Chaos and extinction risks of sexually reproductive generalist top predator in a seasonally forced food chain system with Allee effect.

This paper investigates the dynamics of a tritrophic food chain model incorporating an Allee effect, sexually reproductive generalist top predators, and Holling type IV and Beddington-DeAngelis functional responses for interactions across different trophic levels. Analytically, we explore the feasible equilibria, their local stability, and various bifurcations, including Hopf, saddle-node, transcritical, and Bogdanov-Takens bifurcations. Numerical findings suggest that higher Allee intensity in prey growth leads to the inability of species coexistence, resulting in a decline in species density. Likewise, a lower reproduction rate and a higher strength of intraspecific competition among top predators also prevent the coexistence of species. Conversely, a rapid increase in the reproduction rate and a decrease in the strength of intraspecific competition among top predators enhance the densities of prey and top predators while decreasing intermediate predator density. We also reveal the presence of bistability and tristability phenomena within the system. Furthermore, we extend our autonomous model to its nonautonomous counterpart by introducing seasonally perturbed parameters. Numerical analysis of the nonautonomous model reveals that higher seasonal strength in the reproduction rate and intraspecific competition of top predators induce chaotic behavior, which is also confirmed by the maximum Lyapunov exponent. Additionally, we observe that seasonality may lead to the extinction of species from the ecosystem. Factors such as the Allee effect and growth rate of prey can cause periodicity in population densities. Understanding these trends is critical for controlling changes in population density within the ecosystem. Ecologists, environmentalists, and policymakers stand to benefit significantly from the invaluable insights garnered from this study. Specifically, our findings offer pivotal guidance for shaping future strategies aimed at safeguarding biodiversity and maintaining ecological stability amidst changing environmental conditions. By contributing to the existing body of knowledge, our study advances the field of ecological science, enhancing the comprehension of predator-prey dynamics across diverse ecological conditions.

Gene expression data classification using topology and machine learning models.

BACKGROUND: Interpretation of high-throughput gene expression data continues to require mathematical tools in data analysis that recognizes the shape of the data in high dimensions. Topological data analysis (TDA) has recently been successful in extracting robust features in several applications dealing with high dimensional constructs. In this work, we utilize some recent developments in TDA to curate gene expression data. Our work differs from the predecessors in two aspects: (1) Traditional TDA pipelines use topological signatures called barcodes to enhance feature vectors which are used for classification. In contrast, this work involves curating relevant features to obtain somewhat better representatives with the help of TDA. This representatives of the entire data facilitates better comprehension of the phenotype labels. (2) Most of the earlier works employ barcodes obtained using topological summaries as fingerprints for the data. Even though they are stable signatures, there exists no direct mapping between the data and said barcodes. RESULTS: The topology relevant curated data that we obtain provides an improvement in shallow learning as well as deep learning based supervised classifications. We further show that the representative cycles we compute have an unsupervised inclination towards phenotype labels. This work thus shows that topological signatures are able to comprehend gene expression levels and classify cohorts accordingly. CONCLUSIONS: In this work, we engender representative persistent cycles to discern the gene expression data. These cycles allow us to directly procure genes entailed in similar processes.

Progression or Aging? A Deep Learning Approach for Distinguishing Glaucoma Progression From Age-Related Changes in OCT Scans.

PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard. DESIGN: Retrospective cohort study. METHODS: Glaucomatous and healthy eyes with ≥5 reliable peripapillary OCT (Spectralis, Heidelberg Engineering) circle scans were included. A weakly supervised time-series learning model, called noise positive-unlabeled (Noise-PU) DL was developed to classify whether sequences of OCT B-scans showed glaucoma progression. The model used 2 learning schemes, one to identify age-related changes by differentiating test sequences from glaucoma vs healthy eyes, and the other to identify test-retest variability based on scrambled OCTs of glaucoma eyes. Both models' bases were convolutional neural networks (CNN) and long short-term memory (LSTM) networks which were combined to form a CNN-LSTM model. Model features were combined and jointly trained to identify glaucoma progression, accounting for age-related loss. The DL model's outcomes were compared with ordinary least squares (OLS) regression of retinal nerve fiber layer (RNFL) thickness over time, matched for specificity. The hit ratio was used as a proxy for sensitivity. RESULTS: Eight thousand seven hundred eighty-five follow-up sequences of 5 consecutive OCT tests from 3253 eyes (1859 subjects) were included in the study. The mean follow-up time was 3.5 ± 1.6 years. In the test sample, the hit ratios of the DL and OLS methods were 0.498 (95%CI: 0.470-0.526) and 0.284 (95%CI: 0.258-0.309) respectively (P < .001) when the specificities were equalized to 95%. CONCLUSION: A DL model was able to identify longitudinal glaucomatous structural changes in OCT B-scans using a surrogate reference standard for progression.

Demographic Characteristics and Treatment Outcomes of Advanced Renal Cell Carcinoma With Clear Cell Histology: A Single-Center Experience From India.

Background Treatment of metastatic renal cell cancer (mRCC) has revolutionized with the introduction of anti-VEGF tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs). There is limited data in the literature on the outcomes of Indian patients treated with TKI. Here, we report the outcome of mRCC treated with first-line TKI in a resource-poor setting. Material and methods This is a single-center retrospective study of clear cell mRCC treated with first-line TKI from June 2012 to December 2022. Demographic characteristics and treatment details, including outcome data, were captured from electronic medical records. Patients who received at least one week of therapy were eligible for survival analysis. Results A total of 345 patients with metastatic clear cell histology were analyzed, with a median age of 61 years (range: 20-84 years). One hundred and eighty patients (52%) underwent nephrectomy before systemic therapy. The majority received pazopanib (257 patients, 75%), followed by sunitinib (36 patients, 10%) and cabozantinib (21 patients, 6%); 145 (45%) patients required dose interruption, and 143 (43%) required dose modification of TKI for adverse events. After a median follow-up of 44 months, the median progression-free survival (PFS) was 20.3 months (95% CI: 17.8-24.8), and the median overall survival (OS) was 22.7 months (95% CI: 18.8-28.3). In the poor-risk International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) group, no prior nephrectomy emerged as an independent poor-risk factor for both PFS and OS in multivariate analysis. Conclusion This is the largest single-center cohort of clear cell mRCC from Asia. Median PFS was 20.3 months with predominantly TKI monotherapy. In the poor-risk IMDC group, no prior nephrectomy emerged as an independent poor-risk factor for both PFS and OS.

Efficacy of cyclin-dependent kinase 4/6 inhibitors in patients with metastatic hormone positive breast cancer: a single institutional study from India.

Purpose: Cyclin-dependent kinase 4/6 (CDK4/6) inhibitors have shown marked benefit in the treatment of hormone positive metastatic breast cancer (HR+ MBC). There are limited real-world studies with palbociclib and ribociclib. Here we report our experience with CDK4/6 inhibitors in these groups of patients. Material and methods: Patients with HR+ MBC who have received either palbociclib or ribociclib during the course of their treatment from January 2017 to January 2022 were included in the study. The baseline demographic features, treatment details and toxicity were recorded. Patients who received at least 1 month of therapy were included in the survival analysis. Results: A total of 144 patients received CDK4/6 inhibitors during the time period. The median age of the population was 53 (30-80) years. Ninety-eight (71.4%) patients presented with de novo metastatic disease. The most common site of metastasis was to the skeleton (74.2%). Most patients (75%) received palbociclib as their therapy. At a median follow-up of 20.2 months, the median progression free survival (PFS) of the whole population was 16.5 (95% confidence interval (95% CI): 11.6-25.5) months and the median overall survival (OS) was 29.7 (95% CI: 21.7-44.6) months. The presence of liver metastases, low progesterone receptor positivity (Allred score < 6) and prior systemic treatment were poor prognostic factors for both PFS and OS in multivariate analysis. Drug was discontinued for only 2.1% of the patient population. Conclusions: Use of CDK4/6 inhibitors has led to improvement in PFS and OS in patients with HR+ MBC and it is well tolerated. The presence of liver metastases and low progesterone receptor positivity (Allred score < 6) and prior treatment are poor prognostic factors.