VacSol-ML(ESKAPE): Machine learning empowering vaccine antigen prediction for ESKAPE pathogens.

The ESKAPE family, comprising Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp., poses a significant global threat due to their heightened virulence and extensive antibiotic resistance. These pathogens contribute largely to the prevalence of nosocomial or hospital-acquired infections, resulting in high morbidity and mortality rates. To tackle this healthcare problem urgent measures are needed, including development of innovative vaccines and therapeutic strategies. Designing vaccines involves a complex and resource-intensive process of identifying protective antigens and potential vaccine candidates (PVCs) from pathogens. Reverse vaccinology (RV), an approach based on genomics, made this process more efficient by leveraging bioinformatics tools to identify potential vaccine candidates. In recent years, artificial intelligence and machine learning (ML) techniques has shown promise in enhancing the accuracy and efficiency of reverse vaccinology. This study introduces a supervised ML classification framework, to predict potential vaccine candidates specifically against ESKAPE pathogens. The model's training utilized biological and physicochemical properties from a dataset containing protective antigens and non-protective proteins of ESKAPE pathogens. Conventional autoencoders based strategy was employed for feature encoding and selection. During the training process, seven machine learning algorithms were trained and subjected to Stratified 5-fold Cross Validation. Random Forest and Logistic Regression exhibited best performance in various metrics including accuracy, precision, recall, WF1 score, and Area under the curve. An ensemble model was developed, to take collective strengths of both the algorithms. To assess efficacy of our final ensemble model, a high-quality benchmark dataset was employed. VacSol-ML(ESKAPE) demonstrated outstanding discrimination between protective vaccine candidates (PVCs) and non-protective antigens. VacSol-ML(ESKAPE), proves to be an invaluable tool in expediting vaccine development for these pathogens. Accessible to the public through both a web server and standalone version, it encourages collaborative research. The web-based and standalone tools are available at http://vacsolml.mgbio.tech/.

The migraine epidemic among medical students: a call for action.

Migraine is characterized by recurrent headaches of moderate-to-severe intensity and poses a significant challenge for medical students. This is a narrative literature review using PubMed and Scopus databases. This study examines how common migraine is in this group and suggests working together to address how it affects students' well-being and chances of succeeding as a medical professional in the future. Early diagnosis by licensed medical specialists is essential for effective management of migraine. To address this, the authors propose a multifaceted strategy. By including direct education on migraines in medical school curricula, future doctors will be better prepared to treat patients with comparable problems and manage their own migraines. Students with migraines can also benefit greatly from creating a supportive learning environment through staff training, accommodating academic policies, and providing easily available healthcare resources. In addition, this technology may be helpful. Apps for relaxation and migraine tracking can help students better manage their condition. Long-term success requires cooperation among all parties. By promoting cooperation among medical schools, student associations, healthcare practitioners, and governmental organizations, the authors can raise public awareness of migraine, make pertinent resources easier to access, and create evidence-based solutions specially designed to meet the needs of medical students who experience migraine. In the end, putting student well-being first and working together to put these solutions into practice can enable aspiring doctors to succeed at both personal and professional levels.

Aberrant HIF1- α and SIX-1 Expression is Associated with Poor Prognosis in Acute Myeloid Leukemia Patients with Isocitrate Dehydrogenase 1 Mutations.

BACKGROUND: IDH1 mutations are common in many cancers, however, their role in promoting the Warburg effect remains elusive. This study elucidates the putative involvement of mutant-IDH1 in regulating hypoxia-inducible factor (HIF1-α) and Sine-Oculis Homeobox-1 (SIX-1) expression. METHODOLOGY: Genetic screening was performed using the ARMS-PCR in acute myeloid leukemia (AML), brain, and breast cancer (BC) cohorts, while transcript expression was determined using qPCR. Further, a meta-analysis of risk factors associated with the R132 mutation was performed. RESULTS: Approximately 32% of AML and ∼60% of glioma cases were mutants, while no mutation was found in the BC cohort. 'AA' and TT' were associated with higher disease risk (OR = 12.18 & 4.68) in AML and had significantly upregulated IDH1 expression. Moreover, downregulated HIF1-α and upregulated SIX-1 expression was also observed in these patients, suggesting that mutant-IDH1 may alter glucose metabolism. Perturbed IDH1 and HIF-α levels exhibited poor prognosis in univariate and multivariate analysis, while age and gender were found to be contributory factors as well. Based on the ROC model, these had a good potential to be used as prognostic markers. A significant variation in frequencies of R132 mutations in AML among different populations was observed. Cytogenesis (R2 = 12.2%), NMP1 mutation status (R2 = 18.5%), and ethnic contributions (R2 = 73.21%) were critical moderators underlying these mutations. Women had a higher risk of R132 mutation (HR = 1.3, P < 0.04). The pooled prevalence was calculated to be 0.29 (95% CI 0.26-0.33, P < 0.01), indicating that IDH1 mutations are a significant prognostic factor in AML. CONCLUSION: IDH1 and HIF1-α profiles are linked to poor survival and prognosis, while high SIX-1 expression in IDH1 mutants suggests a role in leukemic transformation and therapy response in AML.

IDH1 mutations are common in many types of cancer, but scientists have not fully understood how they contribute to the Warburg effect - a process that alters glucose metabolism in cells. In this study, we evaluate the association between mutant-IDH1 and HIF1 as well as SIX-1 gene expression. We analyzed genetic data from patients with brain cancer, breast cancer, and acute myeloid leukemia (AML), and found that roughly 32% of AML cases and 60% of glioma cases had IDH1 mutations, while no mutations were found in breast cancer. Patients with mutant genotypes had a higher risk of disease and showed upregulated IDH1 expression. They also had downregulated HIF1 and upregulated SIX-1 expression, suggesting that mutant-IDH1 can change glucose metabolism in cancer cells. Patients with abnormal IDH1 and HIF1 levels were more likely to have a poor prognosis. Further, we identified several risk factors that can influence IDH1 mutations, including cytogenesis, NMP1 mutation status, and ethnicity. The researchers calculated that IDH1 mutations are a significant factor in predicting outcomes for AML.

Molecular Modification Strategies of Nitrilase for Its Potential Application in Agriculture.

Some feed source plants will produce secondary metabolites such as cyanogenic glycosides during metabolism, which will produce some poisonous nitrile compounds after hydrolysis and remain in plant tissues. The consumption of feed-source plants without proper treatment affect the health of the animals' bodies. Nitrilases can convert nitriles and have been used in industry as green biocatalysts. However, due to their bottleneck problems, their application in agriculture is still facing challenges. Acid-resistant nitrilase preparations, high-temperature resistance, antiprotease activity, strong activity, and strict reaction specificity urgently need to be developed. In this paper, the application potential of nitrilase in agriculture, especially in feed processing industry was explored, the source properties and catalytic mechanism of nitrilase were reviewed, and modification strategies for nitrilase application in agriculture were proposed to provide references for future research and application of nitrilase in agricultural and especially in the biological feed scene.

Natural products can be potential inhibitors of metalloproteinase II from Bacteroides fragilis to intervene colorectal cancer.

Bacteroides fragilis, a gram negative and obligate anaerobe bacterium, is a member of normal gut microbiota and facilitates many essential roles being performed in human body in normal circumstances specifically in Gastrointestinal or GI tract. Sometimes, due to genetics, epigenetics, and environmental factors, Bacteroides fragilis and their protein(s) start interacting with intestinal epithelium thus damaging the lining leading to colorectal cancers (CRC). To identify these protein(s), we incorporated a novel subtractive proteomics approach in the study. Metalloproteinase II (MPII), a Bacteroides fragilis toxin (bft), was investigated for its virulence and unique pathways to demonstrate its specificity and uniqueness in pathogenicity followed by molecular docking against a set of small drug-like natural molecules to discover potential inhibitors against the toxin. All these identified inhibitor-like molecules were analyzed for their ADMET calculations and detailed physiochemical properties to predict their druggability, GI absorption, blood brain barrier and skin permeation, and others. Resultantly, a total of ten compounds with the least binding energies were obtained and were subjected to protein-compound interaction analysis. Interaction analysis revealed the most common ligand-interacting residues in MPII are His 345, Glu 346, His 339, Gly 310, Tyr 341, Pro 340, Asp 187, Phe 309, Lys 307, Ile 185, Thr 308, and Pro 184. Therefore, top three compounds complexed with MPII having best binding energies were selected in order to analyze their trajectories. RMSD, RMSF, Rg and MMPBSA analysis revealed that all compounds showed good binding and keeping the complex stable and compact throughout the simulation time in addition to all properties and qualities of being a potential inhibitor against MPII.

A Compact Linear Microstrip Patch Beamformer Antenna Array for Millimeter-Wave Future Communication.

5/6G is anticipated to address challenges such as low data speed and high latency in current cellular networks, particularly as the number of users overwhelms 4G and LTE capabilities. This paper proposes a microstrip patch antenna array comprising six radiating patches and utilizing a microstrip line feeding technique to facilitate the compact design crucial for 5G implementation. ROGER 3003, chosen for its advanced and environmentally friendly features, serves as the dielectric material, ensuring suitability for 5G and B5G applications. The designed antenna, evaluated at a resonating frequency of 28.8 GHz with a -10 dB impedance bandwidth of 1 GHz, offers a high gain of 9.19 dBi. Its compact array, cost-effectiveness, and broad impedance and radiation coverage position it as a viable candidate for 5G and future communication applications.

Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.

This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML - particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors - in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components - including the blue-green-red, multiple, and spatial attentions - in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.

Heavy Metal Concentrations in Water, Sediment, and Fish Species in Chashma Barrage, Indus River: A Comprehensive Health Risk Assessment.

The increasing levels of heavy metals in aquatic environments, driven by human activities, pose a critical threat to ecosystems' overall health and sustainability. This study investigates the bioaccumulation of heavy metals (Pb, Cu, Cr, and Cd) in water, sediment, and three fish species (Catla catla, Labeo rohita, Cirrhinus mrigala) of different feeding zones within Chashma Barrage, located in the Mianwali district of Punjab, Pakistan, on the Indus River. A comprehensive analysis, including an assessment of associated human health risks, was conducted. Thirty samples from all three sites for each fish species, with an average body weight of 160 ± 32 g, were collected from Chashma Barrage. Water quality parameters indicated suitability for fish growth and health. Heavy metal concentrations were determined using an atomic absorption spectrometer. Results indicated elevated levels of Cd, Cr, and Cu in sediment and Pb and Cd in water, surpassing WHO standard limits. Among the fish species, bottom feeder (C. mrigala) exhibited significantly (P < 0.05) higher heavy metal levels in its tissues (gills, liver, and muscle) compared to column feeder (L. rohita) and surface feeder (C. catla). Liver tissues across all species showed higher heavy metal bioaccumulation, followed by gills. Principal component analysis (PCA) revealed strong correlations among heavy metals in sediment, gills, muscle, and water in every fish species. However, the vector direction suggests that Cr was not correlated with other heavy metals in the system, indicating a different source. The human health risk analysis revealed lower EDI, THQ, and HI values (< 1) for the fish species, indicating no adverse health effects for the exposed population. The study emphasizes the bioaccumulation differences among fish species, underscoring the higher heavy metal concentrations in bottom feeder fish within Chashma Barrage.

Assessment of heavy metal levels in polyculture fish farms and their aquatic ecosystems: an integrative study addressing environmental and human health risks associated with dam water usage.

This study examines the levels of heavy metals in polyculture fish (Labeo rohita, Cyprinus carpio, and Catla catla), water, and sediment in Tanda Dam, Kohat, Pakistan, aiming to understand environmental and health risks. Samples of fish, water, and sediment were collected from 3 fish farms, and heavy metal concentrations were measured using a Flame Atomic Absorption Spectrophotometer (AAS). Results reveal that C. catla exhibited significantly higher (p < 0.05) levels of Zn than other fish species. Conversely, C. carpio showed significantly higher (p < 0.05) concentrations of Pb, Cd, Cr, Mn, Cu, As, and Ni than other species. The heavy metal hierarchy in C. carpio was found to be Zn > Cu > Pb > Cr > Cd > Mn > As > Ni. While heavy metal levels in L. rohita and C. catla generally fell within reference ranges, exceptions were noted for Zn, Pb, and Cd. Conversely, in C. carpio, all metals exceeded reference ranges except for Cu and Ni. Principal Component Analysis (PCA) indicated a close relationship between water and sediment. Additionally, cluster analysis suggested that C. catla formed a distinct cluster from L. rohita and C. carpio, implying different responses to the environment. Despite concerns raised by the Geoaccumulation Index (Igeo) and Contamination Factor (CF), particularly for Cd, which exhibited a high CF. Furthermore, Hazard Index (HI) values for all three fish species were below 1, suggesting low health risks. However, elevated Igeo and CF values for Cd suggest significant pollution originating from anthropogenic sources. This study underscores the importance of monitoring heavy metals in water for both environmental preservation and human health protection. Future research efforts should prioritize pollution control measures to ensure ecosystem and public health safety.

Burden and factors associated with occupational tuberculosis infection among high-risk workers in Lahore District, Pakistan.

INTRODUCTION: The main objective of the study was to estimate the burden of occupational tuberculosis infection in high-risk occupational workers and to identify risk factors associated with the prevalence of Mycobacterium tuberculosis complex (MTBC). METHODOLOGY: An analytical cross-sectional study was conducted among high-risk occupational workers including veterinarians, abattoir workers, animal handlers, livestock farmers, and microbiology laboratory workers. Sputum samples were collected from 100 participants and polymerase chain reaction (PCR) tests were done to diagnose tuberculosis (TB) infection. Data on potential risk factors was collected in a pre-designed questionnaire. The MTBC prevalence ratio was estimated. Logistic regression analysis was conducted to identify risk factors and the crude odds ratio (OR) was calculated. RESULTS: Among the 100 enrolled high risk occupational workers, the prevalence of MTBC was 46% (95% CI: 35.98-56.25). Living in a joint family (OR 3.85, 95% CI: 1.58-9.37), and use of unpasteurized milk (OR 3.42, 95% CI: 1.4-8.39), were significantly associated with MTBC infection. CONCLUSIONS: Tuberculosis is a significant health burden in high-risk occupational groups, especially animal handlers and laboratory workers, in Lahore, Pakistan. The study also emphasized the need for formal work-related training, and enhanced zoonotic TB awareness among occupational workers.

Perspective on Agricultural Industrialization: Modification Strategies for Enhancing the Catalytic Capacity of Keratinase.

Keratinases is a special hydrolytic enzyme produced by microorganisms, which has the ability to catalyze the degradation of keratin. Currently, keratinases show great potential for application in many agricultural and industrial fields, such as biofermented feed, leather tanning, hair removal, and fertilizer production. However, these potentials have not yet been fully unleashed on an industrial scale. This paper reviews the sources, properties, and catalytic mechanisms of keratinases. Strategies for the molecular modification of keratinases are summarized and discussed in terms of improving the substrate specificity, thermostability, and pH tolerance of keratinases. The modification strategies are also enriched by the introduction of immobilized enzymes and directed evolution. In addition, the selection of modification strategies when facing specific industrial applications is discussed and prospects are provided. We believe that this review serves as a reference for the future quest to extend the application of keratinases from the laboratory to industry.

Initial Experience With Robotic Liver Resection in the United States.

BACKGROUND: This study's aim was to show the feasibility and safety of robotic liver resection (RLR) even without extensive experience in major laparoscopic liver resection (LLR). METHODS: A single center, retrospective analysis was performed for consecutive liver resections for solid liver tumors from 2014 to 2022. RESULTS: The analysis included 226 liver resections, comprising 127 (56.2%) open surgeries, 28 (12.4%) LLR, and 71 (31.4%) RLR. The rate of RLR increased and that of LLR decreased over time. In a comparison between propensity score matching-selected open liver resection and RLR (41:41), RLR had significantly less blood loss (384 ± 413 vs 649 ± 646 mL, P = .030) and shorter hospital stay (4.4 ± 3.0 vs 6.4 ± 3.7 days, P = .010), as well as comparable operative time (289 ± 123 vs 290 ± 132 mins, P = .954). A comparison between LLR and RLR showed comparable perioperative outcomes, even with more surgeries with higher difficulty score included in RLR (5.2 ± 2.7 vs 4.3 ± 2.5, P = .147). The analysis of the learning curve in RLR demonstrated that blood loss, conversion rate, and complication rate consistently improved over time, with the case number required to achieve the learning curve appearing to be 60 cases. CONCLUSIONS: The findings suggest that RLR is a feasible, safe, and acceptable platform for liver resection, and that the safe implementation and dissemination of RLR can be achieved without solid experience of LLR.

A transfer learning enabled approach for ocular disease detection and classification.

Ocular diseases pose significant challenges in timely diagnosis and effective treatment. Deep learning has emerged as a promising technique in medical image analysis, offering potential solutions for accurately detecting and classifying ocular diseases. In this research, we propose Ocular Net, a novel deep learning model for detecting and classifying ocular diseases, including Cataracts, Diabetic, Uveitis, and Glaucoma, using a large dataset of ocular images. The study utilized an image dataset comprising 6200 images of both eyes of patients. Specifically, 70% of these images (4000 images) were allocated for model training, while the remaining 30% (2200 images) were designated for testing purposes. The dataset contains images of five categories that include four diseases, and one normal category. The proposed model uses transfer learning, average pooling layers, Clipped Relu, Leaky Relu and various other layers to accurately detect the ocular diseases from images. Our approach involves training a novel Ocular Net model on diverse ocular images and evaluating its accuracy and performance metrics for disease detection. We also employ data augmentation techniques to improve model performance and mitigate overfitting. The proposed model is tested on different training and testing ratios with varied parameters. Additionally, we compare the performance of the Ocular Net with previous methods based on various evaluation parameters, assessing its potential for enhancing the accuracy and efficiency of ocular disease diagnosis. The results demonstrate that Ocular Net achieves 98.89% accuracy and 0.12% loss value in detecting and classifying ocular diseases by outperforming existing methods.

Exploring the effects of noise pollution on physiology and ptilochronology of birds.

Short and long-term sound-induced stress on daily basis can affect the physiology of avian individuals because they are more susceptible to sound stress in an open environment. OBJECTIVES: An ex-situ study was carried out to determine the impact of noise on physiology and ptilochronology of non-breeding male domesticated quail birds. METHODOLOGY: During 60-days long trial, male quail birds, aged 5-weeks, weighing (c.100gm) were used. Out of 72 experimental birds, 18 birds were assigned to the Control Group (G1) while remaining 54 birds were divided equally into 3 treatment groups: Road Traffic noise (G2), Military activity noise (G3) and Human Activities noise (G4). Birds were housed in standard-sized separate cages (20 ×45 × 20 cm), every bird was kept apart in separate cage in open laboratory under maintained environmental conditions. Millet seeds and water were provided to all the experimental birds ad libitum. Noise originated from several sources of recorded high-intensity music (1125 Hz/ 90 dB), was administered for 5-6 hours per day. Observations were recorded in the morning and afternoon. The experiment was conducted during the non-breeding season from August to October in triplicate. Blood sampling was done after 60 days. RESULTS: According to the current study, noise stress significantly (p<0.05) increased the concentrations of creatinine, aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), bilirubin, uric acid, cholesterol, triglycerides, total protein, and glucose while a decline in the levels of albumin was seen in treatment birds of G3. While in terms of hematology, total white blood cells count (TWBC), total red blood cells count (TRBC), mean cell volume (MCV) & packed cell volume (PCV) concentrations were raised in blood of treatment birds of G3. In terms of hormones, noise stress significantly (p<0.05) increased the serum concentrations of Corticosterone in G3 while a significant (p<0.05) decline was observed in the concentrations of luteinizing hormone (LH), thyroid stimulating hormone (TSH), and follicle stimulating hormone (FSH) in the same group. Moreover, fault bar formation in G3 was more prominent than others. CONCLUSION: Noise stress can significantly affect serology, hematology, hormonal physiology and ptilochronology in quail birds.

Mitigating potential of polystyrene microplastics on bioavailability, uptake, and toxicity of copper in maize (Zea mays L.).

The coexistence of polystyrene microplastics (PSMPs) and copper (Cu) has become a pressing issue for croplands. However, limited literature is available regarding the interaction of PSMPs with essential micronutrients in Cu-contaminated soils. Therefore, the present study aimed to analyze the immobilization potential of PSMPs for micronutrient bioavailability in soil and Cu toxicity in maize (Zea mays L.). A pot experiment was conducted with maize variety "Islamabad gold" exposed to varying Cu concentrations (0, 50, 100, 200, and 400 mg/kg) and PSMPs (150-250 µm size, 0, 1, and 3% w/w) via soil spiking for 60 days. The concentrations of essential micronutrients (Zn, Cu, Mn, Fe) in soil and plant tissues were measured using an atomic absorption spectrophotometer. Moreover, malondialdehyde (MDA) and antioxidant activities (superoxide dismutase, ascorbate peroxidase, catalase, and peroxidase) were recorded. The concentration of Cu showed significant reduction in post-harvesting soil by 21, 24.8, 27.6, 29.2, and 30.2% from Cu0 to Cu400 mg/kg respectively from pre-sowing soil. On the other hand, the addition of 1%PSMPs and 3%PSMPs declined Cu by 16, 21.6, 24.4, 25.9, 27.8, and 12.6, 16.5, 19.9, 23.2, 25% from Cu0 to Cu400 mg/kg respectively. Maize showed significant improvement in growth under combined exposure of Cu and 3% PSMPs compared to individual exposure. The MDA level was decreased under the combined presence of Cu and PSMPs compared to individual Cu exposure. The percentage difference with 1%PSMPs was 98.1, 95.0, 92.0, 90.0, and 89.6%, while with 3%PSMPs was 93.2, 93.2, 87.7, 81.4, and 79.2% from Cu0 to Cu400 mg/kg respectively. Moreover, the impact of PSMPs was more prominent at a 3% dose compared to a 1% dose. The findings provided significant knowledge about the potential of PSMPs to mitigate Cu toxicity in maize. Future research should incorporate a variety of particle size distributions at natural conditions for variety-specific differences.

Corrigendum: Investigating the dynamic responses of Aegilops tauschii Coss. to salinity, drought, and nitrogen stress: a comprehensive study of competitive growth and biochemical and molecular pathways.

[This corrects the article DOI: 10.3389/fpls.2023.1238704.].

Harbor seal whiskers optimization algorithm with deep learning-based medical imaging analysis for gastrointestinal cancer detection.

Gastrointestinal (GI) cancer detection includes the detection of cancerous or potentially cancerous lesions within the GI tract. Earlier diagnosis is critical for increasing the success of treatment and improving patient outcomes. Medical imaging plays a major role in diagnosing and detecting GI cancer. CT scans, endoscopy, MRI, ultrasound, and positron emission tomography (PET) scans can help detect lesions, abnormal masses, and changes in tissue structure. Artificial intelligence (AI) and machine learning (ML) methods are being gradually applied to medical imaging for cancer diagnosis. ML algorithms, including deep learning methodologies like convolutional neural network (CNN), are applied frequently for cancer diagnosis. These models learn features and patterns from labelled datasets to discriminate between normal and abnormal areas in medical images. This article presents a new Harbor Seal Whiskers Optimization Algorithm with Deep Learning based Medical Imaging Analysis for Gastrointestinal Cancer Detection (HSWOA-DLGCD) technique. The goal of the HSWOA-DLGCD algorithm is to explore the GI images for the cancer diagnosis. In order to accomplish this, the HSWOA-DLGCD system applies bilateral filtering (BF) approach for the removal of noise. In addition, the HSWOA-DLGCD technique makes use of HSWOA with Xception model for feature extraction. For cancer recognition, the HSWOA-DLGCD technique applies extreme gradient boosting (XGBoost) model. Finally, the parameters compared with the XGBoost system can be selected by moth flame optimization (MFO) system. The experimental results of the HSWOA-DLGCD technique could be verified on the Kvasir database. The simulation outcome demonstrated a best possible solution of the HSWOA-DLGCD method than other recent methods.

Identification and characterization of ACR gene family in maize for salt stress tolerance.

Background: Members of the ACR gene family are commonly involved in various physiological processes, including amino acid metabolism and stress responses. In recent decades, significant progress has been made in the study of ACR genes in plants. However, little is known about their characteristics and function in maize. Methods: In this study, ACR genes were identified from the maize genome, and their molecular characteristics, gene structure, gene evolution, gene collinearity analysis, cis-acting elements were analyzed. qRT-PCR technology was used to verify the expression patterns of the ZmACR gene family in different tissues under salt stress. In addition, Ectopic expression technique of ZmACR5 in Arabidopsis thaliana was utilized to identify its role in response to salt stress. Results: A total of 28 ZmACR genes were identified, and their molecular characteristics were extensively described. Two gene pairs arising from segmented replication events were detected in maize, and 18 collinear gene pairs were detected between maize and 3 other species. Through phylogenetic analysis, three subgroups were revealed, demonstrating distinct divergence between monocotyledonous and dicotyledonous plants. Analysis of ZmACR cis-acting elements revealed the optional involvement of ZmACR genes in light response, hormone response and stress resistance. Expression analysis of 8 ZmACR genes under salt treatment clearly revealed their role in the response to salt stress. Ectopic overexpression of ZmACR5 in Arabidopsis notably reduced salt tolerance compared to that of the wild type under salt treatment, suggesting that ZmACR5 has a negative role in the response to salt stress. Conclusion: Taken together, these findings confirmed the involvement of ZmACR genes in regulating salt stress and contributed significantly to our understanding of the molecular function of ACR genes in maize, facilitating further research in this field.