Occurrence Of Disease Research Articles

Associations of the Serum KL-6 with Severity and Prognosis in Patients with Acute Exacerbation of Chronic Obstructive Pulmonary Disease.

As a biomarker of alveolar-capillary basement membrane injury, Krebs von den Lungen-6 (KL-6) is involved in the occurrence and development of pulmonary diseases. However, the role of the KL-6 in patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) has yet to be elucidated. This prospective study was designed to clarify the associations of the serum KL-6 with the severity and prognosis in patients with AECOPD. This study enrolled 199 eligible AECOPD patients. Demographic data and clinical characteristics were recorded. Follow-up was tracked to evaluate acute exacerbation and death. The serum KL-6 concentration was measured via an enzyme-linked immunosorbent assay. Serum KL-6 level at admission was higher in AECOPD patients than in control subjects. The serum KL-6 concentration gradually elevated with increasing severity of AECOPD. Pearson and Spearman analyses revealed that the serum KL-6 concentration was positively correlated with the severity score, monocyte count and concentrations of C-reactive protein, interleukin-6, uric acid, and lactate dehydrogenase in AECOPD patients during hospitalization. A statistical analysis of long-term follow-up data showed that elevated KL-6 level at admission was associated with longer hospital stays, an increased risk of future frequent acute exacerbations, and increased severity of exacerbation in COPD patients. Serum KL-6 level at admission is positively correlated with increased disease severity, prolonged hospital stay and increased risk of future acute exacerbations in COPD patients. There are positive dose-response associations of elevated serum KL-6 with severity and poor prognosis in COPD patients. The serum KL-6 concentration could be a novel diagnostic and prognostic biomarker in AECOPD patients.

Dysregulation of the Gut Microbiota Contributes to Sevoflurane-Induced Cognitive Dysfunction in Aged Mice by Activating the NLRP3 Inflammasome.

Postoperative cognitive dysfunction (POCD), a common complication in elderly patients after surgery, seriously affects patients' quality of life. Long-term or repeated inhalation of sevoflurane can cause neuroinflammation, which is a risk factor for POCD. However, the underlying mechanism needs to be further explored. Recent research had revealed a correlation between neurological disorders and changes in the gut microbiota. Dysfunction of the gut microbiota is involved in the occurrence and development of central nervous system diseases. Here, we found that cognitive dysfunction in aged mice induced by sevoflurane exposure (3%, 2 hours daily, for 3 days) was related to gut microbiota dysbiosis, while probiotics improved cognitive function by alleviating dysbiosis. Sevoflurane caused a significant decrease in the abundance of Akkermansia (P<0.05), while probiotics restored the abundance of Akkermansia. Compared to those in the control group, sevoflurane significantly increased the expression of NLRP3 inflammasome-associated proteins in the gut and brain in the sevoflurane-exposed group, thus causing neuroinflammation and synaptic damage, which probiotics can mitigate (con vs. sev, P < 0.01; p+sev vs. sev, P < 0.05). In conclusion, for the first time, our study revealed that dysbiosis of the gut microbiota caused by sevoflurane anesthesia contributes to the NLRP3 inflammasome-mediated neuroinflammation and cognitive dysfunction from the perspective of the gut-brain axis. Perhaps postoperative cognitive impairment in elderly patients can be alleviated or even prevented by regulating the gut microbiota. This study provides new insights and methods for the prevention and treatment of cognitive impairment induced by sevoflurane.

Serum ApoB/ApoA1 ratio in patients with CHB and the occurrence of HBV related cirrhosis and HBV related hepatocellular carcinoma

Clinical research has suggested that chronic HBV infection exerts a certain effect on the occurrence of cardiovascular disease by regulating cholesterol metabolism in liver cells. High serum apolipoprotein B/apolipoprotein A1 (ApoB/ApoA1) ratio plays a certain role in the above regulation, and it serves as a risk factor for cardiovascular disease. However, whether the ApoB/ApoA1 ratio is correlated with chronic HBV infection and its disease progression remains unclear. In accordance with the inclusion and exclusion criteria, all 378 participants administrated at Renmin Hospital of Wuhan University from March 2021 to March 2022, fell into Healthy Control (HC) group (50 participants), Hepatocellular carcinoma (HCC) group (107 patients), liver cirrhosis (LC) group (64 patients), chronic hepatitis B (CHB) group (62 patients), chronic hepatitis C (CHC) group (46 patients) and Hepatitis E Virus (HEV) group (49 patients). Serum ApoA1 and ApoB concentrations were measured at admission, and the ApoB/ApoA1 ratio was determined. The levels of laboratory parameters in the respective group were compared and ApoB/ApoA1 ratios in HCC patients and LC patients with different severity were further analyzed. ROC curves were plotted to analyze the early diagnostic ability of ApoB/ApoA1 ratio for HBV-associated HCC. Logistic regression and restricted cubic spline analysis were used to explore the correlation between ApoB/ApoA1 ratio and LC and HCC risk. A comparison was drawn in terms of ApoB/ApoA1 ratio between the groups, and the result was expressed in descending sequence: HEV group > CHB group > LC group > HCC group > CHC group > HC group, early-stage HCC < middle-stage HCC < advanced-stage HCC, Class A LC < Class B LC < Class C LC. Serum ApoB/ApoA1 ratio combined diagnosis with AFP exhibited the capability of increasing the detection efficacy and specificity of AFP for HCC and AFP-negative HCC. The incidence of LC and HCC in the respective logistic regression model showed a negative correlation with the serum ApoB/ApoA1 ratio in CHB patients (P < 0.05). After all confounding factors covered in this study were regulated, the result of the restricted cubic spline analysis suggested that in a certain range, serum ApoB/ApoA1 ratio showed an inverse correlation with the prevalence of LC or HCC in CHB patients. Serum ApoB/ApoA1 ratio in CHB patients may be conducive to identifying high-risk patients for HCC or LC, such that LC and HCC can be early diagnosed and treated.

The Effect of Saltwater Stress on the Performance of Cherry Tomatoes

Rising sea levels and saltwater intrusion in aquifers pose significant challenges for South Florida agriculture, leading to increased groundwater salinity and potential crop losses. Utilizing salttolerant crop species presents a potential solution for saline soils and regions with active saltwater intrusion. However, the effects of soil salinization through groundwater alone remains less studied. This research investigates the impact of short-term, below-ground saltwater stress on the growth, survival, and overall health of commonly grown cherry tomatoes (Solanum lycopersicum). The objectives of the study are to: 1) determine the impact of saline groundwater on tomato plant health and 2) compare the nutrient content of soil and tomato plant tissue exposed to varying concentrations of saline water. Established cherry tomato plants were exposed to varying concentrations of NaCl solution, simulating saltwater intrusion into groundwater. Over 28 days, plant height, leaf chlorophyll levels, and disease occurrences were monitored. It was found that the NaCl treatments did not significantly affect cherry tomato performance under the parameters of height, chlorophyll levels, or leaf nutrition when compared to the control group. This study suggests that cherry tomatoes can tolerate short-term exposure to NaCl in groundwater. Further exploration of more intense salt stress conditions from groundwater could be beneficial for utilizing this crop in areas with saline soils or polluted groundwater. Identification of salt-tolerant cherry tomato varieties can provide alternative crop options for non-arable land affected by high soil salinity.

A Comprehensive Survey and Assessment of Rice Blast Disease Incidence from Major Rice Growing Districts of Telangana State, India

Aim: The aim of this study was to assess the incidence and severity of rice blast disease caused by Pyricularia grisea in various rice cultivars grown in Telangana State during the kharif season of 2019. Methodology: A purposive disease survey was conducted in major rice-growing areas of Telangana State. Disease incidence and severity were recorded from the tillering to grain hardening stage. Percentage disease incidence and severity were calculated at each sampling location. Additionally, pathogenicity tests were conducted on susceptible rice cultivar TN-1 using different isolates of P. grisea. Results: The study revealed significant variations in rice blast disease incidence and severity across the surveyed areas. Disease incidence ranged from 18.5% in Mancherial to 79.41% in Peddapalli, while severity ranged from 22.2% to 85.1%. All sampled cultivars exhibited varying degrees of susceptibility, with severity scores ranging from moderately resistant (2.0) to highly susceptible (9.0). TN-1 showed the highest blast severity, while WGL 1368 exhibited the least severity. Pathogenicity tests showed significant differences among P. grisea isolates, with reactions on TN-1 ranging from moderately susceptible (4.0 to 5.0) to highly susceptible (8.0 to 9.0). Conclusion: The study highlights the widespread occurrence of rice blast disease in Telangana State and underscores the varying susceptibility levels among rice cultivars. These findings emphasize the importance of continued monitoring and management strategies to mitigate the impact of rice blast disease on rice production in the region.

Childhood obesity accelerates biological ageing: is oxidative stress a link?

Obesity is multifactorial pathophysiological condition with an imbalance in biochemical, immunochemical, redox status and genetic parameters values. We aimed estimate connection between relative leukocyte telomere lengths (rLRL) - biomarker of cellular aging with metabolic and redox status biomarkers values in a group of obese and lean children. The study includes 110 obese and 42 lean children and adolescents, both genders. The results suggested that rLTL are significantly shorter in obese, compared to lean group (p<0,01). Negative correlation of rLTL with total oxidant status, TOS (Spearman's ρ = -0.365, p<0.001) as well as with C reactive protein (Spearman's ρ= -0,363, p<0.001) were observed. Principal component analysis (PCA) extracted three distinct factors (i.e. principal components) entitled as: prooxidant factor with 35% of total variability; antioxidant factor with 30% of total variability and lipid antioxidant - biological ageing factor with 12% of the total variability. The most important predictor of body mass index (BMI) >30kg/m2 according to logistic regression analysis was PCA derived antioxidant factor's score (OR: 1.66, 95th Cl: 1.05-2.6, p=0.029). PCA analysis confirmed oxidative stress importance in biological ageing caused by obesity and its multiple consequences related to prooxidants augmentation and antioxidants exhaustion and gave us clear signs of disturbed cellular homeostasis deepness, even before any overt disease occurrence.

Leveraging machine learning for predicting acute graft-versus-host disease grades in allogeneic hematopoietic cell transplantation for T-cell prolymphocytic leukaemia

Orphan diseases, exemplified by T-cell prolymphocytic leukemia, present inherent challenges due to limited data availability and complexities in effective care. This study delves into harnessing the potential of machine learning to enhance care strategies for orphan diseases, specifically focusing on allogeneic hematopoietic cell transplantation (allo-HCT) in T-cell prolymphocytic leukemia. The investigation evaluates how varying numbers of variables impact model performance, considering the rarity of the disease. Utilizing data from the Center for International Blood and Marrow Transplant Research, the study scrutinizes outcomes following allo-HCT for T-cell prolymphocytic leukemia. Diverse machine learning models were developed to forecast acute graft-versus-host disease (aGvHD) occurrence and its distinct grades post-allo-HCT. Assessment of model performance relied on balanced accuracy, F1 score, and ROC AUC metrics. The findings highlight the Linear Discriminant Analysis (LDA) classifier achieving the highest testing balanced accuracy of 0.58 in predicting aGvHD. However, challenges arose in its performance during multi-class classification tasks. While affirming the potential of machine learning in enhancing care for orphan diseases, the study underscores the impact of limited data and disease rarity on model performance.

Report on Mortalities of Nile Tilapia (Oreochromis niloticus) Due to Tilapinevirus tilapiae and Co-infecting Bacteria in Winter Season

Background: Tilapinevirus tilapiae, commonly known as Tilapia lake virus, is a major viral infection of cultured tilapia, causing severe rates of fatalites and has evolved as a serious hazard to the tilapia in the world. Predisposing factors, notably temperature and the development stage of the host play a decisive role in influencing the extent of pathogenesis and the mortality rate associated with the disease. The seasonality of TiLV outbreaks has been observed in multiple countries, with a higher frequency occurring during the summer months. Methods: In the current investigation, the occurrence of TiLV disease was examined in fry and fingerlings of tilapia during the winter season (January - February, 2020) in Pune District, Maharashtra, India. The disease was observed in fish reared in open cages (Case I) and in nursery rearing farm (Case II). Infected tilapia displayed generalized gross clinical signs. TiLV was identified and confirmed in both instances using a semi-nested reverse transcriptase PCR by amplifying TiLV genome segment 3 and further confirmed by histopathogical investigation. Result: The research demonstrated the vulnerability of early developmemtal stages of O. niloticus to TiLV disease, suggesting the potential occurrence of this infection during the winter season, characterized by lower temperatures. Additionally, microbial investigations exposed the prevalence of Aeromonas spp., predominantly associated with TiLV-positive fish. These results align with previous publications, underscoring the potential role of bacterial infectious agents (Notably aeromonads), in exacerbating TiLV disease during outbreaks.

Molecular genetic analysis of natural introgression to enhance chestnut blight resistance of Castanea henryi var. omeiensis

Castanea species have enormous potential for broad applications in the biomedical sciences and chemical wood industries. Due to the occurrence of fungal diseases, especially chestnut blight, the survival status of Castanea plants is seriously threatened. Castanea plants in China have natural tolerance to chestnut blight. This study investigated the genetic diversity and chestnut blight resistance of Castanea plants in China and searched for germplasm with high tolerance resistance. Cluster and population structure analysis revealed that wild Castanea plants in China could be divided into three groups with significant interspecific boundary, C. henryi var. omeiensis is located between C. mollissima and C. henryi var. henryi. The ABBA-BBAA test showed that significant gene introgression occurred between C. mollissima and C. henryi in Mount Emei, supporting the conclusion that C. henryi var. omeiensis as a natural hybrid resource. The results of disease resistance evaluation showed that the tolerance of wild C. mollissima to chestnut blight was the highest and significantly higher than that of wild C. henryi and C. seguinii. In particular, there is a strong association between disease tolerance in C. henryi var. omeiensis and the level of genetic introgression from C. mollissima. Then, the rIBD fragments of C. henryi var. omeiensis derived from C. mollissima were analyzed to reveal the source of disease resistance using whole genome resequencing data. A total of 203 rIBD segments containing 435 genes were identified. Finally, 41 candidate genes for chestnut blight resistance were selected through multiple omics. In conclusion, this study investigated a type of interspecific disease-resistant hybrid resources, identified a set of candidate genes for chestnut blight resistance, and provided new strategies for disease-resistant breeding of chestnut resources in the future.

Piloting Pulse Producer Support System Through ICT Enabled Services for Enhanced Climate Resilience

Background: In peninsular India, the frequency of climate-related risks is significantly lowering agricultural productivity and farmers’ standard of living. Reducing losses in the agriculture sector requires precise and timely weather information, weather based advisories on crop production, protection and post-harvest. Disseminating agromet advisories directly to the farmers using ICT tools allows farmers to adopt appropriate corrective measures, minimize loss and ensure climate resilient agriculture. In this sense, the Tamil Nadu Agricultural University established an Automated Agromet Advisory Web cum Mobile App (TNAU-AAS) in 2018, which sends location, crop and stage specific agromet advisories directly to farmers’ mobile App. Methods: TNAU-AAS App was evaluated in pulse crops as “Piloting pulse produce support system through ICT enabled services” between 2019 and 2021 in three TNAU centers under three different eco systems namely irrigated (Coimbatore), rice fallow (Aduthurai) and rainfed (Kovilpatti). Pulse growing farmers were registered in this smartphone application for agromet advisory based response farming. Result: The TNAU-AAS App had delivered approximately 10-12 advisories to each farmer, tailored to their unique crop and stage during the cropping period. Survey results inferred that the farmers received timely notifications on their mobile phones, 2-3 days in advance, which assisted them in preplanning. Occurrences of pests and diseases were well aligned (68-83%) with TNAU- AAS recommendations and the climate vulnerability of non AAS farmers is more than AAS farmer. According to partial budget analysis, the irrigated, rice fallow and rainfed black gram farmers have gained Rs. 2800, Rs. 2300 and Rs. 1900 per ha, respectively. It is concluded that the ICT-enabled agromet advisory system guarantees timely alerts, boosts labor and land productivity, lowers input loss and shields crops from weather-induced pests and diseases as well as climatic variability risk.

A Comparative Study between Time Series and Machine Learning Technique to Predict Dengue Fever in Dhaka City

The dengue virus is the most dangerous one that mosquitoes may spread to people. Despite attempts by the government, dengue outbreaks are becoming increasingly common in Bangladesh. Interventions in public health rely heavily on KAP (knowledge, attitude, and practice) studies. The primary goal of this research is to forecast the occurrence of dengue disease in the city of Dhaka using methods from machine learning and time series analysis and then to compare the models in order to find the one with the lowest MAPE. From January 2016 through July 2021, monthly data were retrieved for this study from WHO and the Directorate General of Health and Services (DGHS). According to the findings of this research, neural networks outperform time series analysis when it comes to making predictions. The best-fitted neural network (NN) model was found in model 04 with 05 hidden layers which produced the minimum error model with the value of error 0.003032557, and the values of RMSE and MAPE are 7.588889e − 06 and 1.15273, respectively, for the prediction of the dengue fever in Dhaka city. In contrast, the original dengue data in the time series analysis is not stationary. Take the difference and run the unit root test by the augmented Dickey–Fuller (ADF) test to make it stationary. The dengue data series is stationary at the first-order difference, as evidenced by the ACF and PACF, which show no noticeable spike in the first-order difference. The ARIMA (6, 1, 1) model with the lowest AIC = −251.8, RMSE = 0.0310797, and MAPE = 15.2892 is the best choice model for predicting the dengue death rate. Therefore, from these two models, the NN model gives better prediction performance with the lowest value of MAPE. So, the neural network gives better prediction performance than time series analysis. The NN model forecasted 12-month death rates of dengue fever that suggest the death rate in dengue fever falling month by month. This study is more innovative than any other research because this research approach is different from any other research approach. The model selection criteria are based on the most effective performance metrics MAPE, indicating the lowest error and better prediction performance. Therefore, from this research, the author suggests machine learning gives better prediction performance than time series analysis for any other prediction performance.

Tracing Geographic and Molecular Footprints of Copepod Crustaceans Causing Multifocal Purple Spots Syndrome in the Caribbean Sea Fan Gorgonia ventalina

The recent rise in ocean temperatures, accompanied by other environmental changes, has notably increased the occurrence and spread of diseases in Octocorallia, many species of which are integral to shallow tropical and subtropical coral reef ecosystems. This study focuses on the understanding of these diseases, which has been largely limited to symptomatic descriptions, with clear etiological factors identified in only a fraction of cases. A key example is the multifocal purple spots syndrome (MFPS) affecting the common Caribbean octocoral sea fan Gorgonia ventalina, linked to the gall-forming copepods of the genus Sphaerippe, a member of the widespread family, Lamippidae. The specialized nature of these copepods as endoparasites in octocorals suggests the potential for the discovery of similar diseases across this host spectrum. Our investigation employed four molecular markers to study disease hotspots in Saint Eustatius, Curaçao, northwest and southwest Cuba, and Bonaire. This led to the discovery of a group of copepod species in these varied Caribbean locations. Importantly, these species are morphologically indistinguishable through traditional methods, challenging established taxonomic approaches. The observed diversity of symbionts, despite the host species’ genetic uniformity, is likely due to variations in larval dispersal mechanisms. Our phylogenetic analyses confirmed that the Lamippidae copepods belong to the order, Poecilostomatoida (Copepoda), and revealed their sister group relationship with the Anchimolgidae, Rhynchomolgidae, and Xarifiidae clades, known for their symbiotic relationships with scleractinian corals. These results add to our understanding of the evolutionary and ecological interactions of copepods and their hosts, and the diseases that they cause, and are important data in a changing climate.

Predictors of cardiovascular risk in familial hypercholesterolemia.

Familial Hypercholesterolemia is associated with an increased risk of cardiovascular disease. The current international guidelines of the main scientific societies consider that all people with familial hypercholesterolemia have a high or very high cardiovascular risk. However, the occurrence of atherosclerotic cardiovascular disease is very heterogeneous in this population. Stratifying risk within people with familial hypercholesterolemia is essential to identify individuals who require intensive cholesterol-lowering therapies. In the last year, several studies have been published focusing on the contribution of diabetes to familial hypercholesterolemia, the role of stroke, as a manifestation of atherosclerotic disease, and the external validation of the SAFEHEART risk equation in the English population diagnosed with Familial Hypercholesterolemia. It is necessary the development of a tool that allows us to identify, in a simple, reproducible, and universal way, patients who may have a high risk of suffering a cardiovascular event and who are susceptible to more intensive treatments to reduce cholesterol levels.

Root exudate-mediated plant–microbiome interactions determine plant health during disease infection

Understanding the interactions between plants and microbes during pathogen infection is crucial for plant health improvement. However, the integrated role of soil nutrients, root exudates, and plant microbiomes in plant health during disease outbreaks remains unclear. In this study, we hypothesized that root exudate-mediated healthy microbiomes (HealthyBiome) would facilitate the escape of soil-borne pathogen infection, whereas a root exudate-mediated disease-conductive microbiome (PathoBiome) would induce disease occurrence. Using watermelon wilt disease caused by Fusarium oxysporum as a model, we investigated these interactions and their implications in plant disease. We examined the pathogen load across plant compartments during the pathogen accumulation (AH) and disease outbreak periods. We analyzed the plant microbiomes and root exudates during the outbreak, which was associated with the emergence in both healthy (BH) and diseased (BD) plants. Compared with AH and BD plants, BH plants had the lowest F. oxysporum density in the root–soil system (0.74–0.84 times that of AH and BD plants). BD plants showed an elevated amino acid metabolism (2.84–3.38 times that of healthy plants), whereas BH plants showed a higher metabolism of phenolic compounds (1.73 times that of diseased plants). BH plants secreted more palmitic acid (1.53 times that of diseased plants) and recruited more nitrogen (N)-fixing bacteria (e.g., Paenarthrobacter, Sphingomonas), which potentially alleviated N scarcity in the soil, promoted plant growth, and improved the plant defense. Structural equation modeling highlighted the interactions among soil nutrients, root metabolites, and plant nutrients in recruiting pro-growth bacteria to the rhizosphere and roots for healthy plant growth. Our findings provide novel evidence of how root exudates influence plant–microbiome interactions during pathogen infections, highlighting the importance of a balanced root exudate profile and its associated “HealthyBiome” in promoting plant health and growth.

Early identification of macrophage activation syndrome secondary to systemic lupus erythematosus with machine learning

ObjectiveThe macrophage activation syndrome (MAS) secondary to systemic lupus erythematosus (SLE) is a severe and life-threatening complication. Early diagnosis of MAS is particularly challenging. In this study, machine learning models and diagnostic scoring card were developed to aid in clinical decision-making using clinical characteristics.MethodsWe retrospectively collected clinical data from 188 patients with either SLE or the MAS secondary to SLE. 13 significant clinical predictor variables were filtered out using the Least Absolute Shrinkage and Selection Operator (LASSO). These variables were subsequently utilized as inputs in five machine learning models. The performance of the models was evaluated using the area under the receiver operating characteristic curve (ROC-AUC), F1 score, and F2 score. To enhance clinical usability, we developed a diagnostic scoring card based on logistic regression (LR) analysis and Chi-Square binning, establishing probability thresholds and stratification for the card. Additionally, this study collected data from four other domestic hospitals for external validation.ResultsAmong all the machine learning models, the LR model demonstrates the highest level of performance in internal validation, achieving a ROC-AUC of 0.998, an F1 score of 0.96, and an F2 score of 0.952. The score card we constructed identifies the probability threshold at a score of 49, achieving a ROC-AUC of 0.994 and an F2 score of 0.936. The score results were categorized into five groups based on diagnostic probability: extremely low (below 5%), low (5–25%), normal (25–75%), high (75–95%), and extremely high (above 95%). During external validation, the performance evaluation revealed that the Support Vector Machine (SVM) model outperformed other models with an AUC value of 0.947, and the scorecard model has an AUC of 0.915. Additionally, we have established an online assessment system for early identification of MAS secondary to SLE.ConclusionMachine learning models can significantly improve the diagnostic accuracy of MAS secondary to SLE, and the diagnostic scorecard model can facilitate personalized probabilistic predictions of disease occurrence in clinical environments.

Advances in proteomics in diffuse large B‑cell lymphoma (Review).

Diffuse large B‑cell lymphoma (DLBCL) is the most common pathological type of non‑Hodgkin's lymphoma. Although the development of monoclonal antibodies, small‑molecule‑targeted drugs and novel chemotherapeutic agents, and the increased use of immunotherapy have markedly improved the outcomes of DLBCL, ~40% of patients cannot be cured following the use of standardized first‑line treatment. In addition, the specific mechanisms of drug resistance and potential factors associated with a poor prognosis in these patients remain unclear. Proteomics research is used to determine potential associations between changes in DLBCL protein expression levels and different stages of disease occurrence and development. Proteomics may aid in the identification of novel molecular mechanisms and drug resistance mechanisms, through identifying multiple associated proteins and monitoring changes in expression levels. Thus, proteomics research may exhibit potential in the development of therapeutic targets and in improving prognostic evaluation in patients with DLBCL. The present study aimed to review the use of proteomic methods for the investigation of DLBCL, including the mechanisms underlying disease progression and drug resistance in DLBCL, and the function of the tumor microenvironment in lymphoma growth. The present review also demonstrated the potential of proteomic‑guided therapeutic strategies for DLBCL and discussed the synergistic benefits of using proteomic methods in DLBCL research.

African ancestry-derived APOL1 risk genotypes show proximal epigenetic associations

Apolipoprotein L1 (APOL1) coding variants, termed G1 and G2, are established genetic risk factors for a growing spectrum of diseases, including kidney disease, in individuals of African ancestry. Evidence suggests that the risk variants, which show a recessive mode of inheritance, lead to toxic gain-of-function changes of the APOL1 protein. Disease occurrence and presentation vary, likely due to modifiers or second hits. To understand the role of the epigenetic landscape in relation to APOL1 risk variants, we performed methylation quantitative trait locus (meQTL) analysis to identify differentially methylated CpGs influenced by APOL1 risk variants in 611 African American individuals. We identified five CpGs that were significantly associated with APOL1 risk alleles in discovery and replication studies, and one CpG-APOL1 association was independent of other genomic variants. Our study highlights proximal DNA methylation alterations that may help explain the variable disease risk and clinical manifestation of APOL1 variants.

THE IMPACT OF STRESS-ASSOCIATED DISEASES OF THE DIGESTIVE ORGANS ON THE PSYCHOPHYSIOLOGICAL STATE OF OPERATORS OF UNMANNED AIRCRAFT SYSTEMS

Introduction. In the conditions of martial law, an important role belongs to modern military equipment, which includes unmanned aircraft systems (UAS). Оperators of UAS in the course of combat missions are significantly affected by the stress factor. It provokes the occurrence of stress-associated diseases in them, therefore, studying the specifics of the impact of stress-associated diseases on the psychophysiological state of operators determines the relevance of this study. The purpose of the study. To establish the patterns of influence of stress-associated diseases of the digestive organs on the psychophysiological state of the operators of unmanned aircraft systems. Materials and methods. In the course of the research, 94 operators of UAS under the age of 40 were examined, who were then divided into groups based on their health status. The examination was carried out using modern modified methods implemented on the basis of the software and hardware complex for psychophysiological research «PFI-2». The results. It has been proven that stress-associated diseases of the digestive organs significantly worsen (p&lt;0,05) the psychophysiological state of operators of UAS in a sufficiently large number of professionally important psychophysiological qualities. The reliable influence of the «state of health» factor on the indicators of simple and complex visual-motor reaction of operators of UAS is shown. The analysis of correlation constellations according to cognitive parameters showed that 6 of them are observed in healthy operators, 2 in mildly ill patients, and 1 in severely ill patients, that is, the system of connections between cognitive functions breaks down quite strongly with the development of stress-associated diseases of the digestive organs. There is a natural decrease in the number of connections in the series «healthy (13) – mildly ill (10) – severely ill (5)». It is shown that the stress that causes the disease fundamentally destroys the system of ensuring professional activity. Conclusions. It was established that stress-associated diseases of the digestive organs negatively affect the psychophysiological state of operators. The degree of degradation of the structure of correlation relations (р=0,0491) as a result of the influence of the stress factor on the operators was revealed. A hypothesis regarding the destruction of the system of connections between cognitive psychophysiological parameters depending on the «severity of the disease» is proposed. The need to develop a set of measures to prevent the development of stress-related diseases among operators of UAS has been established.

Effect of pitavastatin on erythrocyte membrane fatty acid content in patients with chronic kidney disease: two-arm parallel randomized controlled trial.

Statins reduce the risk of cardiovascular events in patients with chronic kidney disease (CKD). Although diabetes mellitus (DM) is a reported side effect of statin treatment, some studies have indicated that pitavastatin does not cause DM. The present study investigated the effect of pitavastatin on the fatty acid (FA) content of erythrocyte membranes, which affects the occurrence of DM and cardiovascular diseases. In addition, changes in adiponectin and glycated hemoglobin (HbA1c) levels were evaluated after pitavastatin treatment. A total of 45 patients were enrolled, 28 of whom completed the study. Over 24 weeks, 16 patients received 2 mg pitavastatin and 12 patients received 10 mg atorvastatin. Dosages were adjusted after 12 weeks if additional lipid control was required. There were 10 and nine patients with DM in the pitavastatin and atorvastatin groups, respectively. Erythrocyte membrane FAs and adiponectin levels were measured using gas chromatography and enzyme-linked immunosorbent assay, respectively. In both groups, saturated FAs, palmitic acid, trans-oleic acid, total cholesterol, and low-density lipoprotein cholesterol levels were significantly lower than those at baseline. The arachidonic acid (AA) content in the erythrocyte membrane increased significantly in the pitavastatin group, but adiponectin levels were unaffected. HbA1c levels decreased in patients treated with pitavastatin. No adverse effects were associated with statin treatment. Pitavastatin treatment in patients with CKD may improve glucose metabolism by altering erythrocyte membrane AA levels. In addition, pitavastatin did not adversely affect glucose control in patients with CKD and DM.

Decoding Organelle Interactions: Unveiling Molecular Mechanisms and Disease Therapies.

Organelles, substructures in the cytoplasm with specific morphological structures and functions, interact with each other via membrane fusion, membrane transport, and protein interactions, collectively termed organelle interaction. Organelle interaction is a complex biological process involving the interaction and regulation of several organelles, including the interaction between mitochondria-endoplasmic reticulum, endoplasmic reticulum-Golgi, mitochondria-lysosomes, and endoplasmic reticulum-peroxisomes. This interaction enables intracellular substance transport, metabolism, and signal transmission, and is closely related to the occurrence, development, and treatment of many diseases, such as cancer, neurodegenerative diseases, and metabolic diseases. Herein, the mechanisms and regulation of organelle interactions are reviewed, which are critical for understanding basic principles of cell biology and disease development mechanisms. The findings will help to facilitate the development of novel strategies for disease prevention, diagnosis, and treatment opportunities.