Search Results (42)

The application of image recognition technology plays a vital role in agricultural disease identification. Existing approaches primarily rely on image classification, object detection, or semantic segmentation. However, a major challenge in current semantic segmentation methods lies in accurately identifying small target objects. In this study, common tobacco leaf diseases—such as frog-eye disease, climate spots, and wildfire disease—are characterized by small lesion areas, with an average target size of only 32 pixels. This poses significant challenges for existing techniques to achieve precise segmentation. To address this issue, we propose integrating two attention mechanisms, namely cross-feature map attention and dual-branch attention, which are incorporated into the semantic segmentation network to enhance performance on small lesion segmentation. Moreover, considering the lack of publicly available datasets for tobacco leaf disease segmentation, we constructed a training dataset via image splicing. Extensive experiments were conducted on baseline segmentation models, including UNet, DeepLab, and HRNet. Experimental results demonstrate that the proposed method improves the mean Intersection over Union (mIoU) by 4.75% on the constructed dataset, with only a 15.07% increase in computational cost. These results validate the effectiveness of our novel attention-based strategy in the specific context of tobacco leaf disease segmentation. Full article

Background/Objectives: Deep brain stimulation (DBS) is an effective surgical treatment for Parkinson’s Disease (PD) and other movement disorders. Despite its benefits, DBS explantation occurs in 5.6% of cases, with costs exceeding USD 22,000 per implant. Traditional statistical methods have struggled to identify reliable risk factors for explantation. We hypothesized that supervised machine learning would more effectively capture complex interactions among perioperative factors, enabling the identification of novel risk factors. Methods: The Medical Informatics Operating Room Vitals and Events Repository was queried for patients with DBS, adequate clinical data, and at least two years of follow-up (n = 38). Fisher’s exact test assessed demographic and medical history variables. Data were analyzed using Anaconda Version 2.3.1. with pandas, numpy, sklearn, sklearn-extra, matplotlin. pyplot, and seaborn. Recursive feature elimination with cross-validation (RFECV) optimized factor selection was used. A multivariate logistic regression model was trained and evaluated using precision, recall, F1-score, and area under the curve (AUC). Results: Fisher’s exact test identified chronic pain (p = 0.0108) and tobacco use (p = 0.0026) as risk factors. RFECV selected 24 optimal features. The logistic regression model demonstrated strong performance (precision: 0.89, recall: 0.86, F1-score: 0.86, AUC: 1.0). Significant risk factors included tobacco use (OR: 3.64; CI: 3.60–3.68), primary PD (OR: 2.01; CI: 1.99–2.02), ASA score (OR: 1.91; CI: 1.90–1.92), chronic pain (OR: 1.82; CI: 1.80–1.85), and diabetes (OR: 1.63; CI: 1.62–1.65). Conclusions: Our study suggests that supervised machine learning can identify risk factors for early DBS explantation. Larger studies are needed to validate our findings. Full article

Science has made significant progress in detecting reactive oxygen species (ROS) in tobacco smoke, which is an important step for precision cancer therapy. An important advance is also the understanding that superoxide can be produced by electrophilic molecules. The dual action of hydrogen peroxide, directly or via electrophilic molecules, in the development of oxidative stress allows for the identification of target proteins that can potentially stop unwanted signals in cancer development. However, despite advances in proteomics, reliable inhibitors to stop ROS-associated cancer progression have not yet been proposed for the treatment of tobacco cigarette smokers. This is likely due to an imperfect understanding of the diversity of molecular mechanisms of anti-ROS action. Fluorescent protein detection in living cells, called in-gel, offers a direct route to a better understanding of the rapid interaction of ROS and electrophilic compounds with targeted proteins. It seemed that the traditional paradigm of pharmaceutical innovation “one drug, one disease” did not solve the problem of tobacco smoking causing cancer. However, among the various therapeutic treatments for tobacco smokers, the best way to combat cancer today is smoking cessation, which fits into the “one-cure” paradigm. Full article

Oral cancer (OC) ranks among the most prevalent head and neck cancers, becoming the eleventh most common cancer worldwide with ~350,000 new cases and 177,000 fatalities annually. The rising trend in the occurrence of OC among young individuals and women who do not have tobacco habits is escalating rapidly. Surgical procedures, radiation therapy, and chemotherapy are among the most prevalent treatment options for oral cancer. To achieve better therapy and an early detection of the cancer, it is essential to understand the disease’s etiology at the molecular level. Saliva, the most prevalent body fluid obtained non-invasively, holds a collection of distinct non-coding RNA pools (ncRNAomes) that can be assessed as biomarkers for identifying oral cancer. Non-coding signatures, which are transcripts lacking a protein-coding function, have been identified as significant in the progression of various cancers, including oral cancer. This review aims to examine the role of various salivary ncRNAs (microRNA, circular RNA, and lncRNA) associated with disease progression and to explore their functions as potential biomarkers for early disease identification to ensure better survival outcomes for oral cancer patients. Full article

Soybean is crucial to food processing and agricultural output. However, pests and diseases can easily impact soybeans, reducing their production. Soybean cyst nematode (SCN) is a soilborne pathogen that has a large geographic range, a long lifespan, and the potential to inflict substantial harm to the soybean industry. Persistent use of major resistance genes leads to a progressive loss of resistance; therefore, continuous identification of new soybean strains and genes is essential for continued sustainable soybean production. In this research, the SCN-resistant and SCN-sensitive germplasm DN-L10 and Heinong 37 were inoculated with SCN 3. After stress treatment, the stressed roots were collected for RNA-Seq analysis. The sequencing results screened out the differentially expressed gene GmbHLH18. The GmbHLH18 gene was cloned, and the overexpression vector pCAMBIA3300-GmbHLH18 was constructed. Agrobacterium infected soybean hairy roots and genetically modified the roots of DN50 soybeans, and transgenic root seedlings were obtained. The transgenically identified root seedlings were transplanted in soil infested with SCN 3, and resistance to root nematodes was determined by magenta staining. The secondary and tertiary structures of the protein, phosphorylation sites, as well as the hydrophilicity related to the GmbHLH18 gene were analyzed. Subsequently, the recombinant subcellular localization vector pCAMBIA1302-GmbHLH18 was employed. Agrobacterium was injected into tobacco leaves, and organelle-specific expression was observed. Finally, stress resistance-related indexes of the roots of overexpressing plants and WT plants under SCN 3 stress were measured. The results showed that overexpression and subcellular localization vectors were successfully constructed and transformed into Agrobacterium K599 and GV3101, respectively. The encoded protein had 1149 amino acids, a molecular weight of 95.76 kDa, an isoelectric point of 5.04, 60 phosphorylation sites, a tertiary structure of a-helix (36.39%), random coil (53.40%), extended chain (8.64%), and corner (1.57%), and was hydrophilic. The protein that the gene encoded was a nuclear-localized protein, according to the results of subcellular localization analysis. Moreover, the Agrobacterium-induced hairy root test revealed that the number of overexpressed pCAMBIA3300-GmbHLH18 transgenic roots in the unit area of DN50 was substantially lower than in the control group, which at first suggested that the gene had partial resistance to SCN 3. Stress resistance-related indexes suggest that the contents of POD, SOD, and proline in the overexpressing root significantly increase after SCN 3 stress, demonstrating that this gene can enhance the plant’s resistance to the SCN 3 pathogen. Future research could focus on further elucidating the molecular mechanism underlying the gene’s resistance to SCN 3 and exploring its potential application in breeding soybean varieties with enhanced resistance. Full article

Alzheimer’s disease (AD) is one of the most common and severe forms of dementia and neurodegenerative disease. As life expectancy increases in line with developments in medicine, the elderly population is projected to increase in the next few decades; therefore, an increase in the prevalence of some diseases, such as AD, is also expected. As a result, until a radical treatment becomes available, AD is expected to be more frequently recorded as one of the top causes of death worldwide. Given the current lack of a cure for AD, and the only treatments available being ones that alleviate major symptoms, the identification of contributing factors that influence disease incidence is crucial. In this context, genetic and/or epigenetic factors, mainly environmental, disease-related, dietary, or combinations/interactions of these factors, are assessed. In this review, we conducted a literature search focusing on environmental factors such as air pollution, toxic elements, pesticides, and infectious agents, as well as dietary factors including various diets, vitamin D deficiency, social factors (e.g., tobacco and alcohol use), and variables that are affected by both environmental and genetic factors, such as dietary behavior and gut microbiota. We also evaluated studies on the beneficial effects of antibiotics and diets, such as the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) and Mediterranean diets. Full article

Black root rot is a dangerous disease affecting many crops. It is caused by pathogens formerly known as Thielaviopsis basicola and then reclassified as two cryptic species, Berkeleyomyces basicola and B. rouxiae. The aim of this study was to perform species identification, morphological characterization, and pathogenicity tests for fungal isolates obtained from tobacco roots with black root rot symptoms in Poland. DNA sequences of the three regions (ITS, ACT, MCM7) were highly similar to the sequences of B. rouxiae deposited in the NCBI database. Phylogenetic analysis confirmed the assignment of the obtained isolates to this species. The cultures of four representative isolates (namely OT2, OT3, WPT7, WPT8) showed a similar structure and gray/brown color of the mycelium, although their growth rate varied from 3.8 to 5.1 mm/day depending on the isolate. The sizes of the endoconidia and chlamydospores showed a considerable variation, although they fit within ranges previously described for B. rouxiae. Pathogenicity tests performed on young tobacco plants grown in the inoculated peat substrate revealed differences among the four isolates. WPT7 demonstrated the lowest level of aggressiveness for tobacco. In contrast, the remaining three isolates caused severe disease symptoms and significantly reduced shoot and root dry weights of the susceptible cultivar Virginia Joyner. A parallel pathogenicity test performed on cultivar VRG 10TL confirmed the effectiveness of black root rot resistance derived from Nicotiana debneyi. Full article

Despite increased availability of effective drug therapy for treatment of heart failure (HF), the morbidity and mortality in chronic heart failure (CHF) are unacceptably high. Therefore, there is an urgent need to ascertain new imaging techniques to identify early sub-clinical forms of cardiac dysfunctions, to guide early relevant treatment. It seems that all the behavioral risk factors—such as tobacco, alcoholism, Western-type diet, sedentary behavior and obesity, emotional disorders, and sleep disorder are associated with early cardiac dysfunction, which may be identified by speckle-tracking echocardiography (STE). Cardiac remodeling can also occur chronologically in association with biological risk factors of CHF, such as diabetes mellitus (DM), hypertension, cardiomyopathy, valvular heart disease, and coronary artery disease (CAD). In these conditions, twisting and untwisting of the heart, cardiac fibrosis, and hypertrophy can be identified early and accurately with 2-Dimentional (2D) and 3D echocardiography (2D echo and 3D echo) with tissue Doppler imaging (TDI), strain imaging via STE, and cardiac magnetic resonance imaging (CMR). Both 2D and 3D echo with STE are also useful in the identification of myocardial damage during chemotherapy and in the presence of risk factors. It is possible that global longitudinal systolic strain (GLS) obtained by STE may be an accurate marker for early identification of the severity of CAD in patients with non-ST segment elevation MI. Left ventricular ejection fraction (LVEF) is not the constant indicator of HF and it is normal in early cardiac dysfunction. In conclusion, this review suggests that GLS can be a useful early diagnostic marker of early or pre-cardiac dysfunction which may be treated by suitable drug therapy of HF along with the causes of HF and adhere to prevention strategies for recurrence. In addition, STE may be a superior clinical tool in the identification of cardiac dysfunction in its early stages compared to ejection fraction (EF) based on conventional echocardiography. Therefore, it is suggested that the chances of either stalling or reversing HF are far better for patients who are identified at an early stage of the disease. Full article

Over the last decade, volumes of international studies have illuminated the potential harms associated with ultra-processed products sold as foods. These potential harms include, but are not limited to, an increased risk of non-communicable diseases, poor mental health, and early mortality. Studies examining such products and health have included top-down methods (e.g., nutritional epidemiology), bottom-up approaches (e.g., animal and pre-clinical mechanistic studies), and human intervention trials. The identification of potential harms associated with high levels of food processing has been aided by the NOVA Food Classification System, developed around 2009. Here, in this perspective essay, we argue that lexicon matters, and the continued reference to such ultra-processed products as “foods” is a barrier to policy-related discourse. Using a historical framework, we contend that the term “ultra-processed food” sits in foundational misalignment with how food has been defined, perceived, deliberated on, engaged with, and experienced by humans over millennia. Moreover, we suggest that language that positions ultra-processed products as “food” is part of a mindset that privileges technology and the continued application of isolated nutrients as a means to remedy deeply rooted socioeconomic problems. In the context of global policy, the parallels between food-like ultra-processed products and tobacco are extraordinary. Full article

A novel deep learning model, DiffuCNN, is introduced in this paper, specifically designed for counting tobacco lesions in complex agricultural settings. By integrating advanced image processing techniques with deep learning methodologies, the model significantly enhances the accuracy of detecting tobacco lesions under low-resolution conditions. After detecting lesions, the grading of the disease severity is achieved through counting. The key features of DiffuCNN include a resolution enhancement module based on diffusion, an object detection network optimized through filter pruning, and the employment of the CentralSGD optimization algorithm. Experimental results demonstrate that DiffuCNN surpasses other models in precision, with respective values of 0.98 on precision, 0.96 on recall, 0.97 on accuracy, and 62 FPS. Particularly in counting tobacco lesions, DiffuCNN exhibits an exceptional performance, attributable to its efficient network architecture and advanced image processing techniques. The resolution enhancement module based on diffusion amplifies minute details and features in images, enabling the model to more effectively recognize and count tobacco lesions. Concurrently, filter pruning technology reduces the model’s parameter count and computational burden, enhancing the processing speed while retaining the capability to recognize key features. The application of the CentralSGD optimization algorithm further improves the model’s training efficiency and final performance. Moreover, an ablation study meticulously analyzes the contribution of each component within DiffuCNN. The results reveal that each component plays a crucial role in enhancing the model performance. The inclusion of the diffusion module significantly boosts the model’s precision and recall, highlighting the importance of optimizing at the model’s input end. The use of filter pruning and the CentralSGD optimization algorithm effectively elevates the model’s computational efficiency and detection accuracy. Full article

The northern region of Portugal has the largest number of companies manufacturing granite and stone products, which has become the region’s trademark. In the municipalities of Marco de Canaveses and Penafiel, the economic activity of this area is important. However, the lack of attractiveness of this activity, combined with the high prevalence of silicosis and tuberculosis in this population, has led to a growing shortage of labor. In order for this project to be the result of collaborative, integral work centered on the people who are the target of health promotion, we used the Participatory Health Research (PHR) approach, based on the PRECEDE-PROCEED model, to implement a mixed-methods study, including participant observation, interviews and document analysis. These data were used to co-create a study design. In 2021, a total of 102 interviews were carried out and self-completion surveys were distributed: the Fantastic Lifestyle Questionnaire (FLQ) and the EQ-5D-3L. Within the scope of occupational health nursing and in the field of action of public health nurses, with the interviews and self-completed surveys carried out, we identified potential focuses for occupational health nursing intervention to promote the health of stone industry workers: adherence to protective measures, energy balance deficit, tobacco and alcohol consumption and access to health services. Data analysis made it possible to assess the prevalence of risk behaviors by order and to involve managers and workers in the co-creation of a health promotion program. The accurate identification of the focuses for nursing intervention not only improves the effectiveness of occupational health services, allowing for targeted interventions adapted to workers’ needs, but also contributes considerably to health promotion in the workplace, resulting in safer working environments, a reduction in occupational diseases and, consequently, a healthier and more productive workforce. This protocol of this study was not registered. Full article

Bacterial wilt, caused by the Ralstonia solanacearum species complex, is one of the most damaging bacterial diseases in tobacco and other Solanaceae crops. In this study, we conducted an analysis and comparison of transcriptome landscape changes in seedling roots of three tobacco BC₄F₅ lines, C244, C010, and C035, with different resistance to bacterial wilt at 3, 9, 24, and 48 h after R. solanacearum infection. A number of biological processes were highlighted for their differential enrichment between C244, C010, and C035, especially those associated with cell wall development, protein quality control, and stress response. Hence, we performed a genome-wide identification of seven cell wall development-related gene families and six heat shock protein (Hsp) families and proposed that genes induced by R. solanacearum and showing distinct expression patterns in C244, C010, and C035 could serve as a potential gene resource for enhancing bacterial wilt resistance. Additionally, a comparative transcriptome analysis of R. solanacearum-inoculated root samples from C244 and C035, as well as C010 and C035, resulted in the identification of a further 33 candidate genes, of which Nitab4.5_0007488g0040, a member of the pathogenesis-related protein 1 (PR-1) family, was found to positively regulate bacterial wilt resistance, supported by real-time quantitative PCR (qRT-PCR) and virus-induced gene silencing (VIGS) assays. Our results contribute to a better understanding of molecular mechanisms underlying bacterial wilt resistance and provide novel alternative genes for resistance improvement. Full article

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people’s lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI’s drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease. Full article

Laryngeal squamous cell cancer (LSCC) is one of the most common malignant tumors of the head and neck region, with a poor survival rate (5-year overall survival 50–80%) as a consequence of an advanced-stage diagnosis and high recurrence rate. Tobacco smoking and alcohol abuse are the main risk factors of LSCC development. An early diagnosis of LSCC, a prompt detection of recurrence and a more precise monitoring of the efficacy of different treatment modalities are currently needed to reduce the mortality. Therefore, the identification of effective diagnostic and prognostic biomarkers for LSCC is crucial to guide disease management and improve clinical outcomes. In the past years, a dysregulated expression of small non-coding RNAs, including microRNAs (miRNAs), has been reported in many human cancers, including LSCC, and many miRNAs have been explored for their diagnostic and prognostic potential and proposed as biomarkers. We searched electronic databases for original papers that were focused on miRNAs and LSCC, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. According to the outcome, 566 articles were initially screened, of which 177 studies were selected and included in the analysis. In this systematic review, we provide an overview of the current literature on the function and the potential diagnostic and prognostic role of tissue and circulating miRNAs in LSCC. Full article

Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity and is linked to tobacco exposure, alcohol consumption, and human papillomavirus infection. Despite therapeutic advances, a lack of molecular understanding of disease etiology, and delayed diagnoses continue to negatively affect survival. The identification of oncogenic drivers and prognostic biomarkers by leveraging bulk and single-cell RNA-sequencing datasets of OSCC can lead to more targeted therapies and improved patient outcomes. However, the generation, analysis, and continued utilization of additional genetic and genomic tools are warranted. Tobacco-induced OSCC can be modeled in mice via 4-nitroquinoline 1-oxide (4NQO), which generates a spectrum of neoplastic lesions mimicking human OSCC and upregulates the oncogenic master transcription factor p63. Here, we molecularly characterized established mouse 4NQO treatment-derived OSCC cell lines and utilized RNA and chromatin immunoprecipitation-sequencing to uncover the global p63 gene regulatory and signaling network. We integrated our p63 datasets with published bulk and single-cell RNA-sequencing of mouse 4NQO-treated tongue and esophageal tumors, respectively, to generate a p63-driven gene signature that sheds new light on the role of p63 in murine OSCC. Our analyses reveal known and novel players, such as COTL1, that are regulated by p63 and influence various oncogenic processes, including metastasis. The identification of new sets of potential biomarkers and pathways, some of which are functionally conserved in human OSCC and can prognosticate patient survival, offers new avenues for future mechanistic studies. Full article