further research on safe and effective protective strategies. At high doses, alkylating drugs toxicity leads to testicular atrophy and spermatogonial stem cells depletion, rendering survivors of childhood cancers infertile, as a recognized late effect. We used in-vivo experimental models to identify early gonadotoxic effects induced by cyclophosphamide (CPA) on prepubertal testes and to investigate if a treatment with antioxidants could reduce that toxicity. Effects were estimated by histological evaluation and whole-transcriptome RNA-sequencing (RNA-seq) of sacrificed animals at 8 hours intervals. Sixty pre-pubertal mice were randomly assigned to 1) intraperitoneal treatment with 100 mg/Kg CPA; 2) CPA in addition to antioxidants (AO) L-Carnitine (LC 50mg/kg) and N-acetyl cysteine (NAC 50); 3) AO alone; 4) control receiving saline solution (Ctrl) (N=15 for each group). Three additional animals were used for baseline assessments. On histology, changes were evident at 32 hours post-treatment. However, RNA-seq analyses revealed that the molecular impact of CPA occurred early within the first 16 hours of exposure to the drug, and that protective effects of AO in combined CPA therapy (CPA+AO) could be achieved during that period. This study demonstrates side effects of CPA on testicular tissue in the first 48 hours on transcriptional scale. Our findings indicate that early administration of AO can mitigate CPA-induced testicular damage in-vivo and suggest that protective treatments against prepubertal testis damage could be developed. However, such protective treatment should not interfere with the desired cytotoxic effects on the malignancies, thus further research is needed to evaluate the timing and how safe administration of the protectant could be achieved for medical treatment.

pharmacology and toxicology↗

bioRxiv2023

Experiments in micro-patterned model membranes support the narrow escape theory

The narrow escape theory (NET) predicts the escape time distribution of Brownian particles confined to a domain with reflecting borders except for one small window. Applications include molecular activation events in cell biology and biophysics. Specifically, the mean first passage time [Formula] can be analytically calculated from the size of the domain, the escape window, and the diffusion coefficient of the particles. In this study, we systematically tested the NET in a disc by variation of the escape opening. Our model system consisted of micro-patterned lipid bilayers. For the measurement of [Formula], we imaged diffusing fluorescently-labeled lipids using single-molecule fluorescence microscopy. We overcame the lifetime limitation of fluorescent probes by re-scaling the measured time with the fraction of escaped particles. Experiments were complemented by matching stochastic numerical simulations. To conclude, we confirmed the NET prediction in vitro and in silico for the disc geometry in the limit of small escape openings. Significance StatementIn the biological context of a cell, a multitude of reactions are facilitated by diffusion. It is astonishing how Brownian motion as a cost-efficient but random process is mediating especially fast reactions. The formalism of the narrow escape theory is a tool to determine the average timescale of such processes to be completed (mean first passage time, MFPT) from the reaction space and diffusion coefficient. We present the systematic proof of this formalism experimentally in a bio-mimetic model system and by random walk simulations. Further, we demonstrate a straightforward solution to determine the MFPT from incomplete experimental traces. This will be beneficial for measurements of the MFPT, reliant on fluorescent probes, that have prior been inaccessible.

biophysics↗

bioRxiv2019

A Neural Network Based Algorithm for Dynamically Adjusting Activity Targets to Sustain Exercise Engagement Among People Using Activity Trackers

It is well established that lack of physical activity is detrimental to overall health of an individual. Modern day activity trackers enable individuals to monitor their daily activity to meet and maintain targets and to promote activity encouraging behavior. However, the benefits of activity trackers are attenuated over time due to waning adherence. One of the key methods to improve adherence to goals is to motivate individuals to improve on their historic performance metrics. In this work we developed a machine learning model to dynamically adjust the activity target for the forthcoming week that can be realistically achieved by the activity-tracker users. This model prescribes activity target for the forthcoming week. We considered individual user-specific personal, social, and environmental factors, daily step count through the current week (7 days). In addition, we computed an entropy measure that characterizes the pattern of daily step count for the current week. Data for training the machine learning model was collected from 30 participants over a duration of 9 weeks. The model predicted target daily count with mean absolute error of 1545 steps. The proposed work can be used to set personalized goals in accordance with the individuals level of activity and thereby improving adherence to fitness tracker.

bioinformatics↗