Disease Management Strategies Research Articles

Emergence of genetic diversity and multi-drug resistant Clostridium perfringens from wild birds

BackgroundClostridium perfringens (C. perfringens) is an important zoonotic microorganism that can cause animal and human infections, however information about the prevalence status in wild birds of this pathogenic bacterium is currently limited.ResultIn this study, 57 strains of C. perfringens were isolated from 328 fecal samples of wild birds. All the isolates were identified as type A and 70.18% of the isolates carried the cpb2 gene. Antimicrobial susceptibility testing showed that and 22.80% of the isolates were classified as multidrug-resistant strains. The MLST analysis of the 57 isolates from wild birds was categorized into 55 different sequence types (STs) and clustered into eight clonal complexes (CCs) with an average of 20.1 alleles and the Simpson Diversity index (Ds) of 0.9812, and revealed a high level of genetic diversity within the C. perfringens populations. Interestingly, the isolates from swan goose were clustered in the same CC while isolates from other bird species were more scattered suggesting that a potential difference in genetic diversity among the C. perfringens populations associated with different bird species.ConclusionC. perfringens exhibits a wide range of host adaptations, varying degrees of antimicrobial resistance, and a high degree of genetic diversity in wild birds. Understanding the prevalence, toxin type, antimicrobial resistance, and genetic diversity of C. perfringens in wildlife populations is essential for developing effective strategies for disease control and management.

Management and breeding for rust resistance in legumes

AbstractRust diseases are a major concern in legume production worldwide causing heavy losses especially in developing countries that depend on grain legumes as staple food. Fungal species from Uromyces, Phakopsora and Puccinia genera are the main causal agents of the various legume rust diseases. They induce up to 100% yield losses on susceptible cultivars and are emerging as a substantial threat to global food security. Developing durable resistance to rust has thus become a critical breeding objective alongside efforts to improve cultural and disease management practices. This review specifically focuses on the recent advances in understanding and enhancing genetic rust resistance across diverse legume crops. Key topics covered include: (i) the diversity and host range of the rust species affecting legumes; (ii) the disease management strategies from cultural practices to chemical control; (iii) the available screening methods for identifying new sources of resistance; (iv) the genetic basis of resistance, encompassing both major resistance genes and quantitative trait loci; (v) insights into gene regulation and effector molecules leading to legume-rust interactions; and (vi) emerging genomic-assisted breeding techniques that can accelerate the development of durable rust resistance in legumes. Overall, this review highlights the progress made to date and the remaining challenges in sustainably managing rust diseases across diverse legume crops through integrated approaches spanning pathogen biology, advanced phenotyping, genetic resistance, and molecular breeding.

Changes in blood serum parameters in farmed rainbow trout (Oncorhynchus mykiss) during a piscine lactococcosis outbreak.

The aquaculture sector plays a vital role in global food security, yet it grapples with significant challenges posed by infectious diseases. Piscine lactococcosis is one of the significant threats in rainbow trout aquaculture due to its potential to cause severe economic losses through mortalities, reduced growth rates, and increased susceptibility to other pathogens. It poses challenges in disease management strategies, impacting the sustainability and profitability of rainbow trout farming. The current study focuses on the variations in serum blood parameters of farmed rainbow trout Oncorhynchus mykiss during a lactococcosis outbreak caused by Lactococcus garvieae. Blood samples were collected for biochemical analysis, fish were examined for parasites and bacteria, and DNA from bacterial colonies was PCR-amplified and sequenced for identification. Overall, 13 biochemical parameters, including proteins, enzymes, lipids, chemicals, and minerals, were measured in serum blood samples from both diseased and healthy fish. The results indicate significant alterations in the levels of these parameters during the outbreak, highlighting the impact of infections on the blood profile of farmed rainbow trout. Urea levels were significantly higher in diseased fish compared to controls, and creatinine, phosphorus, and magnesium also showed similar trends. Alanine aminotransferase and total protein levels were higher in control fish. Chloride levels differed significantly between groups. Iron levels were higher in controls and lower in diseased fish. No significant differences were found in other parameters. This study reveals significant changes in serum blood parameters of rainbow trout during a lactococcosis outbreak caused by L. garvieae. These changes highlight the potential of these parameters as tools for monitoring health status, stress, and aquaculture management. Continuous monitoring can provide valuable insights into disease severity and overall fish health, aiding in the development of improved management practices. The presented data contribute to understanding the pathophysiology of piscine lactococcosis and developing effective mitigation strategies for farmed rainbow trout.

Mapping serogroup distribution and seroprevalence of leptospirosis in livestock of Assam, Northeastern State of India: Unveiling uncommon Leptospira serogroups

Leptospirosis is a significant zoonotic disease affecting livestock, leading to reproductive issues and economic losses. Despite its endemic status in India, research has predominantly focused on coastal regions, leaving the North Eastern Region (NER) underexplored. This study aims to investigate the seroprevalence and serogroup distribution of leptospirosis in livestock across Assam, a major state in the North Eastern Region (NER) of India. Serum samples (n=811) from cattle, buffalo, sheep, goats, and pigs were collected between 2016 and 2019 and screened using the Microscopic Agglutination Test (MAT) for 24 serogroups. The overall seroprevalence was 22.9 % (186/811), with highest prevalence in cattle (26.2 %) and buffalo (25 %), followed by small ruminants (19.8 %) and pigs (18.6 %) . Notably, uncommon serovars such as Mini (28.8 %), Manhao (12.4 %), and Cynopteri (7.5 %) were identified, indicating a unique epidemiological pattern in Assam. High seroprevalence was observed in districts like Bongaigaon (66.7 %), Kamrup Metropolitan (50.0 %), and Nalbari (40.0 %), emphasizing the need for targeted intervention strategies. The presence of these uncommon serogroups, typically found in neighbouring countries and other regions, suggests potential transboundary transmission from these countries. This study provides valuable insights into the seroprevalence and serogroup distribution of leptospirosis in Assam's livestock, highlighting the need for region-specific surveillance and control measures. These findings underscore the importance of understanding the local epidemiological landscape to develop effective disease management and prevention strategies, ultimately reducing the impact of leptospirosis in the NER of India.

Corneal endothelium and central corneal thickness changes in patients with type 2 diabetes mellitus

Background Understanding the effect of diabetes mellitus on the corneal endothelium is essential for developing effective strategies for early detection, monitoring, and management of diabetic eye disease. Aim The aim of this study was to investigate the changes in corneal endothelium and central corneal thickness (CCT) in patients with type 2 diabetes mellitus. Patients and methods This is a prospective, nonrandomized, comparative study that included 500 eyes of 500 participants that were divided into two groups: group A: 250 eyes of type 2 diabetic patients and group B: 250 eyes of healthy controls. The diabetic group was further subdivided into two subgroups based on the duration of diabetes (<10 years and >10 years) and glycemic control (uncontrolled diabetes and controlled diabetes). Specular microscopy was performed in all eyes. Results The CCT was significantly higher in the diabetic group (516.93±36.43 μm) than in the control group (503.47±41.89 μm) (P=0.001). However, the two groups were relatively similar in cell density (CD) (cells/mm2), coefficient of variation (%), and hexagonality, with no statistically significant differences (P>0.05 for all). The CD was significantly higher in the patients with a duration of diabetes less than 10 years than those with a duration more than 10 years (2834.04±268.54 vs. 2277.13±244.54 cells/mm2, respectively, P=0.001). In addition, the hexagonality was significantly less in those with the duration of diabetes of more than 10 years (P=0.005). The CD was significantly less in the uncontrolled group than in the controlled group (2292.27±263.4 vs. 2644.69±370.88 cells/mm2, respectively, P=0.001). Conclusion Diabetic patients may exhibit alterations in their corneal parameters, particularly the CCT and endothelial CD. These changes are more in diabetic patients with a disease duration of more than 10 years and those with poor glycemic control.

Evaluation of antifungal activity of plant extracts against plant pathogen associated with sweet orange (Citrus sinensis L.), in Jimma, Western Ethiopia

BackgroundSweet oranges are a vital agricultural commodity, but they are susceptible to various pathogenic fungi that can cause significant economic losses. This study aimed to evaluate the antifungal activity of plant extracts against pathogenic fungi attacking sweet oranges in Jimma, Ethiopia.Materials and methodsVarious plant extracts were obtained from locally available plant species and tested against selected pathogenic fungi using standard in vitro antifungal assays. The results revealed varying degrees of antifungal activity among the plant extracts, with some showing promising inhibitory effects against the targeted fungi. Statistical analysis indicated significant differences in antifungal efficacy among the tested extracts.ResultsThe study found that Penicilium spp., Aspergillus spp., Rhizopus spp., Mucor spp., and Fusarium spp, were the predominant fungal species. The study found that the extracts of Vernonia amygdalina, Ginger oficealae, and Pterolobium stalatam were effective in inhibiting mycelial growth against these pathogens. This suggests the use of these medicinal plants for the preservation and control of sweet orange fruit in most of the Ethiopian region.ConlusionThese findings suggest the potential of certain plant extracts as natural alternatives to synthetic fungicides for managing fungal diseases in sweet orange cultivation. Further research is needed to elucidate the mechanisms of action of these plant extracts and optimize their application in agricultural practices. This study contributes to the development of sustainable and eco-friendly strategies for plant disease management in the region.

Impact of infectious diseases on wild bovidae populations in Thailand: insights from population modelling and disease dynamics.

The wildlife and livestock interface is vital for wildlife conservation and habitat management. Infectious diseases maintained by domestic species may impact threatened species such as Asian bovids, as they share natural resources and habitats. To predict the population impact of infectious diseases with different traits, we used stochastic mathematical models to simulate the population dynamics over 100 years for 100 times in a model gaur (Bos gaurus) population with and without disease. We simulated repeated introductions from a reservoir, such as domestic cattle. We selected six bovine infectious diseases; anthrax, bovine tuberculosis, haemorrhagic septicaemia, lumpy skin disease, foot and mouth disease and brucellosis, all of which have caused outbreaks in wildlife populations. From a starting population of 300, the disease-free population increased by an average of 228% over 100 years. Brucellosis with frequency-dependent transmission showed the highest average population declines (-97%), with population extinction occurring 16% of the time. Foot and mouth disease with frequency-dependent transmission showed the lowest impact, with an average population increase of 200%. Overall, acute infections with very high or low fatality had the lowest impact, whereas chronic infections produced the greatest population decline. These results may help disease management and surveillance strategies support wildlife conservation.

Genetic Structure of Ganoderma boninense Populations Associated with Oil Palm at Neighboring Fields with Different Planting Ages in Malaysia.

Ganoderma boninense is a basidiomycete pathogen of African oil palm (Elaeis guineensis) and the causal agent of basal stem rot (BSR) disease, which is the most destructive fungal disease of oil palm in Southeast Asia. The disease is fatal for infected palms and can result in 50 to 80% losses in oil yields because of a reduction in productive life span and a yield decline of infected oil palms. In this study, G. boninense isolates collected from different locations and planting blocks with different palm ages were molecularly characterized using microsatellite genotyping. Results showed high pathogen genetic diversity (He = 0.67 to 0.74) among planting blocks and between oil palm estates. Two nearby planting blocks with similar planting ages (i.e., 1999 and 2001) had a similar percentage of BSR incidence (>20%) but showed distinct Ganoderma genetic structure as detected using STRUCTURE. Similar results were obtained from another trial site where planting blocks differing in planting age but located only less than 1 km apart showed a diverse genetic background. The pathogen genetic admixture of the oldest planting (>30% BSR incidence) differed significantly from the younger planting (1.8 to 2.8% BSR incidence, breeding trial block), suggesting that the host-pathogen genotype interaction may impact the Ganoderma genetic variation over time. The genetic structure of G. boninense, as revealed in this study, implies positive selection resulting from the pathogen genetic variation, host-pathogen interaction, and possible introductions of novel genetic variants (through spores) from adjacent plantings. These findings offer new insights into the genetic changes of G. boninense over time. The information is essential to design disease management strategies and breeding for BSR resistance in oil palm.

Smart agriculture: An intelligent approach for apple leaf disease identification based on convolutional neural network

AbstractPlant diseases pose a significant threat to global agricultural productivity and food safety. Early detection and accurate identification of these diseases are essential for effective disease management strategies. Traditional plant disease identification mainly relies on manual observation and experienced expert judgement, which has the disadvantages of being time‐consuming, labour‐intensive and low efficiency. Given the above problems, this study proposes a method for identifying apple leaf diseases based on a convolutional neural network combining hybrid attention and bidirectional long short‐term memory (BiLSTM). Appropriate apple leaf disease samples were selected from multiple public data sets to form an experimental data set. Then, the data set is imported into the improved convolutional neural network for training. Based on the original ResNet18 model, a new convolutional neural network, AppleNet, is constructed by adding a hybrid attention module and modifying the classifier structure. The experimental results show that the average recognition accuracy of AppleNet is 94.66%, which is 2.47% higher than that of the ResNet18 network. In addition, the training time of the model is only slightly increased. The ablation experiment further verified the effectiveness of the model modification. Compared with other advanced models in recognition accuracy and model training time, the superiority of AppleNet is confirmed. This study verifies that deep learning has great potential and application prospects in plant disease identification and provides a new technical solution for intelligent and convenient plant disease identification.

Diabetes and Oral Health: Advancements in Prevention, Screening and Treatment of Periodontal Diseases

ABSTRACT Diabetes is a pervasive metabolic disorder that notably impacts various body systems, including oral health. One of the critical manifestations of diabetes in oral health is its exacerbating effect on periodontal diseases. Recent advancements in the prevention, screening, and treatment of periodontal diseases have become increasingly significant due to the bidirectional link between periodontal health and diabetes management. This review explores contemporary strategies and technological innovations in the comprehensive management of periodontal diseases among diabetic patients. It highlights the integration of digital dentistry tools such as artificial intelligence (AI) and teledentistry in enhancing diagnostic precision and treatment outcomes. Preventive measures, including new pharmacological formulations and lifestyle interventions tailored for diabetic individuals, are discussed. Additionally, the review underscores the importance of routine screening protocols that incorporate glycemic control status to refine treatment plans for periodontal therapies. Furthermore, advancements in regenerative therapies, including the application of growth factors and stem cell therapy, are examined for their potential to restore periodontal tissue integrity, offering promising directions for future research and clinical practice. Through a synthesis of recent literature, this review aims to provide insights into effective periodontal disease management strategies that are critical for improving the overall health and quality of life of individuals with diabetes.

Sheath Blight and Bacterial Blight Resistance in Rice: Mechanisms, Progress and Future Perspectives for Sustainable Rice Production

Rice (Oryza sativa L.) is a staple food for nearly half of the world's population, including Pakistan. But several diseases are always posing a threat to rice farming, with bacterial blight and sheath blight being the two most common causes. R. solani is the main source of rice sheath blight and stood crop’s most destructive diseases. Reviewing published data on pathogenicity and disease management is crucial to developing effective crop protection against sheath blight. It also helps identify research gaps that need more in-depth investigation. Although there has been progress in identifying rice and pathogen-related genes linked to pathogenesis, the underlying processes are still unknown. Research on the applications of; agronomic techniques, chemical control, biological control, and genetic improvement in disease management strategies has been conducted. Optimizing the application of nitrogen fertilizer in combination to plant-plant spacing can minimize the transmission of infection, whereas SMART agricultural technologies; like crop monitoring using Unmanned Aerial Systems help detect and treat sheath blight disease early on. Biological agents and natural fungicides can be used to effectively prevent sheath blight while reducing the negative effects on the environment. Genetic strategies that hold potential to control sheath blight include the use of exogenous dsRNA to suppress pathogen gene expression, genome editing to create rice lines less susceptible to sheath blight, and the development of transgenic rice lines that over express or silence genes related to pathogenesis. The pathogen’s flexibility, the absence of resistant rice types, the lack of single resistance genes for breeding, and farmers' restricted access to informative programs about optimum management methods all work against effective crop protection against sheath blight.

The Role of Hemoglobin in Maintaining Health: A Literature Review

The role of hemoglobin in maintaining health is still not fully understood Thoroughly. Therefore, this literature review aims to uncover comprehensively the role of hemoglobin in maintaining health. Objective: The purpose of this literature review is to investigate the role of hemoglobin in maintaining health by collecting and analyzing current findings in the scientific literature. The methodology used is a systematic search through academic databases such as PubMed, Google Scholar, and Scopus using relevant keywords such as "hemoglobin", "health maintenance", and "literature review". Articles that meet the inclusion criteria are selected for analysis. The samples in this review literature are scientific articles published within a certain time frame and focus on the role of hemoglobin in maintaining health. The population studied includes humans of various age groups and health conditions. The results of the analysis stated that hemoglobin plays an important role in maintaining oxygen balance in the body, supporting the function of vital organs, and preventing diseases related to hemoglobin disorders. Factors such as nutrition, environment, and health conditions can affect a person's hemoglobin levels. In conclusion, this literature review confirms that a deep understanding of hemoglobin's role in maintaining health is important for the development of prevention and management strategies for diseases associated with hemoglobin disorders. Thus, further research in this area is needed to improve general well-being.

Chronic Kidney Disease Prediction Using Machine Learning

Abstract: Chronic kidney disease (CKD) is a progressive condition in which the kidneys lose their ability to function effectively over time. Individuals with hypertension, diabetes, or a family history of CKD are at increased risk, emphasizing the importance of early detection for effective intervention and management. Recent research has focused on employing machine learning techniques, including Ant Colony Optimization (ACO) and Support Vector Machine (SVM) classifiers, to predict CKD presence using a minimal set of features. This study aims to optimize predictive accuracy through advanced machine learning methodologies, facilitating timely and targeted healthcare interventions for at-risk individuals. By analyzing relevant clinical data, predictive models developed in this research offer promising avenues for early identification of CKD, enabling proactive disease management strategies. The integration of machine learning techniques in CKD prediction not only enhances predictive accuracy but also contributes to advancing personalized healthcare. Early detection allows for the implementation of preventive measures and personalized treatment plans, ultimately improving patient outcomes and reducing the burden on healthcare systems. The proposed methodology seeks to optimize predictive accuracy, aiding in timely and targeted healthcare interventions.

DURIAN STEM CANKER STATUS AND RELATIVE SOIL PROPERTIES IN PENINSULAR MALAYSIA

The arising stem canker issues can be devastating when new durian orchards are developed without proper disease management strategies. Hence, this study aims to obtain the current durian stem canker incident rates in Peninsular Malaysia including soil properties. 15 plots (2,500 m2 per plot) of durian orchards were evaluated and composite soil samples were analysed. Results showed that the greatest disease incidence (100%) was in old orchards (> 10 years), with a severity of 63% whereas young orchards (< 10 years) are at risk of severe stem canker with 71% of incidence and 43% of severity. There was a negative correlation (r = –0.220) between the incidence of stem canker and soil pH which indicated that durian trees grown in acidic soil (pH < 5.0) were more prone to disease dispersion in the orchards. Moreover, severely infected trees were observed more in durian orchards with a higher proportion of finer sand particles in the soil (DI/ r = 0.199 & DSI/r = 0.206) compared to coarse particles of clay and silt. Our work will play a crucial role in advancing real-time monitoring of durian stem cankers and enabling the prediction of potential disease outbreaks for the implementation of preventive measures.

A Novel Approach for Red Rot Disease Detection in Sugarcane Plants Using Transfer Learning Based VGG16 Model

Red rot in sugarcane is a devastating fungal disease caused by Colletotrichum falcatum. It leads to wilting, red discoloration, and yield losses, which can impact sugar production globally. Red rot hampers sugarcane production, causing yield reductions, and economic losses, and affecting the nation’s sugar industry demanding stringent disease management strategies. There is a need of a system that detects the red rot disease at an early stage before it spreads. Transfer learning enhances agricultural models, leveraging pre-trained data for improved crop predictions, resource optimization, and sustainability. In this article, we have proposed a transfer learning-based VGG16 model for the detection of red rot disease in sugarcane on a large dataset. The model achieves a remarkable 98.85% accuracy and an F1 score of 0.93 in early red rot detection in sugarcane. This work underscores the potency of leveraging re-trained models for crop disease identification, offering a promising avenue for proactive disease management. This research marks a substantial step towards enhancing crop yield and sustainability, presenting an accessible and impactful technological solution for early disease detection in sugarcane, ultimately benefiting agricultural practices.

Seroprevalence of Maedi-Visna Infection in Sheep in the Central Black Sea Region of Türkiye

Maedi-Visna is a slowly progressive infection of sheep that affects the respiratory and nervous systems and causes significant yield losses worldwide. This study was conducted to assess the seroprevalence of Maedi-Visna infection in sheep across several provinces in the Central Black Sea region of Türkiye, namely Samsun, Sinop, Ordu, Giresun, Amasya and Tokat. A total of 476 sheep serum samples were collected for further analysis. The presence of antibodies in the sera was determined by Ab ELISA. As a result of the study, 263 (55.25%) blood serum samples were positive for the presence of MVV antibodies. Seropositivity rates by province were as follows: Samsun 45%, Sinop 80%, Ordu 58.75%, Giresun 35%, Amasya 38.75%, Tokat 75%. Notably, our findings suggest a significantly high seroprevalence of Maedi-Visna infection in this region compared to other studies conducted in different parts of Türkiye This information is pivotal for understanding the extent of the infection in the sheep population of the Central Black Sea region and can contribute to the development of strategies for disease management and control.

Varietal Susceptibility of Olive to Pseudomonas savastanoi pv. savastanoi and the Antibacterial Potential of Plant-Based Agents.

Olive knot disease, caused by the bacterium Pseudomonas savastanoi pv. savastanoi, causes great damage in olive orchards. While control measures of P. savastanoi pv. savastanoi in olive orchards primarily rely on pruning and copper-based treatments, the use of antibiotics as bactericidal preparations in agriculture is limited and highly restricted. However, plants are naturally endowed with protective molecules, such as phenolic compounds, which defend them against herbivores, insects, and microorganisms. This research aimed to test the virulence of five strains of P. savastanoi pv. savastanoi isolated from different growing regions and olive varieties, and to examine whether there is a difference in plant susceptibility based on the variety. An additional goal was to test the antimicrobial activity of olive mill wastewater, known for its high content of phenolic compounds, and aqueous garlic hydrolysate, as well as to compare them with a commercial copper-based product, pure hydroxytyrosol, and a standard antibiotic as references. Analysis of knot characteristics showed variations in the virulence of the P. savastanoi pv. savastanoi strains, with the highest virulence being observed for the strain I7L and the lowest virulence for the strain B45C-PR. The olive cultivar Rosinjola displayed higher susceptibility compared to Frantoio, Buža, and Leccino, while cv. Istarska bjelica exhibited the least susceptibility compared to the other investigated olive cultivars. In an attempt to explore alternative solutions for disease control, in vitro tests revealed that the phenol HTyr, GE, and the wastewater with the highest total phenolic content (cv. Istarska bjelica) possess the highest antibacterial activity. This supports the role of polyphenols in host defense, aligning with previous field observations of lower susceptibility of cv. Istarska bjelica to olive knot disease. These findings highlight the complex nature of olive knot interactions with bacterial strains and olive cultivars, simultaneously accentuating and underscoring the importance of considering the host's defenses as well as bacterial virulence in disease management strategies.

MicroRNA-29b attenuates fibrosis in a rat model of Peyronie's disease.

Peyronie's disease is characterized by the formation of fibrotic plaques in the penile tunica albuginea. Effective treatments are limited, warranting the investigation of new promising therapies, such as the application of microRNAs that regulate fibrosis-related genes. We aimed to investigate the therapeutic potential of mimicking microRNA-29b in a fibrin-induced rat model of Peyronie's disease. The study was designed in two phases. To establish an optimal Peyronie's disease model, rats received either human fibrin and thrombin or saline solutions into the tunica albuginea on days 0 and 5. The animal model validation was done through expression and histopathological analyses, the latest by an experienced uropathologist. After validation, we performed microRNA-29b treatment on days 14, 21, and 28 of the study. This phase had control (normal saline) and scramble (microRNA scramble) groups. The mid-penile shaft was removed on day 30 for histological examination and molecular analyses in both study stages. The control group displayed typical tunica albuginea histologic architecture in the animal model validation. In Peyronie's disease group, the Hematoxylin and eosin and Masson Trichrome staining methods demonstrated an interstitial inflammatory process with concomitant dense fibrotic plaques as well as disarrangement of collagen fibers. Additionally, we found out that reduced microRNA-29b (p=0.05) was associated with significantly increased COL1A1 and transforming growth factor β1 genes and proteins (p>0.05) in the Peyronie's disease group. After treatment with mimic microRNA-29b stimulation, the Hematoxylin & eosin and Masson Trichrome staining revealed a discrete and less dense fibrotic plaque. This result was associated with significantly decreasing expression of COL1A1, COL3A1, and transforming growth factor β1 genes and proteins (p<0.05). The fibrin-induced animal model showed significant histopathological and molecular changes compared to the Control group, suggesting that our model was appropriate. Previous findings have shown that increased expression of microRNA-29b was associated with decreased pathological fibrosis. In the present study, treatment with microRNA-29b decreased the gene and protein expression of collagens and transforming growth factor β1. This study reveals the therapeutic potential for Peyronie's disease involving molecular targets. MicroRNA-29b application on the rat's tunica albuginea attenuated fibrosis, arising as a novel potential strategy for Peyronie's disease management.

Characterizing genetic diversity of Sclerotium rolfsii isolates by biomapping of mycelial compatibility groupings and multilocus sequence analysis.

Genetic diversity in Sclerotium rolfsii is useful for understanding its population structure, identifying different mycelial compatibility groups (MCGs), and developing targeted strategies for disease management in affected crops. In our study, a comprehensive genetic analysis was conducted on 50 isolates of S. rolfsii, collected from various geographic regions and host plants. Two specific genes, TEF1α and RPB2, were utilized to assess the genetic diversity and relationships among these isolates. Notably, out of 1225 pairings examined, only 154 exhibited a compatible reaction, while the majority displayed antagonistic reactions, resulting in the formation of a barrier zone. The isolates were grouped into 10 distinct MCGs. These MCGs were further characterized using genetic sequencing. TEF1α sequences distinguished the isolates into 17 distinct clusters, and RPB2 sequences classified them into 20 clusters. Some MCGs shared identical gene sequences within each gene, while others exhibited unique sequences. Intriguingly, when both TEF1α and RPB2 sequences were combined, all 10 MCGs were effectively differentiated, even those that appeared identical with single-gene analysis. This combined approach provided a comprehensive understanding of the genetic diversity and relationships among the S. rolfsii isolates, allowing for precise discrimination between different MCGs. The results shed light on the population structure and genetic variability within this plant pathogenic fungus, providing valuable insights for disease management and control strategies. This study highlights the significance of comprehending the varied virulence characteristics within S. rolfsii isolates, categorizing them into specific virulence groups based on disease severity index (DSI) values. The association with MCGs provides additional insights into the genetic underpinnings of virulence in this pathogen. Furthermore, the identification of geographical patterns in virulence implies the influence of region-specific factors, with potential implications for disease control and crop protection strategies.Please confirm if the author names are presented accurately and in the correct sequence (given name, middle name/initial, family name). Author 1 Given name: [G. M. Sandeep] Last name [Kumar]. Author 2 Given name: [Praveen Kumar] Last name [Singh]. Also, kindly confirm the details in the metadata are correct.I confirm that the given names are accurate and presented in the correct sequence.

Challenges and Opportunities Arising from Host-Botrytis cinerea Interactions to Outline Novel and Sustainable Control Strategies: The Key Role of RNA Interference.

The necrotrophic plant pathogenic fungus Botrytis cinerea (Pers., 1794), the causative agent of gray mold disease, causes significant losses in agricultural production. Control of this fungal pathogen is quite difficult due to its wide host range and environmental persistence. Currently, the management of the disease is still mainly based on chemicals, which can have harmful effects not only on the environment and on human health but also because they favor the development of strains resistant to fungicides. The flexibility and plasticity of B. cinerea in challenging plant defense mechanisms and its ability to evolve strategies to escape chemicals require the development of new control strategies for successful disease management. In this review, some aspects of the host-pathogen interactions from which novel and sustainable control strategies could be developed (e.g., signaling pathways, molecules involved in plant immune mechanisms, hormones, post-transcriptional gene silencing) were analyzed. New biotechnological tools based on the use of RNA interference (RNAi) are emerging in the crop protection scenario as versatile, sustainable, effective, and environmentally friendly alternatives to the use of chemicals. RNAi-based fungicides are expected to be approved soon, although they will face several challenges before reaching the market.