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Abstract Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

Abstract Different species of aphids, responsible for severe yield losses of cereal crops including wheat, (Triticum aestivum L.) are managed by insecticides, which are harmful to organisms and the environment under field conditions. Therefore, an environment friendly aphidicidal product of plant origin is required. Mentha spicata oil was found to be rich in carvone (81.88%), but the use of its oil and carvone in crop protection is lacking due to their volatility, poor solubility, and stability. A nanoformulaton not only solves these problems but also improve the efficacy and dose of the bioactive compounds. Thus, nano-emulsions of the oil and carvone prepared were characterized, and evaluated against Rhopalosiphum maidis (corn aphid) and Sitobion avenae (wheat aphid) The average droplet size of nano-emulsions of the oil and carvone was found to be 22.1 and 41.21 nm. Nano-emulsion of carvone exhibited higher aphid mortality (LC50 = 0.87–1.94 mg/mL) at 24 h and acetylcholinesterase inhibitory activity (IC50 = 0.07–3.83 mg/mL) compared to the nano-emulsion of the oil (LC50 = 2.87–2.81 mg/mL; IC50 = 1.66–5.34 mg/mL). The repellence index (RI) in nano-emulsion of essential oil was found to be higher (84.73 and 81.72%) at the highest concentration (0.05 µL/cm2) than that of carvone (77.59 and 80.98%) for R. maidis and S. avenae. Further, in silico studies also revealed the favourable binding energy (− 6.6 to − 8.5 kcal/mol) of the main compounds in the oil with acetylcholinesterase, facilitated by hydrophobic interactions and hydrogen bonding. This study suggests that the nano-emulsions of the essential oil and carvone can be explored under field conditions to establish efficacy for their utilization as aphidicidal and repellent products against aphids. In the present study, aphidicial and repellent activities of its essential oil and carvone were reported for the first time against R.maidis and S.avenae.

Abstract Calcium is involved in vision processes in the retina and implicated in various pathologies, including glaucoma. Rod cells rely on store-operated calcium entry (SOCE) to safeguard against the prolonged lowering of intracellular calcium ion concentrations. Zebrafish that lacked the endoplasmic reticulum Ca2+ sensor Stim2 (stim2 knockout [KO]) exhibited impaired vision and lower light perception-related gene expression. We sought to understand mechanisms that are responsible for vision impairment in stim2 KO zebrafish. The single-cell RNA (scRNA) sequencing of neuronal cells from brains of 5 days postfertilization larvae distinguished 27 cell clusters, 10 of which exhibited distinct gene expression patterns, including amacrine and γ-aminobutyric acid (GABA)ergic retinal interneurons and GABAergic optic tectum cells. Five clusters exhibited significant changes in cell proportions between stim2 KO and controls, including GABAergic diencephalon and optic tectum cells. Transmission electron microscopy of stim2 KO zebrafish revealed decreases in width of the inner plexiform layer, ganglion cells, and their dendrites numbers (a hallmark of glaucoma). GABAergic neuron densities in the inner nuclear layer, including amacrine cells, as well as photoreceptors significantly decreased in stim2 KO zebrafish. Our study suggests a novel role for Stim2 in the regulation of neuronal insulin expression and GABAergic-dependent vision causing glaucoma-like retinal pathology.

Abstract To be able to resolve multiple organelles at high throughput is an incredible achievement. This will have immediate implications in a range of fields ranging from fundamental cell biology to translational medicine. To realize such a high-throughput multicolor interrogation modality, we have developed a light-sheet based flow imaging system that is capable of visualizing multiple sub-cellular components with organelle-level resolution. This is possible due to the unique optical design that combines an illumination system comprising two collinear light sheets illuminating the flowing cells and a dedicated dual-color 4f-detection, enabling simultaneous recording of multiple organelles. The system PSF sections up to 4 parallel microfluidic channels through which cells are flowing, and multicolor images of cell cross-sections are recorded. The data is then computationally processed (filtered using ML algorithm, shift-corrected, and merged) and combined to reconstruct the 3D multicolor volume. System testing is conducted using multicolor fluorescent nano-beads (size $$\sim 175$$ ∼ 175 nm) and flow-based imaging parameters (PSF size, motion-blur, flow rate, frame rate, and number of cell-sections) are determined for quality imaging. Drug treatment studies were carried out for healthy and cancerous HeLa cells to check the performance of the proposed system. The cells were treated with a drug (Vincristine, which is known to promote mitochondrial fission in cells), and the same is compared with untreated control cells. The proposed multicolor iLIFE system could screen $$\sim 800$$ ∼ 800 cells/min (at a flow speed of $$2490 ~\upmu$$ 2490 μ m/s), and the drug treatment studies were carried out up to 24 h. Studies showed the disintegration of mitochondrial network and dysfunctional lysosomes and their accumulation at the cell membrane, which is a clear indication of cell apoptosis. Compared to control cells (untreated), the mortality is highest at a concentration of 500 nM post 12 h of drug treatment. With the capability of multiorganelle interrogation and organelle-level resolution, the multicolor iLIFE cytometry system is suitably placed to assist optical imaging and biomedical research.

Abstract Monel K-500 is a high-performance superalloy composed of nickel and copper, renowned for its exceptional strength, hardness, and resistance to corrosion. To machine this material more precisely and accurately, Electrical Discharge Machining (EDM) is one of the best choices. In EDM, material removal rate (MRR) and electrode wear rate (EWR) are crucial performance parameters that are often conflicting in nature. These parameters depend on several input variables, including peak current (Ip), pulse on time (Ton), duty cycle (Tau), and servo voltage (SV). Optimizing the EDM process is essential for enhancing performance. In this research, a set of experiments were conducted using EDM on Monel K500 alloy to determine the optimal process parameters. The Box–Behnken design was used to prepare the experimental design matrix. Utilizing the experimental data, a second-order mathematical model was developed using Response Surface Methodology (RSM). R2 value is found to be 99.40% and 96.60% for MRR and EWR RSM-based prediction model, respectively. High value of R2 is indicated is indicated good adequacy for prediction. The mathematical model further used in multi-objective dragonfly algorithm (MODA): a new meta-heuristic optimization technique to solve multi-objective optimization problem of EDM. The MODA is a very useful technique to achieve optimal solutions from the multi decision criteria. Utilizing this technique, a set of non-dominated solutions was obtained. Further, the TOPSIS method was used to determine the most desirable optimal solution, which was found to be 0.0135 mm3/min for EWR and 6.968 mm3/min for MRR. These results were obtained when the optimal process parameters were selected as Ip = 6 A, Ton = 200 µs, Tau = 12, and SV = 41.6 V. Operators can machine Monel K500 by selecting the above-mentioned optimal parameters to achieve the best performance.

Abstract Rare earth metals (REMs), such as Dysprosium (Dy) and Ytterbium (Yb), have experienced unprecedented demand in recent times due to their applications in high-end technologies. REMs are found only in select geographic locations placing tremendous economic constraints on their use. In this work, we have developed Gum Acacia-grafted hydrogels (GmAc-FluoroTerPs) that are capable of selective detection and capture of Dy and Yb. The intrinsically blue fluorescent polymer hydrogel GmAc-FluoroTerP has been optimized for Dy(III) and Yb(III) specific quenching, enabling limit of detection of the REMs at 0.13 nM and 60.8 pM, respectively. A comprehensive structural characterization of the fluorescent hydrogel has been performed via NMR, FTIR, XPS, EPR, TGA, XRD, TEM, SEM, EDX, TCSPC, and DLS. In addition to an in situ generated fluorophore, GmAc-FluoroTerP displays a distinctive aggregation induced emission enhancement in mixed solvents. The complexation of Dy(III)/Yb(III) with GmAc-FluoroTerP hydrogel has been characterized by XPS, TCSPC, and logic gate analyses, and the adsorptive capacity for Dy(III) and Yb(III) are found to be best reported till date as 125.57 mg g−1 and 102.27 mg g−1, respectively. Desorption at acidic pH allows recovery of the REMs. We also report semiconducting behaviour of the native fluorescent hydrogel, that is enhanced upon adsorptive capture of Dy(III) and Yb(III), with calculated band gaps at 1.37, 0.77, and 0.49 eV, respectively. The convergent sensing, capture, and reuse of Dy(III) and Yb(III) presented in this work promises a hitherto unreported template for application on other REMs.

Abstract Older adults residing in refugee settlements with unhealthy living environments, inadequate access to health care services, and limited psychosocial support are vulnerable to experience mental health problems jeopardizing their mental well-being. The present study aims to explore the mental well-being status and its socio-economic determinants among the older adults living in the Rohingya refugee camp in Bangladesh. This cross-sectional study was conducted among adults aged ≥ 60 residing in five sub-camps within the Rohingya refugee camp of Cox’s Bazar, Bangladesh. Data were collected through face-to-face interviews conducted between November and December 2021. The 14-item Warwick-Edinburgh Mental Well-being Scale was used to assess mental well-being. A cumulated score was derived using the scale ranging from 14 to 70, with higher scores indicating greater levels of mental well-being. A generalized linear regression model was used to examine the socio-economic factors associated with the mental well-being of older adults. A total of 864 older adults participated in the study having a mean mental well-being score of 45.4. Regression analysis revealed that the difference in the logs of mental well-being score was expected to be significantly lower among participants aged 70–79 years (β: − 1.661; 95% CI: − 2.750 to − 0.572; p = 0.003), aged ≥ 80 years (β: − 3.198; 95% CI: − 5.114 to − 1.282; p = 0.001), and those with any non-communicable chronic conditions (β: − 2.903; 95% CI: − 3.833 to − 1.974; p

Abstract Senescent cells have been linked to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). However, the effectiveness of senolytic drugs in reducing liver damage in mice with MASLD is not clear. Additionally, MASLD has been reported to adversely affect male reproductive function. Therefore, this study aimed to evaluate the protective effect of senolytic drugs on liver damage and fertility in male mice with MASLD. Three-month-old male mice were fed a standard diet (SD) or a choline-deficient western diet (WD) until 9 months of age. At 6 months of age mice were randomized within dietary treatment groups into senolytic (dasatinib + quercetin [D + Q]; fisetin [FIS]) or vehicle control treatment groups. We found that mice fed choline-deficient WD had liver damage characteristic of MASLD, with increased liver size, triglycerides accumulation, fibrosis, along increased liver cellular senescence and liver and systemic inflammation. Senolytics were not able to reduce liver damage, senescence and systemic inflammation, suggesting limited efficacy in controlling WD-induced liver damage. Sperm quality and fertility remained unchanged in mice developing MASLD or receiving senolytics. Our data suggest that liver damage and senescence in mice developing MASLD is not reversible by the use of senolytics. Additionally, neither MASLD nor senolytics affected fertility in male mice.

Abstract Pancreatic adenocarcinoma is one of the most aggressive and lethal forms of cancer. Chemotherapy is the primary treatment for pancreatic cancer, but resistance to the drugs used remains a major challenge. A genome-wide CRISPR interference and knockout screen in the PANC-1 cell line with the drug nab-paclitaxel has identified a group of spindle assembly checkpoint (SAC) genes that enhance survival in nab-paclitaxel. Knockdown of these SAC genes (BUB1B, BUB3, and TTK) attenuates paclitaxel-induced cell death. Cells treated with the small molecule inhibitors BAY 1217389 or MPI 0479605, targeting the threonine tyrosine kinase (TTK), also enhance survival in paclitaxel. Overexpression of these SAC genes does not affect sensitivity to paclitaxel. These discoveries have helped to elucidate the mechanisms behind paclitaxel cytotoxicity. The outcomes of this investigation may pave the way for a deeper comprehension of the diverse responses of pancreatic cancer to therapies including paclitaxel. Additionally, they could facilitate the formulation of novel treatment approaches for pancreatic cancer.

Abstract The use of novel active ingredients for the functional modification of chitosan nanoformulations has attracted global attention. In this study, chitosan has been functionalized via histidine to craft novel chitosan–histidine nanoformulation (C–H NF) using ionic gelation method. C–H NF exhibited elite physico-biochemical properties, influencing physiological and biochemical dynamics in Tomato. These elite properties include homogenous-sized nanoparticles (314.4 nm), lower PDI (0.218), viscosity (1.43 Cps), higher zeta potential (11.2 mV), nanoparticle concentration/ml (3.53 × 108), conductivity (0.046 mS/cm), encapsulation efficiency (53%), loading capacity (24%) and yield (32.17%). FTIR spectroscopy revealed histidine interaction with C–H NF, while SEM and TEM exposed its porous structure. Application of C–H NF to Tomato seedling and potted plants through seed treatment and foliar spray positively impacts growth parameters, antioxidant-defense enzyme activities, reactive oxygen species (ROS) content, and chlorophyll and nitrogen content. We claim that the histidine-functionalized chitosan nanoformulation enhances physico-biochemical properties, highlighting its potential to elevate biochemical and physiological processes of Tomato plant.

Abstract To date, degraded mangrove ecosystem restoration accomplished worldwide primarily aligns towards rehabilitation with monotypic plantations, while ecological restoration principles are rarely followed in these interventions. However, researchers admit that most of these initiatives' success rate is not appreciable often. An integrative framework of ecological restoration for degraded mangroves where site-specific observations could be scientifically rationalized, with co-located reference pristine mangroves as the target ecosystem to achieve is currently distinctively lacking. Through this experimental scale study, we studied the suitability of site-specific strategies to ecologically restore degraded mangrove patches vis-à-vis the conventional mono-species plantations in a highly vulnerable mangrove ecosystem in Indian Sundarbans. This comprehensive restoration framework was trialed in small discrete degraded mangrove patches spanning ~ 65 ha. Site-specific key restoration components applied are statistically validated through RDA analyses and Bayesian t-tests. 25 quantifiable metrics evaluate the restoration success of a ~ 3 ha degraded mangrove patch with Ridgeline distribution, Kolmogorov–Smirnov (K-S) tests, and Mahalanobis Distance (D2) measure to prove the site’s near-equivalence to pristine reference in multiple ecosystem attributes. This restoration intervention irrevocably establishes the greater potential of this framework in the recovery of ecosystem functions and self-sustenance compared to that of predominant monoculture practices for vulnerable mangroves.

The study was aimed to evaluate the performance of a newly developed spectroscopy-based non-invasive and noncontact device (SAMIRA) for the simultaneous measurement of hemoglobin, bilirubin and oxygen saturation as an alternative to the invasive biochemical method of blood sampling. The accuracy of the device was assessed in 4318 neonates having incidences of either anemia, jaundice, or hypoxia. Transcutaneous bilirubin, hemoglobin and blood saturation values were obtained by the newly developed instrument which was corroborated with the biochemical blood tests by expert clinicians. The instrument is trained using Artificial Neural Network Analysis to increase the acceptability of the data. The artificial intelligence incorporated within the instrument determines the disease condition of the neonate. The Pearson's correlation coefficient, r was found to be 0.987 for hemoglobin estimation and 0.988 for bilirubin and blood gas saturation respectively. The bias and the limits of agreement for the measurement of all the three parameters were within the clinically acceptance limit.

Abstract Chest Radiography is a non-invasive imaging modality for diagnosing and managing chronic lung disorders, encompassing conditions such as pneumonia, tuberculosis, and COVID-19. While it is crucial for disease localization and severity assessment, existing computer-aided diagnosis (CAD) systems primarily focus on classification tasks, often overlooking these aspects. Additionally, prevalent approaches rely on class activation or saliency maps, providing only a rough localization. This research endeavors to address these limitations by proposing a comprehensive multi-stage framework. Initially, the framework identifies relevant lung areas by filtering out extraneous regions. Subsequently, an advanced fuzzy-based ensemble approach is employed to categorize images into specific classes. In the final stage, the framework identifies infected areas and quantifies the extent of infection in COVID-19 cases, assigning severity scores ranging from 0 to 3 based on the infection’s severity. Specifically, COVID-19 images are classified into distinct severity levels, such as mild, moderate, severe, and critical, determined by the modified RALE scoring system. The study utilizes publicly available datasets, surpassing previous state-of-the-art works. Incorporating lung segmentation into the proposed ensemble-based classification approach enhances the overall classification process. This solution can be a valuable alternative for clinicians and radiologists, serving as a secondary reader for chest X-rays, reducing reporting turnaround times, aiding clinical decision-making, and alleviating the workload on hospital staff.

Abstract Common wheat (Triticum aestivum L.) is a major staple food crop, providing a fifth of food calories and proteins to the world’s human population. Despite the impressive growth in global wheat production in recent decades, further increases in grain yield are required to meet future demands. Here we estimated genetic gain and genotype stability for grain yield (GY) and determined the trait associations that contributed uniquely or in combination to increased GY, through a retrospective analysis of top-performing genotypes selected from the elite spring wheat yield trial (ESWYT) evaluated internationally during a 14-year period (2003 to 2016). Fifty-six ESWYT genotypes and four checks were sown under optimally irrigated conditions in three phenotyping trials during three consecutive growing seasons (2018–2019 to 2020–2021) at Norman E. Borlaug Research Station, Ciudad Obregon, Mexico. The mean GY rose from 6.75 (24th ESWYT) to 7.87 t ha−1 (37th ESWYT), representing a cumulative increase of 1.12 t ha−1. The annual genetic gain for GY was estimated at 0.96% (65 kg ha−1 year−1) accompanied by a positive trend in genotype stability over time. The GY progress was mainly associated with increases in biomass (BM), grain filling rate (GFR), total radiation use efficiency (RUE_total), grain weight per spike (GWS), and reduction in days to heading (DTH), which together explained 95.5% of the GY variation. Regression lines over the years showed significant increases of 0.015 kg m−2 year−1 (p

Abstract The Shiga Epidemiological Study of Subclinical Atherosclerosis was conducted in Kusatsu City, Shiga, Japan, from 2006 to 2008. Participants were measured for LDL-p through nuclear magnetic resonance technology. 740 men participated in follow-up and underwent 1.5 T brain magnetic resonance angiography from 2012 to 2015. Participants were categorized as no-ICAS, and ICAS consisted of mild-ICAS (1 to

Abstract The present study was conducted to assess the impact of non-encapsulated, air-dried microencapsulated, and lyophilized microencapsulated probiotics in indigenous cattle calves (Bos indicus). Twenty-four (5–7 days old) indigenous cattle calves were selected and assigned into four groups, with six calves in each as follows: control (CON), fed milk and basal diet alone, and treatment groups supplemented with non-encapsulated (NEC), air-dried microencapsulated (AEC) and lyophilized microencapsulated (LEC) probiotic L. reuteri SW23 at 108 CFU/head/day in skim milk as a carrier provided for 60 days. The animals were divided into four groups, adopting a complete randomized design, and the effects were considered significant at p ≤ 0.05. Probiotics supplementation increased (p

Abstract The present work explores the potential for observing multiple reentrant localization behavior in a double-stranded helical (DSH) system, extending beyond the conventional nearest-neighbor hopping (NNH) interaction. The DSH system is considered to have hopping dimerization in each strand, while also being subjected to a transverse electric field. The inclusion of an electric field serves the dual purpose of inducing quasi-periodic disorder and strand-wise staggered site energies. Two reentrant localization regions are identified: one exhibiting true extended behavior in the thermodynamic limit, while the second region shows quasi-extended characteristics with partial spreading within the helix. The DSH system exhibits three distinct single-particle mobility edges linked to localization transitions present in the system. The analysis in this study involves examining various parameters such as the single-particle energy spectrum, inverse participation ratio, local probability amplitude, and more. Our proposal, combining achievable hopping dimerization and induced correlated disorder, presents a unique opportunity to study phenomenon of reentrant localization, generating significant research interest.

Abstract The prevalence and mobility of smartphones make these a widely used tool for environmental health research. However, their potential for determining aggregated air quality index (AQI) based on PM2.5 concentration in specific locations remains largely unexplored in the existing literature. In this paper, we thoroughly examine the challenges associated with predicting location-specific PM2.5 concentration using images taken with smartphone cameras. The focus of our study is on Dhaka, the capital of Bangladesh, due to its significant air pollution levels and the large population exposed to it. Our research involves the development of a Deep Convolutional Neural Network (DCNN), which we train using over a thousand outdoor images taken and annotated. These photos are captured at various locations in Dhaka, and their labels are based on PM2.5 concentration data obtained from the local US consulate, calculated using the NowCast algorithm. Through supervised learning, our model establishes a correlation index during training, enhancing its ability to function as a Picture-based Predictor of PM2.5 Concentration (PPPC). This enables the algorithm to calculate an equivalent daily averaged AQI index from a smartphone image. Unlike, popular overly parameterized models, our model shows resource efficiency since it uses fewer parameters. Furthermore, test results indicate that our model outperforms popular models like ViT and INN, as well as popular CNN-based models such as VGG19, ResNet50, and MobileNetV2, in predicting location-specific PM2.5 concentration. Our dataset is the first publicly available collection that includes atmospheric images and corresponding PM2.5 measurements from Dhaka. Our codes and dataset are available at https://github.com/lepotatoguy/aqi .

Abstract This study explored the influence of climatic oscillations on the striped, blue, and silver marlin catch rates in the Indian Ocean by using logbook data from Taiwanese large-scale fishing vessels and climate records from 1994 to 2016. Only the Madden–Julian oscillation (MJO) and the subtropical Indian Ocean dipole (SIOD) had immediate effects on the striped and silver marlin catch rates. The positive and negative phases of the IOD at the lags of 7 and 3 years corresponded to increased and decreased catch rates, respectively, for both the striped and blue marlin, contrasting to the reverse pattern for the silver marlin. Similarly, all three marlin species experienced decreased and increased catch rates respectively during the positive and negative phases of the Pacific decadal oscillation. The striped and blue marlin catch rates decreased and increased during the positive and negative phases, respectively, of the SIOD and MJO with various lags. Our results suggest that the impacts of climatic oscillations on fish species are crucial for policymakers and coastal communities for managing marine resources, forecasting changes in marine ecosystems, and developing strategies to adapt to and mitigate the effects of climate variability.

Abstract Having powerful antibacterial and antioxidant effects, zinc oxide and manganese oxide nanomaterials are of great interest. Here we have synthesized manganese oxide decorated zinc oxide (MZO) nanocomposites by co-precipitation method, calcined at different temperatures (300–750 °C) and studied various properties. Here the crystalline structure of the nanocomposite and phase change of the manganese oxide are observed with calcination temperature. The average crystalline size increases and the dislocation density and microstrain decrease with the increase in calcined temperature for the same structural features. The formation of composites was confirmed by XRD pattern and SEM images. EDAX spectra proved the high purity of the composites. Here, different biological properties change with the calcination temperature for different shapes, sizes and structures of the nanocomposite. Nanomaterial calcined at 750 °C provides the best anti-microbial activity against Escherichia coli, Salmonella typhimurium, Shigella flexneri (gram-negative), Bacillus subtilis and Bacillus megaterium (gram-positive) bacterial strain at 300 µg/mL concentration. The nanomaterial with calcination temperatures of 300 °C and 450 °C provided better antioxidant properties.

Abstract This study deals with the characterization of a natural fiber which is extracted from the stem of an unexplored plant of Cyperus platystylis R.Br. (CPS) with an aim to establish it as a potent alternative fiber for the plant fiber-based industries. CPS fiber has been investigated for its physical, chemical, thermal, mechanical, and morphological characteristics. The presence of different functional groups in CPS fiber i.e., cellulose, hemicellulose, and lignin which was ensured by Fourier Transformed Infrared (FTIR) Spectrophotometer analysis. X-ray diffraction and chemical constituent analysis revealed high cellulose content and crystallinity i.e., 66.1% and 41.12% respectively, which is comparatively moderate in the case of CPS fiber. Scherrer's equation has been used to determine crystallite size i.e., 2.28 nm. The mean length and diameter of the CPS fiber were 382.0 and 23.36 μm, respectively. The maximum tensile strength was obtained at 657 ± 58.8 MPa for 50 mm fiber and young’s modulus 88.76 ± 30.42 MPa for 50 mm fiber. The required energy to break has been recorded at 346.16 J. Thermal analysis revealed that CPS fibers have thermal stability up to 279 °C. The unique Cyperus platystylis stem fibers could therefore be a suitable reinforcement material for the bio-composites used in semi-structural applications since they have higher functional qualities.