Biology subjects

Jonnalagadda, A.

Publications and source records attributed to Jonnalagadda, A..

3 recordsLinked to original sources

bioRxiv2026

Model of naturally occurring refractive error (NORE) in mice

PurposeAnimal models of myopia typically induce monocular refractive shifts via form deprivation (FD) or lens-induced myopia (LIM), modeling susceptibility to myopia, but with potentially limited applicability to childhood myopia. Here we describe a novel, genetically diverse mouse model of naturally occurring refractive error (NORE) with three distinct refractive phenotypes: hyperopic, myopic, and intermediate. MethodsC57BL/6J mice were mated to 129S2/SvPasCrl mice to create F1 or F2 offspring. Refractive errors in male and female F1 (N=21) and F2 (N=101) mice were assessed on postnatal days (P) 28 and 42 using photorefractometry. In a subset of mice (N=30-40), corneal radius of curvature, axial ocular dimensions, retinal and visual function were assessed. ResultsF2 mice were classified as NORE with either hyperopic (RE[≥]0 diopters (D) at P28 and P42), myopic (RE<0D at P28 and P42) or intermediate (RE<0D at P28 and RE[≥]0D at P42) refractions based on individual trajectories. All ocular parameters changed with age, with significantly slower growth in axial length and vitreous chamber depth in the intermediate versus myopic mice (p<0.05). Lens thickness was smaller in the myopic group at P28. Differences in refraction were not attributed to variances in retinal function or dopamine signaling. ConclusionsNORE mice represent a novel, genetically diverse wild-type mouse model that, unlike traditional models, does not require interventions such as FD or LIM to induce myopia. NORE mice provide a valuable tool for future investigations of genetic and environmental mechanisms and targeted therapeutic strategies for refractive errors.

genetics↗

bioRxiv2024

Enhancing cellular production of fucoxanthin through machine learning assisted predictive approach in Isochrysis galbana

The marine microalgae, Isochrysis galbana is a prolific producer of fucoxanthin which is a xanthophyll carotenoid with substantial global market value boasting extensive applications in the food, nutraceutical, pharmaceutical, and cosmetic industries. Although supplementation of different phytohormones to medium enhances fucoxanthin production, the quantification of pigment by conventional means is time-consuming and labor-intensive. This study addressed the multiple methodological limitations of HPLC-based fucoxanthin quantification and emphasized the need to develop a Machine Learning (ML) model as optimization and precise prediction remain a challenging task. Hence, an integrated experimental approach coupled with ML models was employed to predict fucoxanthin yield by supplementation of various phytohormones. The accuracy of fucoxanthin prediction excluding and including hormone descriptors was compared and evaluated using the ML models namely Random Forest (RF), Linear Regression (LR), Artificial Neural Network (ANN), and Support Vector Machine (SVM). RF model provided the most accurate prediction excluding hormone descriptors with coefficient of determination (R2 = 0.809) and root-mean-square error (RMSE = 0.776) followed by the ANN model with (R2 = 0.722) and (RMSE = 0.937). The inclusion of hormone descriptors for training and pre-processing of data further improved the fucoxanthin prediction accuracy of the RF model to (R2 = 0.839) and (RMSE = 0.712) and ANN model to (R2 = 0.738) and (RMSE = 0.909). These results indicated that the combination of low-cost, Ultraviolet (UV) spectrometric-based fucoxanthin quantification coupled with ML algorithms can be efficiently used for reliable prediction and enhanced fucoxanthin production, therefore highlighting a promising approach and furnishing invaluable insights towards the commercialization of microalgal fucoxanthin production.

systems biology↗

bioRxiv2022

Concurrent fMRI demonstrates propagation of TMS effects across task-related networks

Transcranial magnetic stimulation (TMS) has become an important technique in both scientific and clinical practices, and yet our understanding of how the brain responds to TMS is still limited. Concurrent neuroimaging during TMS may bridge this gap, and emerging evidence suggests widespread that modulatory effects of TMS may be best captured through changes in functional connectivity between distributed networks, rather than local changes in cortical activity. However, the relationship between TMS stimulation parameters and evoked changes in functional connectivity is unknown. In this study, 24 healthy volunteers received concurrent TMS-fMRI while performing a dot-motion direction discrimination task. An MR-compatible coil was used to apply trains of three pulses at 10 Hz rTMS over the primary visual cortex (V1) at the onset of the dot stimuli with four levels of stimulation intensity (20%, 40%, 80%, and 120% of resting motor threshold, RMT). Behavioral results demonstrated impairment of motion discrimination at 80% RMT. FMRI results yielded three findings. First, functional connectivity between visual and non-visual areas increased as a function of rTMS intensity. Second, connectivity within the visual network was positively associated with motion accuracy, while the connectivity between visual and non-visual regions was negatively associated with motion accuracy. Lastly, we found that reductions in the similarity between functional and structural connectivity associated with increasing TMS intensity were constrained to the visual network. These findings demonstrate spatially dependent nonlinear effects of TMS intensity on brain functional connectivity that proceed beyond the site of stimulation and influence associated behavior.

neuroscience↗