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.

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.

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.

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.

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.

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.

Analyzing the impact of pesticides on the indus river: contamination levels in water, sediment, fish, and associated human health risks.

Pesticides are frequently used to protect crop yields and manage malaria vectors; however, their inadvertent transport into aquatic habitats poses a significant concern. Various anthropogenic activities influence the Indus River in Pakistan. This study aimed to assess the presence of eight pesticide residues at three different sites (Kalabagh, Kundian, and Chashma) in water, sediment, and the fish species (Labeo rohita) during both dry and wet seasons to measure the intensity of this pressure. Pesticide analysis was carried out using gas chromatography equipped with an electron capture detector. The results revealed the highest concentrations of pesticides during both dry and wet seasons at all sites, measuring 0.83 and 0.62 µg/l (water), 12.37 and 9.20 µg/g/dw (sediment), and 14.27 and 11.29 µg/g/ww (L. rohita), respectively. Overall, pesticide concentrations were higher in the dry season than in the wet season across all study sites. Based on detection frequency and concentration in both seasons at all sites, dominant pesticides included cypermethrin and carbofuran (in water), as well as endosulfan and cypermethrin (in sediment and fish tissue). Levels of endosulfan and cypermethrin exceeded standard limits. Moreover, principal component analysis (PCA) indicated no correlation among pesticides in fish tissue, sediment, and water. However, pesticides exhibited different behavior in different seasons. Furthermore, endosulfan and triazophos impose great human health risk, as indicated by the THQ value (> 1). The overall HI value was greater for site 1 in the dry season (8.378). The study concluded that the presence of agricultural pesticides in the Indus River poses a risk to aquatic life and has the potential to disrupt the entire food chain. This highlights the importance of sustainable practices for the study area and Pakistan overall agricultural and environmental sustainability. It is further recommended to strengthen regulations for reduced pesticide use and promote eco-friendly pest management.

Bacillus subtilis: current and future modification strategies as a protein secreting factory.

Bacillus subtilis is regarded as a promising microbial expression system in bioengineering due to its high stress resistance, nontoxic, low codon preference and grow fast. The strain has a relatively efficient expression system, as it has at least three protein secretion pathways and abundant molecular chaperones, which guarantee its expression ability and compatibility. Currently, many proteins are expressed in Bacillus subtilis, and their application prospects are broad. Although Bacillus subtilis has great advantages compared with other prokaryotes related to protein expression and secretion, it still faces deficiencies, such as low wild-type expression, low product activity, and easy gene loss, which limit its large-scale application. Over the years, many researchers have achieved abundant results in the modification of Bacillus subtilis expression systems, especially the optimization of promoters, expression vectors, signal peptides, transport pathways and molecular chaperones. An optimal vector with a suitable promoter strength and other regulatory elements could increase protein synthesis and secretion, increasing industrial profits. This review highlights the research status of optimization strategies related to the expression system of Bacillus subtilis. Moreover, research progress on its application as a food-grade expression system is also presented, along with some future modification and application directions.

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.].

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.

Atto-Scale Noise Near-Infrared Organic Photodetectors Enabled by Controlling Interfacial Energetic Offset through Enhanced Anchoring Ability.

The near-infrared (NIR) sensor technology is crucial for various applications such as autonomous driving and biometric tracking. Silicon photodetectors (SiPDs) are widely used in NIR applications; however, their scalability is limited by their crystalline properties. Organic photodetectors (OPDs) have attracted attention for NIR applications owing to their scalability, low-temperature processing, and notably low dark current density (JD), which is similar to that of SiPDs. However, the still high JD (at NIR band) and few measurements of noise equivalent powers (NEPs) pose challenges for accurate performance comparisons. This study addresses these issues by quantitatively characterizing the performance matrix and JD generation mechanism using electron-blocking layers (EBLs) in OPDs. The energy offset at an EBL/photosensitive layer interface determines the thermal activation energy and directly affects JD. A newly synthesized EBL (3PAFBr) substantially enhances the interfacial energy barrier by forming a homogeneous contact owing to the improved anchoring ability of 3PAFBr. As a result, the OPD with 3PAFBr yields a noise current of 852 aA (JD = 12.3 fA cm⁻2 at V → -0.1 V) and several femtowatt-scale NEPs. As far as it is known, this is an ultralow of JD in NIR OPDs. This emphasizes the necessity for quantitative performance characterization.

Mycophenolate mofetil vs. cyclophosphamide-based induction regimens for lupus nephritis: Outcomes at a tertiary care centre in Lahore, Pakistan.

Objectives: To compare the efficacy of mycophenolate mofetil with intravenous cyclophosphamideas induction therapy in lupus nephritis. METHODS: The observational, prospecrive, cohort study was conducted at the Rheumatology Department of Fatima Memorial Hospital, Lahore, Pakistan, from July 2016 to June 2019, and comprised lupus nephritis patients. For induction therapy, the patients were assigned at the discretion of the treating rheumatologist to mycophenolate mofetil group MMF, and intravenous cyclophosphamide group CYC. The latter group was further divided into NIH subgroup that received the therapy as per the protocol of the National Institutes of Health, and ELNT subgroup which recived the therapy as per the Euro Lupus Nephritis Trial protocol. Maintenance therapy in all groups was mycophenolate mofetil. Tacrolimus was added in case of non-response. The outcome was the achievement of complete renal response at 6, 12 and 24 months. Data was analysed using SPSS 26. RESULTS: Of the 131 patients, 126(96.2%) were females. The overall mean age was 27±7.7 years. There were 58(44.2%) patients in group MMF and 73(55.7%) in group CYC, which had subgroup NIH 46(63%) and subgrpup ELNT 27(37%). The complete renal response rates at 6, 12, and 24 months were 22 (43.1%), 35 (71.4%), and 40(83.3%) for group MMF; 5(12.5%), 9(22%) and 24 (58.5%) for subgroup NIH, and 6(26.1%), 8(36.4%) and 14(63.6%) for subgroup ELNT. Group MMF outcomes were significantly better than the rest (p<0.05). CONCLUSIONS: Mycophenolate mofetil induction therapy was more effective than intraveenous cyclophosphamide in terms of achieving remission at 6, 12 and 24 months.

Interplay between leadership and patient safety in dentistry: a dental hospital-based cross-sectional study.

OBJECTIVES: The study aimed to study the association of leadership practices and patient safety culture in a dental hospital. DESIGN: Hospital-based, cross-sectional study SETTING: Riphah Dental Hospital (RDH), Islamabad, Pakistan. PARTICIPANTS: All dentists working at RDH were invited to participate. MAIN OUTCOME MEASURES: A questionnaire comprised of the Transformational Leadership Scale (TLS) and the Dental adapted version of the Medical Office Survey of Patient Safety Culture (DMOSOPS) was distributed among the participants. The response rates for each dimension were calculated. The positive responses were added to calculate scores for each of the patient safety and leadership dimensions and the Total Leadership Score (TLS) and total patient safety score (TPSS). Correlational analysis is performed to assess any associations. RESULTS: A total of 104 dentists participated in the study. A high positive response was observed on three of the leadership dimensions: inspirational communication (85.25%), intellectual stimulation (86%), and supportive leadership (75.17%). A low positive response was found on the following items: 'acknowledges improvement in my quality of work' (19%) and 'has a clear sense of where he/she wants our unit to be in 5 years' (35.64%). The reported positive responses in the patient safety dimensions were high on three of the patient safety dimensions: organisational learning (78.41%), teamwork (82.91%), and patient care tracking/follow-up (77.05%); and low on work pressure and pace (32.02%). A moderately positive correlation was found between TLS and TPSS (r=0.455, p<0.001). CONCLUSIONS: Leadership was found to be associated with patient safety culture in a dental hospital. Leadership training programmes should be incorporated during dental training to prepare future leaders who can inspire a positive patient safety culture.

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.].

Personality and teachers' burnout stress: exploring the digital competence as personal job resource in allied health institutions.

Introduction: Job burnout has severe consequences for teachers and students. This study aimed to measure the direct effects of personality traits on job burnout-stress syndrome among allied health educators. Furthermore, teachers' digital competence was evaluated as a personal job resource for mitigating the negative impact of burnout. Methods: This study examined direct relationships between work-related stress syndrome and personality traits, namely, extroversion, agreeableness, conscientiousness, emotional stability, and openness to experience. Data was collected from 334 allied health institution teachers through a self-reported questionnaire. Linear regression analysis was used to test for direct effects. Moderating effects were evaluated using Andrew F Hays PROCESS macro v2.16.3. Results: All five personality traits had a significant negative relationship to burnout and teachers' digital competence moderated the relationship between personality traits and burnout. This study's findings provide evidence, that personality is significantly related to job burnout among allied health educators. Conclusion: These empirical findings conclude that personality traits are related to burnout in the non-Western culture of Pakistan. Furthermore, teachers' digital competence acts as a personal job resource and potential moderator in the current digital working environment. Therefore, future teachers should enrich their digital competencies for improved performance, and advanced digital competency courses should be included in their curriculum.

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.

An Automated Multi-scale Feature Fusion Network for Spine Fracture Segmentation Using Computed Tomography Images.

Spine fractures represent a critical health concern with far-reaching implications for patient care and clinical decision-making. Accurate segmentation of spine fractures from medical images is a crucial task due to its location, shape, type, and severity. Addressing these challenges often requires the use of advanced machine learning and deep learning techniques. In this research, a novel multi-scale feature fusion deep learning model is proposed for the automated spine fracture segmentation using Computed Tomography (CT) to these challenges. The proposed model consists of six modules; Feature Fusion Module (FFM), Squeeze and Excitation (SEM), Atrous Spatial Pyramid Pooling (ASPP), Residual Convolution Block Attention Module (RCBAM), Residual Border Refinement Attention Block (RBRAB), and Local Position Residual Attention Block (LPRAB). These modules are used to apply multi-scale feature fusion, spatial feature extraction, channel-wise feature improvement, segmentation border results border refinement, and positional focus on the region of interest. After that, a decoder network is used to predict the fractured spine. The experimental results show that the proposed approach achieves better accuracy results in solving the above challenges and also performs well compared to the existing segmentation methods.