Global hotspots and research trends of radiation-induced skin injury: a bibliometric analysis from 2004 to 2023.

Background: Radiation therapy has become an important treatment for many malignant tumours after surgery and for palliative tumour care. Although modern radiotherapy technology is constantly improving, radiation damage to normal tissues is often difficult to avoid, and radiation-induced skin injury (RSI) is a common complication, manifested as skin erythema, peeling, ulceration, and even bone and deep organ damage, seriously affect the quality of life for patients. Basic research and clinical trials related to RSI have achieved certain results, while no researchers have conducted comprehensive bibliometric studies. Objective: A comprehensive bibliometric analysis of publications on RSI published between 2004 and 2023 was conducted to identify current hotspots and future directions in this area of study. Methods: RSI-related publications published between January 1, 2004, and December 31, 2023, were retrieved from the Web of Science Core Collection (WoSCC) database for analysis using VOSviewer and CiteSpace analytics. Results: A total of 1009 publications on RSI from 2004 to 2023 were included in the WoSCC database. The United States had the highest productivity with 299 papers, accounting for 29.63% of the total production, followed by China with 193 papers (19.13%) and Japan with 111 papers (11.00%). In terms of research institutions and journals, the University of Toronto and Journal of Supportive Care in Cancer published the highest number of papers. Professor Edward Chow published the most articles, while Professor Shuyu Zhang was the most cited. The top ten most-cited papers focused on the pathogenesis, prevention, and management of RSI. Keyword co-occurrence analysis and the top 25 keywords with the strongest citation bursts suggest that current research focuses on the pathogenesis, prevention, and treatment management of RSI. Conclusion: This study conducted a systematic bibliometric analysis of RSI publications from 2004 to 2023; identified the trends in RSI publications, major research countries, major research institutions, major research journals, major research authors, and major research keywords; and revealed the future development direction and research hotspots of this field. This study provides a valuable reference for future RSI research.

Effect of molecular structure on membrane diffusion: Triphenylmethanes across Escherichia coli studied by second harmonic light scattering.

Understanding how the structure of molecules affects their permeability across cell membranes is crucial for many topics in biomedical research, including the development of drugs. In this work, we examine the transport rates of structurally similar triphenylmethane dyes, malachite green (MG) and brilliant green (BG), across the membranes of living Escherichia coli (E. coli) cells and biomimetic liposomes. Using the time-resolved second harmonic light scattering technique, we found that BG passively diffuses across the E. coli cytoplasmic membrane (CM) 3.8 times faster than MG. In addition, BG exhibits a diffusion rate 3.1 times higher than MG across the membranes of liposomes made from E. coli polar lipid extracts. Measurements on these two molecules, alongside previously studied crystal violet (CV), another triphenylmethane molecule, are compared against the set of propensity rules developed by Lipinski and co-workers for assessing the permeability of hydrophobic ion-like drug molecules through biomembranes. It indicates that BG's increased diffusion rate is due to its higher lipophilicity, with a distribution coefficient 25 times greater than MG. In contrast, CV, despite having similar lipophilicity to MG, shows negligible permeation through the E. coli CM on the observation scale, attributed to its more hydrogen bonding sites and larger polar surface area. Importantly, cell viability tests revealed that BG's antimicrobial efficacy is ∼2.4 times greater than that of MG, which aligns well with its enhanced diffusion into the E. coli cytosol. These findings offer valuable insights for drug design and development, especially for improving the permeability of poorly permeable drug molecules.

Frontiers and hotspots evolution in mild cognitive impairment: a bibliometric analysis of from 2013 to 2023.

Background: Mild cognitive impairment is a heterogeneous syndrome. The heterogeneity of the syndrome and the absence of consensus limited the advancement of MCI. The purpose of our research is to create a visual framework of the last decade, highlight the hotspots of current research, and forecast the most fruitful avenues for future MCI research. Methods: We collected all the MCI-related literature published between 1 January 2013, and 24 April 2023, on the "Web of Science." The visual graph was created by the CiteSpace and VOSviewer. The current research hotspots and future research directions are summarized through the analysis of keywords and co-cited literature. Results: There are 6,075 articles were included in the final analysis. The number of publications shows an upward trend, especially after 2018. The United States and the University of California System are the most prolific countries and institutions, respectively. Petersen is the author who ranks first in terms of publication volume and influence. Journal of Alzheimer's Disease was the most productive journal. "neuroimaging," "fluid markers," and "predictors" are the focus of current research, and "machine learning," "electroencephalogram," "deep learning," and "blood biomarkers" are potential research directions in the future. Conclusion: The cognition of MCI has been continuously evolved and renewed by multiple countries' joint efforts in the past decade. Hotspots for current research are on diagnostic biomarkers, such as fluid markers, neuroimaging, and so on. Future hotspots might be focused on the best prognostic and diagnostic models generated by machine learning and large-scale screening tools such as EEG and blood biomarkers.

Gefitinib (an EGFR tyrosine kinase inhibitor) plus anlotinib (an multikinase inhibitor) for untreated, EGFR-mutated, advanced non-small cell lung cancer (FL-ALTER): a multicenter phase III trial.

Dual inhibition of vascular endothelial growth factor and epidermal growth factor receptor (EGFR) signaling pathways offers the prospect of improving the effectiveness of EFGR-targeted therapy. In this phase 3 study (ClinicalTrial.gov: NCT04028778), 315 patients with treatment-naïve, EGFR-mutated, advanced non-small cell lung cancer (NSCLC) were randomized (1:1) to receive anlotinib or placebo plus gefitinib once daily on days 1-14 per a 3-week cycle. At the prespecified final analysis of progression-free survival (PFS), a significant improvement in PFS was observed for the anlotinib arm over the placebo arm (hazards ratio [HR] = 0.64, 95% CI, 0.48-0.80, P = 0.003). Particularly, patients with brain metastasis and those harboring EGFR amplification or high tumor mutation load gained significant more benefits in PFS from gefitinib plus anlotinib. The incidence of grade 3 or higher treatment-emergent adverse events was 49.7% of the patients receiving gefitinib plus anlotinib versus 31.0% of the patients receiving gefitinib plus placebo. Anlotinib plus gefitinib significantly improves PFS in patients with treatment-naïve, EGFR-mutated, advanced NSCLC, with a manageable safety profile.

No genetic link between Parkinson's disease and SARS-CoV-2 infection: a two-sample Mendelian randomization study.

Objective: Existing literature has not clearly elucidated whether SARS-CoV-2 infection increases the incidence of Parkinson's disease or if Parkinson's disease patients are more susceptible to the effects of SARS-CoV-2 infection. To clarify the issue, this study employs a genetic epidemiological approach to investigate the association. Methods: This study utilizes a two-sample Mendelian randomization analysis. The primary analysis employs the inverse variance-weighted (IVW) method, supplemented by secondary analyses including MR-Egger regression, weighted median, IVW radial method, and weighted mode, to evaluate the bidirectional causal relationship between Parkinson's disease and SARS-CoV-2 infection. Results: IVW results showed no genetic causality between SARS-CoV-2 susceptibility, hospitalization rate and severity and Parkinson's disease. (IVW method: p = 0.408 OR = 1.10 95% CI: 0.87 ~ 1.39; p = 0.744 OR = 1.11 95% CI: 0.94 ~ 1.09; p = 0.436 OR = 1.05 95% CI: 0.93 ~ 1.17). Parkinson's disease was not genetically associated with susceptibility to new crown infections, hospitalization rates, and severity (IVW method: p = 0.173 OR = 1.01 95% CI: 0.99 ~ 1.03; p = 0.109 OR = 1.05 95% CI: 0.99 ~ 1.12; p = 0.209 OR = 1.03 95% CI: 0.99 ~ 1.07). MR-Egger regression, weighted median, IVW radial method, and weighted mode results are consistent with the results of the IVW method. Conclusion: This study does not support a genetic link between Parkinson's disease and SARS-CoV-2 infection, and the association observed in previous cohort studies and observational studies may be due to other confounding factors.

A novel causative factor of injury: Severe burns related to fires and explosions of lithium-ion batteries of electric motorcycles.

Severe burns related to fires and explosions of lithium-ion batteries of electric motorcycles have not been reported to date. We retrospectively studied 419 patients admitted to our burn intensive care unit from January 2016 to December 2021. Of these 419 patients, 26 (22 male, 4 female; median age, 42 years) had burns related to lithium-ion battery fires and explosions, and all of their injury characteristics were similar to those of traditional flame burns. Lithium-ion battery-related burns were the eighth most common cause of burn injuries among all hospitalized patients. The 26 patients comprised 10 unemployed and 16 employed individuals. Twenty-three patients were injured at home during the battery charging process, and three were injured outdoors (one by a fire while the electric motorcycle was stationary and the others two by a fire while riding the motorcycle). The burn sites were distributed over the whole body; the burn area ranged from 10 % to 100 % of the total body surface area, and the burn depth ranged from superficial second-degree burns to third-degree burns. Twenty-three patients had inhalation injuries, and ten underwent prophylactic tracheostomy and intubation. Multiple operations were required for wound repair. Although convenient, lithium-ion electric motorcycles can also cause severe burns. To prevent these injuries, we must increase public safety awareness and education, develop new battery energy storage systems and battery management systems, and ensure the safety of batteries. Consumers should be aware of the potential dangers of lithium-ion batteries and comply with related security measures.

Escherichia coli exopolysaccharides disrupt Pseudomonas aeruginosa biofilm and increase its antibiotic susceptibility.

Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen that causes infectious diseases. It has high tendency to form biofilms, resulting in the failure of traditional antibiotic therapies. Inspired by the phenomenon that co-culture of Escherichia coli (E. coli) and P. aeruginosa leads to a biofilm reduction, we reveal that E. coli exopolysaccharides (EPS) can disrupt P. aeruginosa biofilm and increase its antibiotic susceptibility. The results show that E. coli EPS effectively inhibit biofilm formation and disrupt mature biofilms in P. aeruginosa, Staphylococcus aureus, and E. coli itself. The maximal inhibition and disruption rates against P. aeruginosa biofilm are 40 % and 47 %, respectively. Based on the biofilm-disrupting ability of E. coli EPS, we develop an E. coli EPS/antibiotic combining strategy for the treatment of P. aeruginosa biofilms. The combination with E. coli EPS increases the antibacterial efficiency of tobramycin against P. aeruginosa biofilms in vitro and in vivo. This study provides a promising strategy for treating biofilm infections. STATEMENT OF SIGNIFICANCE: Biofilm formation is a leading cause of chronic infections. It blocks antibiotics, increases antibiotic-tolerance, and aids in immune evasion, thus representing a great challenge in clinic. This study proposes a promising approach to combat pathogenic Pseudomonas aeruginosa (P. aeruginosa) biofilms by combining Escherichia coli exopolysaccharides with antibiotics. This strategy shows high efficiency in different P. aeruginosa stains, including two laboratory strains, PAO1 and ATCC 10145, as well as a clinically acquired carbapenem-resistant strain. In addition, in vivo experiments have shown that this approach is effective against implanted P. aeruginosa biofilms and can prevent systemic inflammation in mice. This strategy offers new possibilities to address the clinical failure of conventional antibiotic therapies for microbial biofilms.

Organophosphate esters in human serum: a relatively simple and efficient liquid chromatography-mass spectrometry method.

Organophosphate esters (OPEs) are widely used as flame retardants and plasticizers, which are of growing concern due to their endocrine-disrupting effects, developmental toxicity, and potential carcinogenicity. However, data on human exposure to OPEs is still scarce. In this study, a relatively simple and efficient method with less serum consumption for the detection of OPEs in human serum was developed and validated. Nine OPEs in 200 µL of human serum were extracted by an acetonitrile-formic acid system and analyzed using ultra-high-performance liquid chromatography-quadrupole tandem time-of-flight high-resolution mass spectrometry. Several experiments were conducted to optimize the chromatographic and mass spectrometric conditions as well as sample preparation to obtain a more sensitive and efficient analytical protocol. The proposed method was examined in terms of its linearity, accuracy, precision, detection limit, and matrix effect. The matrix-spiked recoveries of the target OPEs ranged from 83.3% to 111.1%, with relative standard deviations between 2.7% and 16.6%. The detection limits were within (0.002 to 0.029) ng mL-1, while the quantification limits were within (0.007 to 0.098) ng mL-1. The internal standard-corrected matrix effects varied from 82.7% to 113.9%. Finally, the method was applied to detect OPEs in actual human serum samples. All nine OPEs were detected in 269 serum samples to varying degrees, with the average concentrations ranging from (0.08 to 1.77) ng mL-1. After validation, the method was found to be simple in pretreatment, high in sensitivity, good in practicality, and suitable for exposure evaluation of OPEs in populations.

Perception of diagnosis by family caregivers in severe brain injury patients in China.

OBJECTIVES: Surrogate decision-making by family caregivers for patients with severe brain injury is influenced by the availability and understanding of relevant information and expectations for future rehabilitation. We aimed to compare the consistency of family caregivers' perceptions with clinical diagnoses and to inform their expectation of prognosis in the future. METHODS: The Coma Recovery Scale-Revised was used to assess the diagnosis of inpatients with severe brain injury between February 2019 and February 2020. A main family caregiver was included per patient. The family caregiver's perception of the patient's consciousness and expectations of future recovery were collected through questionnaires and compared consistently with the clinical diagnosis. RESULTS: The final sample included 101 main family caregivers of patients (57 UWS, unresponsive wakefulness syndrome, 37 MCS, minimally conscious state, 7 EMCS, emergence from MCS) with severe brain injury. Only 57 family caregivers correctly assessed the level of consciousness as indicated by the CRS-R, showing weak consistency (Kappa = 0.217, P = 0.002). Family caregivers' demographic characteristics and CRS-R diagnosis influenced the consistency between perception and clinical diagnosis. Family caregivers who provided hands-on care to patients showed higher levels of consistent perception (AOR = 12.24, 95% CI = 2.06-73.00, P = 0.006). Compared to UWS, the family caregivers of MCS patients were more likely to have a correct perception (OR = 7.68, 95% CI = 1.34-44.06). Family caregivers had positive expectations for patients' recovery in terms of both communication and returning to normal life. CONCLUSION: Nearly half of family caregivers have inadequate understanding of their relative's level of consciousness, and most of them report overly optimistic expectations that do not align with clinical diagnosis. Providing more medical information to family caregivers to support their surrogate decision-making process is essential.

3D Bioprinting of Artificial Skin Substitute with Improved Mechanical Property and Regulated Cell Behavior through Integrating Patterned Nanofibrous Films.

Three-dimensional (3D) bioprinting has advantages for constructing artificial skin tissues in replicating the structures and functions of native skin. Although many studies have presented improved effect of printing skin substitutes in wound healing, using hydrogel inks to fabricate 3D bioprinting architectures with complicated structures, mimicking mechanical properties, and appropriate cellular environments is still challenging. Inspired by collagen nanofibers withstanding stress and regulating cell behavior, a patterned nanofibrous film was introduced to the printed hydrogel scaffold to fabricate a composite artificial skin substitute (CASS). The artificial dermis was printed using gelatin-hyaluronan hybrid hydrogels containing human dermal fibroblasts with gradient porosity and integrated with patterned nanofibrous films simultaneously, while the artificial epidermis was formed by seeding human keratinocytes upon the dermis. The collagen-mimicking nanofibrous film effectively improved the tensile strength and fracture resistance of the CASS, making it sewable for firm implantation into skin defects. Meanwhile, the patterned nanofibrous film also provided the biological cues to guide cell behavior. Consequently, CASS could effectively accelerate the regeneration of large-area skin defects in mouse and pig models by promoting re-epithelialization and collagen deposition. This research developed an effective strategy to prepare composite bioprinting architectures for enhancing mechanical property and regulating cell behavior, and CASS could be a promising skin substitute for treating large-area skin defects.

Novel decision tree models predict the overall survival of patients with submandibular gland cancer.

BACKGROUND: While the accurate prediction of the overall survival (OS) in patients with submandibular gland cancer (SGC) is paramount for informed therapeutic planning, the development of reliable survival prediction models has been hindered by the rarity of SGC cases. The purpose of this study is to identify key prognostic factors for OS in SGC patients using a large database and construct decision tree models to aid the prediction of survival probabilities in 12, 24, 60 and 120 months. MATERIALS AND METHODS: We performed a retrospective cohort study using the Surveillance, Epidemiology and End Result (SEER) program. Demographic and peri-operative predictor variables were identified. The outcome variables overall survival at 12-, 24-, 60, and 120 months. The C5.0 algorithm was utilized to establish the dichotomous decision tree models, with the depth of tree limited within 4 layers. To evaluate the performances of the novel models, the receiver operator characteristic (ROC) curves were generated, and the metrics such as accuracy rate, and area under ROC curve (AUC) were calculated. RESULTS: A total of 1,705, 1,666, 1,543, and 1,413 SGC patients with a follow up of 12, 24, 60 and 120 months and exact survival status were identified from the SEER database. Predictor variables of age, sex, surgery, radiation, chemotherapy, tumor histology, summary stage, metastasis to distant lymph node, and marital status exerted substantial influence on overall survival. Decision tree models were then developed, incorporating these vital prognostic indicators. Favorable consistency was presented between the predicted and actual survival statuses. For the training dataset, the accuracy rates for the 12-, 24-, 60- and 120-month survival models were 0.866, 0.767, 0.737 and 0.797. Correspondingly, the AUC values were 0.841, 0.756, 0.725, and 0.774 for the same time points. CONCLUSIONS: Based on the most important predictor variables identified using the large, SEER database, decision tree models were established that predict OS of SGC patients. The models offer a more exhaustive evaluation of mortality risk and may lead to more personalized treatment strategies.

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study.

Background: Accurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methods: Patients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. Results: A total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71-0.84; test: 0.77, 95%CI 0.68-0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. Conclusions: Our study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Thyroid cancer prognostic biomarker ARL4A and its relationship with immune infiltration.

BACKGROUND: Thyroid cancer (THCA) is a prevalent form of cancer with high rates of morbidity and mortality. The small GTPase ADP-ribosylation factor-like 4A (ARL4A) is integral to various cellular processes, including cytoskeletal restructuring, vesicular transport, cell migration, and neuronal development. However, the role of ARL4A as a clinical predictor, particularly its relation to immune cell infiltration in THCA, remains unclear. METHODS: A combination of experimental studies and analysis of online databases was employed to investigate ARL4A expression in THCA. Clinical and pathological data from THCA patients were compiled for a comprehensive subgroup analysis. The Kaplan-Meier and Cox regression methods were utilized to evaluate the prognostic significance of ARL4A in THCA patients. Finally, the "Cancer Genome Atlas" was analyzed to explore the correlation between immune cell infiltration, ARL4A expression, and their joint impact on prognosis. RESULTS: ARL4A exhibited low expression in THCA. An elevated ARL4A was associated with poor prognosis. Moreover, the expression of ARL4A was correlated with the age, gender, and pathological stage of THCA patients. Finally, ARL4A expression was found to be negatively correlated with immune cell infiltration and influenced the prognosis of patients through changes in the immune environment. CONCLUSION: ARL4A may serve as a potential biomarker for the diagnosis and treatment of THCA, impacting the prognosis of patients through the modulation of the immune microenvironment.

Causal-ARG: a causality-guided framework for annotating properties of antibiotic resistance genes.

MOTIVATION: The crisis of antibiotic resistance, which causes antibiotics used to treat bacterial infections to become less effective, has emerged as one of the foremost challenges to public health. Identifying the properties of antibiotic resistance genes (ARGs) is an essential way to mitigate this issue. Although numerous methods have been proposed for this task, most of these approaches concentrate solely on predicting antibiotic class, disregarding other important properties of ARGs. In addition, existing methods for simultaneously predicting multiple properties of ARGs fail to account for the causal relationships among these properties, limiting the predictive performance. RESULTS: In this study, we propose a causality-guided framework for annotating properties of ARGs, in which causal inference is utilized for representation learning. More specifically, the hidden biological patterns determining the properties of ARGs are described by a Gaussian Mixture Model, and procedure of causal representation learning is used to derive the hidden features. In addition, a causal graph among different properties is constructed to capture the causal relationships among properties of ARGs, which is integrated into the task of annotating properties of ARGs. The experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework on the task of annotating properties of ARGs. AVAILABILITY AND IMPLEMENTATION: The data and source codes are available in GitHub at https://github.com/David-WZhao/CausalARG.

Multifunctional antibacterial hydrogels for chronic wound management.

Chronic wounds have gradually evolved into a global health challenge, comprising long-term non-healing wounds, local tissue necrosis, and even amputation in severe cases. Accordingly, chronic wounds place a considerable psychological and economic burden on patients and society. Chronic wounds have multifaceted pathogenesis involving excessive inflammation, insufficient angiogenesis, and elevated reactive oxygen species levels, with bacterial infection playing a crucial role. Hydrogels, renowned for their excellent biocompatibility, moisture retention, swelling properties, and oxygen permeability, have emerged as promising wound repair dressings. However, hydrogels with singular functions fall short of addressing the complex requirements associated with chronic wound healing. Hence, current research emphasises the development of multifunctional antibacterial hydrogels. This article reviews chronic wound characteristics and the properties and classification of antibacterial hydrogels, as well as their potential application in chronic wound management.

Subtask-Aware Representation Learning for Predicting Antibiotic Resistance Gene Properties via Gating-Controlled Mechanism.

The crisis of antibiotic resistance has become a significant global threat to human health. Understanding properties of antibiotic resistance genes (ARGs) is the first step to mitigate this issue. Although many methods have been proposed for predicting properties of ARGs, most of these methods focus only on predicting antibiotic classes, while ignoring other properties of ARGs, such as resistance mechanisms and transferability. However, acquiring all of these properties of ARGs can help researchers gain a more comprehensive understanding of the essence of antibiotic resistance, which will facilitate the development of antibiotics. In this paper, the task of predicting properties of ARGs is modeled as a multi-task learning problem, and an effective subtask-aware representation learning-based framework is proposed accordingly. More specifically, property-specific expert networks and shared expert networks are utilized respectively to learn subtask-specific features for each subtask and shared features among different subtasks. In addition, a gating-controlled mechanism is employed to dynamically allocate weights to subtask-specific semantics and shared semantics obtained respectively from property-specific expert networks and shared expert networks, thus adjusting distinctive contributions of subtask-specific features and shared features to achieve optimal performance for each subtask simultaneously. Extensive experiments are conducted on publicly available data, and experimental results demonstrate the effectiveness of the proposed framework on the task of ARGs properties prediction.

Age-stage, two-sex life table, and population projection of Ostrinia furnacalis (Lepidoptera: Crambidae) at different temperatures.

Ostrinia furnacalis (Guenée) (Lepidoptera: Crambidae), a highly destructive pest in Asia, poses a significant threat to maize production by causing substantial yield losses. However, there is a lack of information regarding the impact of temperature variations on its population dynamics and the age-stage and two-sex life table. This study aimed to investigate the impact of 4 temperatures (20 °C, 24 °C, 28 °C, 32 °C) on the development, reproduction, and survival of O. furnacalis under controlled laboratory conditions. Our results revealed that O. furnacalis successfully developed, survived, and laid eggs across the tested temperatures (20-32 °C). The shortest developmental duration for all immature stages was observed at 32 °C. Conversely, increasing temperatures led to decreased longevity. Among the temperatures tested, 28 °C proved to be optimal for O. furnacalis, exhibiting the highest intrinsic rate of increase, finite rate of increase, and net reproductive rate. Our findings indicate that O. furnacalis thrives within a wide temperature range of 20-32 °C, with 28 °C being the most favorable for reproduction. These insights are crucial for predicting population dynamics under diverse climatic conditions and developing effective control strategies against O. furnacalis. This study enhances our understanding of O. furnacalis' life-history traits and provides valuable information for targeted pest management approaches.