Comparative proteomic analysis of the ovarian fluid and eggs of Siberian sturgeon.

BACKGROUND: Sturgeon species are living fossils that exhibit unique reproductive characteristics, and elucidation of the molecular processes governing the formation and quality of sturgeon eggs is crucial. However, comprehensive data on the protein composition of sturgeon ovarian fluid (OF) and eggs and their functional significance are lacking. To address this knowledge gap, the aim of the present study was to conduct a comprehensive comparative proteomic analysis of Siberian sturgeon OF and eggs using liquid chromatography-mass spectrometry (LC-MS/MS). RESULTS: A total of 617 proteins were identified in OF, and 565 proteins were identified in eggs. A total of 772 proteins showed differential abundance. Among the differentially abundant proteins, 365 were more abundant in OFs, while 407 were more abundant in eggs. We identified 339 proteins unique to OFs and 287 proteins specific to eggs, and further investigated the top 10 most abundant proteins in each. The functional annotation of the OF proteins highlighted their predominant association with immune system processes, including the complement and coagulation cascade, neutrophil and leukocyte-mediated immunity, cholesterol metabolism, and regulation of the actin cytoskeleton. Analysis of egg proteins revealed enrichment in metabolic pathways, such as oxidative phosphorylation and fatty acid metabolism, and protein ubiquitination and translation. OF-specific proteins included extracellular matrix and secretory vesicles, and eggs were enriched in proteins localized to mitochondria and ribosome components. CONCLUSIONS: This study presents the first comprehensive characterization of the protein composition of sturgeon OF and eggs and elucidates their distinct functional roles. These findings advance our understanding of sturgeon reproduction, OF-egg signaling and the origin of OF proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD044168 to ensure accessibility for further research.

Analysis of factors influencing vascular calcification in peritoneal dialysis patients and their impact on long-term prognosis.

BACKGROUND: This study aims to investigate the influencing factors of vascular calcification in peritoneal dialysis (PD) patients and its relationship with long-term prognosis. METHODS: This retrospective cohort study included chronic kidney disease patients undergoing peritoneal dialysis at the Peritoneal Dialysis Center of Beijing Luhu Hospital, Capital Medical University, from January 2019 to March 2019. Demographic and clinical laboratory data, including serum sclerostin (SOST), calcium (Ca), phosphate (P), serum albumin (ALB), and intact parathyroid hormone (iPTH) levels, were collected. Abdominal aortic calcification (AAC) was assessed using abdominal lateral X-ray examination to determine the occurrence of vascular calcification, and patients were divided into the AAC group and Non-AAC group based on the results. RESULTS: A total of 91 patients were included in the study. The AAC group consisted of 46 patients, while the Non-AAC group consisted of 45 patients. The AAC group had significantly older patients compared to the non-AAC group (P < 0.001) and longer dialysis time (P = 0.004). Multivariable logistic regression analysis indicated that risk factors for vascular calcification in PD patients included dialysis time, diabetes, hypertension, and SOST. Kaplan-Meier survival analysis showed that the AAC group had a significantly higher mortality rate than the non-AAC group (χ2 = 35.993, P < 0.001). Multivariable Cox regression analysis revealed that dialysis time, diabetes and AAC were risk factors for all-cause mortality in peritoneal dialysis patients. CONCLUSION: Longer dialysis time, comorbid diabetes, comorbid hypertension, and SOST are risk factors for vascular calcification in PD patients. Additionally, AAC, longer dialysis time, and comorbid diabetes are associated with increased risk of all-cause mortality in peritoneal dialysis patients.

Association between lipid profiles and cigarette smoke among adults in the Persian cohort (Shahedieh) study.

OBJECTIVES: Exposure to cigarette smoke introduces a large amount of nicotine into the bloodstream through the lungs. So, smoking can be a risk factor for many diseases. The present study was conducted to investigate the effect of active and passive cigarette smoke on the blood lipid profile and dyslipidemia. METHODS: This cross-sectional study was performed on 5052 individuals who participated in the recruitment phase of the Shahedieh cohort study. A logistic regression model was used to investigate the relationship between smoking exposure status and lipid profiles. RESULTS: The prevalence of abnormal low-density lipoprotein-cholesterol (LDL-C), abnormal HDL-C, abnormal total cholesterol (TC), abnormal triglyceride (TG), and dyslipidemia were 254 (5.00%), 562 (11.10%), 470 (9.30%), 1008 (20.00%), and 1527 (30.20%), respectively. Adjusting for confounders, it was observed that current smokers had higher odds of having abnormal HDL-C [OR (95% CI), 2.90 (2.28-3.69)], abnormal TG [OR (95% CI), 1.71 (1.38-2.13)] and dyslipidemia [OR (95% CI), 1.86 (1.53-2.25)]. Ex-smokers also had greater odds of abnormal HDL-C [OR (95% CI), 1.51 (1.06-2.16)] compared to non-smokers who were not exposed to cigarette smoke. CONCLUSIONS: The findings indicated that current smokers had higher TG and lower HDL. So, necessary measures should be taken to reduce smoking. The findings also showed that the prevalence of abnormal TG and HDL in ex-smokers was lower than in current smokers. Therefore, the existence of incentive policies to quit smoking seems necessary.

Prognostic significance of preoperative lymphocytes, albumin, and neutrophils (LANR) index in resectable pancreatic ductal adenocarcinoma.

PURPOSE: The index composed of preoperative lymphocytes, albumin, and neutrophils (LANR), a new composite score based on inflammatory response and nutritional status, has been reported to be associated with the prognosis of multiple types of cancer, but the role of LANR in the prognosis of resectable pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated. PATIENTS AND METHODS: The data of 142 patients with PDAC who underwent radical resection in the Affiliated Hospital of Jiangnan University from January 2015 to December 2018 were retrospectively analyzed. Receiver Operating Characteristic (ROC) curves were generated to determine the optimal cut-off values for these parameters, as well as the sensitivity and specificity of LANR in predicting survival. The Kaplan-Meier method was used to draw the survival curves. Log rank test was used for univariate analysis, and Cox proportional hazards regression model was used for multivariate analysis.  RESULTS: The optimal cut-off value of LANR was 18.145, and a low preoperative LANR was significantly correlated with the location of the tumor (p = 0.047). Multivariate analysis showed that tumor differentiation degree (HR:2.357, 95%CI:1.388-4.003,p = 0.002), lymph node metastasis (HR:1.755, 95%CI: 1.115-2.763, p = 0.015), TNM stage (HR:4.686, 95%CI: 2.958-7.425, p < 0.001), preoperative cancer antigen 19 - 9 levels (HR:1.001, 95%CI: 1.000-1.001, p < 0.001) and preoperative LANR (HR:0.221, 95%CI: 0.111-0.441, p < 0.001) were independent risk factors for a poor prognosis in patients undergoing radical resection of PDAC. CONCLUSION: This study found that preoperative LANR can be used to assess the prognosis of radical resection in patients with PDAC; those with low preoperative LANR had a worse outcome.

Ovarian response to controlled stimulation and its predictors in a limited-resource setting.

BACKGROUND: Infertility remains a serious health concern for Ethiopian women. Most of its treatment approaches entail controlled ovarian stimulation, the responses of which vary. However, there are no data on ovarian response to stimulation or its predictors in our situation. Thus, the current study aimed to assess the ovarian response to controlled stimulation and identify predictors. METHODS: A retrospective follow-up study was undertaken from April 1, 2021, to March 31, 2022, among patients who had first-cycle controlled ovarian stimulation at St.Paul's Hospital Fertility Center in Addis Ababa, Ethiopia. Clinical data were extracted using a checklist. SPSS-26 for data analysis and Epidata-4.2 for data entry were employed. The binary logistic regression model was fitted. A p-value < 0.05 indicated a significant association. The ROC curve was used to determine cutoff values and identify accurate predictors. RESULTS: A total of 412 study participants were included in the final analysis. The patients had a mean age of 32.3 ± 5.1 years (range: 20 - 4). The good ovarian response rate was 67% (95% CI: 62.2-71.5). An anti-Mullerian hormone (AMH) concentration < 1.2ng/ml (AOR = 0.19, 95% CI (0.06-0.57)), an antral follicle count (AFC) < 5 (AOR = 0.16, 95% CI (0.05-0.56)), and an induction length < 10 days (AOR = 0.23, 95% CI (0.06-0.93)) were significantly associated with ovarian response. The prediction accuracies for the AFC and AMH concentrations were 0.844 and 0.719, respectively. The optimal cutoff point for prediction was 5.5 AFC, which had a sensitivity of 77.2% and a specificity of 72.8%. However, its positive and negative predictive values were 85.2% and 61.1%, respectively. For AMH, the optimal cutoff value was 0.71ng/mL, with a corresponding sensitivity and specificity of 65.2% and 66%. At this value, the positive and negative predictive values were 63.8% and 67.3%, respectively. CONCLUSION: Only two-thirds of our patients achieved a good ovarian response. Induction duration, AMH concentration, and AFC were found to be predictors, with the AFC being the strongest predictor. Therefore, the AFC should be performed on all of our patients, and the AMH is selectively employed. Future research must verify the best cutoff points and investigate additional factors affecting ovarian response.

Cross-sectional and longitudinal associations of Iron biomarkers and cardiovascular risk factors in pre- and postmenopausal women: leveraging repeated measurements to address natural variability.

BACKGROUND: The association between iron biomarkers and cardiovascular disease risk factors (CVD-RFs) remains unclear. We aimed to (1) evaluate the cross-sectional and longitudinal associations between iron biomarkers (serum ferritin, transferrin saturation (TSAT), transferrin) and CVD-RFs among women, and (2) explore if these associations were modified by menopausal status. METHOD: Cross-sectional and longitudinal analyses including 2542 and 1482 women from CoLaus cohort, respectively. Multiple linear regression and multilevel mixed models were used to analyse the associations between Iron biomarkers and CVD-RFs. Variability of outcomes and iron markers between surveys was accessed using intraclass correlation (ICC). RESULTS: After multivariable adjustment, elevated serum ferritin levels were associated with increased insulin and glucose levels, while higher transferrin levels were linked to elevated glucose, insulin and total cholesterol, and systolic and diastolic blood pressure (p < 0.05). No association was observed between CVD-RFs and TSAT (p > 0.05). Iron biomarkers demonstrated low reliability across reproductive stages but exhibited stronger associations in the perimenopausal group. In longitudinal analysis, we found association only for transferrin with lower glucose levels [ß = - 0.59, 95% CI (- 1.10, - 0.08), p = 0.02] and lower diastolic blood pressure [ß = - 7.81, 95% CI (- 15.9, - 0.56), p = 0.04]. CONCLUSION: In cross-sectional analysis, transferrin was associated with several CVD-RFs, and the associations did not change according to menopausal status. Conversely, in the longitudinal analyses, changes in transferrin were associated only with lower glucose and diastolic blood pressure levels. These differences might stem from the substantial longitudinal variation of iron biomarkers, underscoring the need for multiple iron measurements in longitudinal analyses.

Joint association of TyG index and high sensitivity C-reactive protein with cardiovascular disease: a national cohort study.

BACKGROUND: Both the triglyceride-glucose (TyG) index, as a surrogate marker of insulin resistance, and systemic inflammation are predictors of cardiovascular diseases; however, little is known about the coexposures and relative contributions of TyG index and inflammation to cardiovascular diseases. Using the nationally representative data from the China Health and Retirement Longitudinal Study (CHARLS), we conducted longitudinal analyses to evaluate the joint and mutual associations of the TyG index and high-sensitivity C-reactive protein (hsCRP) with cardiovascular events in middle-aged and older Chinese population. METHODS: This study comprised 8 658 participants aged at least 45 years from the CHARLS 2011 who are free of cardiovascular diseases at baseline. The TyG index was calculated as Ln [fasting triglyceride (mg/dL) × fasting glucose (mg/dL)/2]. Cardiovascular events were defined as the presence of physician-diagnosed heart disease and/or stroke followed until 2018.We performed adjusted Cox proportional hazards regression and mediation analyses. RESULTS: The mean age of the participants was 58.6 ± 9.0 years, and 3988 (46.1%) were females. During a maximum follow-up of 7.0 years, 2606 (30.1%) people developed cardiovascular diseases, including 2012 (23.2%) cases of heart diseases and 848 (9.8%) cases of stroke. Compared with people with a lower TyG index (< 8.6 [median level]) and hsCRP < 1 mg/L, those concurrently with a higher TyG and hsCRP had the highest risk of overall cardiovascular disease (adjusted hazard ratio [aHR], 1.300; 95% CI 1.155-1.462), coronary heart disease (aHR, 1.294; 95% CI 1.130-1.481) and stroke (aHR, 1.333; 95% CI 1.093-1.628), which were predominant among those aged 70 years or below. High hsCRP significantly mediated 13.4% of the association between the TyG index and cardiovascular disease, while TyG simultaneously mediated 7.9% of the association between hsCRP and cardiovascular risk. CONCLUSIONS: The findings highlight the coexposure effects and mutual mediation between the TyG index and hsCRP on cardiovascular diseases. Joint assessments of the TyG index and hsCRP should be underlined for the residual risk stratification and primary prevention of cardiovascular diseases, especially for middle-aged adults.

Role of biomarkers in antimicrobial stewardship: physicians' perspectives.

Biomarkers are playing an increasingly important role in antimicrobial stewardship. Their applications have included use in algorithms that evaluate suspected bacterial infections or provide guidance on when to start or stop antibiotic therapy, or when therapy should be repeated over a short period (6-12 h). Diseases in which biomarkers are used as complementary tools to determine the initiation of antibiotics include sepsis, lower respiratory tract infection (LRTI), COVID-19, acute heart failure, infectious endocarditis, acute coronary syndrome, and acute pancreatitis. In addition, cut-off values of biomarkers have been used to inform the decision to discontinue antibiotics for diseases such as sepsis, LRTI, and febrile neutropenia. The biomarkers used in antimicrobial stewardship include procalcitonin (PCT), C-reactive protein (CRP), presepsin, and interleukin (IL)-1ß/IL-8. The cut-off values vary depending on the disease and study, with a range of 0.25-1.0 ng/mL for PCT and 8-50 mg/L for CRP. Biomarkers can complement clinical diagnosis, but further studies of microbiological biomarkers are needed to ensure appropriate antibiotic selection.

Cover crops support the climate change mitigation potential of agroecosystems.

Cover crops have the potential to mitigate climate change by reducing negative impacts of agriculture on ecosystems. This study is first to quantify the net climate change mitigation impact of cover crops including land-use effects. A systematic literature and data review was conducted to identify major drivers for climate benefits and costs of cover crops in maize (Zea maize L.) production systems. The results indicate that cover crops lead to a net climate change mitigation impact (NCCMI) of 3.30 Mg CO2e ha-1 a-1. We created four scenarios with different impact weights of the drivers and all of them showing a positive NCCMI. Carbon land benefit, the carbon opportunity costs based on maize yield gains following cover crops, is the major contributor to the NCCMI (34.5% of all benefits). Carbon sequestration is the second largest contributor (33.8%). The climate costs of cover crops are mainly dominated by emissions from their seed production and foregone benefits due to land use for cover crops seeds. However, these two costs account for only 15.8% of the benefits. Extrapolating these results, planting cover crops before all maize acreage in the EU results in a climate change mitigation of 49.80 million Mg CO2e a-1, which is equivalent to 13.0% of the EU's agricultural emissions. This study highlights the importance of incorporating cover crops into sustainable cropping systems to minimize the agricultural impact to climate change.

Multi-vehicle adaptive 3D mapping for targeted ocean sampling.

Expanding spatial presentation from two-dimensional profile transects to three-dimensional ocean mapping is key for a better understanding of ocean processes. Phytoplankton distributions can be highly patchy and the accurate identification of these patches with the context, variability, and uncertainty of measurements on relevant scales is difficult to achieve. Traditional sampling methods, such as plankton nets, water samplers and in-situ vertical sensors, provide a snapshot and often miss the fine-scale horizontal and temporal variability. Here, we show how two autonomous underwater vehicles measured, adapted to, and reported real-time chlorophyll a measurements, giving insights into the spatiotemporal distribution of phytoplankton biomass and patchiness. To gain the maximum available information within their sensing scope, the vehicles moved in an adaptive fashion, looking for the regions of the highest predicted chlorophyll a concentration, the greatest uncertainty, and the least possibility of collision with other underwater vehicles and ships. The vehicles collaborated by exchanging data with each other and operators via satellite, using a common segmentation of the area to maximize information exchange over the limited bandwidth of the satellite. Importantly, the use of multiple autonomous underwater vehicles reporting real-time data combined with targeted sampling can provide better match with sampling towards understanding of plankton patchiness and ocean processes.

A low-cost spectroscopic nutrient management system for Microscale Smart Hydroponic system.

Hydroponics offers a promising approach to help alleviate pressure on food security for urban residents. It requires minimal space and uses less resources, but management can be complex. Microscale Smart Hydroponics (MSH) systems leverage IoT systems to simplify hydroponics management for home users. Previous work in nutrient management has produced systems that use expensive sensing methods or utilized lower cost methods at the expense of accuracy. This study presents a novel inexpensive nutrient management system for MSH applications that utilises a novel waterproofed, IoT spectroscopy sensor (AS7265x) in a transflective application. The sensor is submerged in a hydroponic solution to monitor the nutrients and MSH system predicts the of nutrients in the hydroponic solution and recommends an adjustment quantity in mL. A three-phase model building process was carried out resulting in significant MLR models for predicting the mL, with an R2 of 0.997. An experiment evaluated the system's performance using the trained models with a 30-day grow of lettuce in a real-world setting, comparing the results of the management system to a control group. The sensor system successfully adjusted and maintained nutrient levels, resulting in plant growth that outperformed the control group. The results of the models in actual deployment showed a strong, significant correlation of 0.77 with the traditional method of measuring the electrical conductivity of nutrients. This novel nutrient management system has the potential to transform the way nutrients are monitored in hydroponics. By simplifying nutrient management, this system can encourage the adoption of hydroponics, contributing to food security and environmental sustainability.

Comparative studies of hair shaft components between healthy and diseased donors.

Globally, the rapid aging of the population is predicted to become even more severe in the second half of the 21st century. Thus, it is expected to establish a growing expectation for innovative, non-invasive health indicators and diagnostic methods to support disease prevention, care, and health promotion efforts. In this study, we aimed to establish a new health index and disease diagnosis method by analyzing the minerals and free amino acid components contained in hair shaft. We first evaluated the range of these components in healthy humans and then conducted a comparative analysis of these components in subjects with diabetes, hypertension, androgenetic alopecia, major depressive disorder, Alzheimer's disease, and stroke. In the statistical analysis, we first used a student's t test to compare the hair components of healthy people and those of patients with various diseases. However, many minerals and free amino acids showed significant differences in all diseases, because the sample size of the healthy group was very large compared to the sample size of the disease group. Therefore, we attempted a comparative analysis based on effect size, which is not affected by differences in sample size. As a result, we were able to narrow down the minerals and free amino acids for all diseases compared to t test analysis. For diabetes, the t test narrowed down the minerals to 15, whereas the effect size measurement narrowed it down to 3 (Cr, Mn, and Hg). For free amino acids, the t test narrowed it down to 15 minerals. By measuring the effect size, we were able to narrow it down to 7 (Gly, His, Lys, Pro, Ser, Thr, and Val). It is also possible to narrow down the minerals and free amino acids in other diseases, and to identify potential health indicators and disease-related components by using effect size.

Large-scale remote sensing analysis reveals an increasing coupling of grassland vitality to atmospheric water demand.

Grasslands provide important ecosystem services to society, including biodiversity, water security, erosion control, and forage production. Grasslands are also vulnerable to droughts, rendering their future vitality under climate change uncertain. Yet, the grassland response to drought is not well understood, especially for heterogeneous Central European grasslands. We here fill this gap by quantifying the spatiotemporal sensitivity of grasslands to drought using a novel remote sensing dataset from Landsat/Sentinel-2 paired with climate re-analysis data. Specifically, we quantified annual grassland vitality at fine spatial scale and national extent (Germany) from 1985 to 2021. We analyzed grassland sensitivity to drought by testing for statistically robust links between grassland vitality and common drought indices. We furthermore explored the spatiotemporal variability of drought sensitivity for 12 grassland habitat types given their different biotic and abiotic features. Grassland vitality maps revealed a large-scale reduction of grassland vitality during past droughts. The unprecedented drought of 2018-2019 stood out as the largest multi-year vitality decline since the mid-1980s. Grassland vitality was consistently coupled to drought (R2 = .09-.22) with Vapor Pressure Deficit explaining vitality best. This suggests that high atmospheric water demand, as observed during recent compounding drought and heatwave events, has major impacts on grassland vitality in Central Europe. We found a significant increase in drought sensitivity over time with highest sensitivities detected in periods of extremely high atmospheric water demand, suggesting that drought impacts on grasslands are becoming more severe with ongoing climate change. The spatial variability of grassland drought sensitivity was linked to different habitat types, with declining sensitivity from dry and mesic to wet habitats. Our study provides the first large-scale, long-term, and spatially explicit evidence of increasing drought sensitivities of Central European grasslands. With rising compound droughts and heatwaves under climate change, large-scale grassland vitality loss, as in 2018-2019, will thus become more likely in the future.

Lead Isotope Signatures and Source Identification of Heavy Metals in Vegetable Soils Irrigated with Swine Wastewater of Jiangxi Province, China.

The rapid development of livestock and poultry industry in China has caused serious environment pollution problems. To understand the heavy metals accumulation and identify their sources, 7 heavy metals contents and lead isotope ratios were determined in 24 soil samples from vegetable fields irrigated with swine wastewater in Dongxiang County, Jiangxi Province, China. The results showed that the concentration of Cr, Ni, Cu, Zn, As, Cd and Pb in the swine wastewater irrigated vegetable soils varied from 38.5 to 86.4, 7.57 to 30.6, 20.0 to 57.1, 37.5 to 174, 9.18 to 53.1, 0.043 to 0.274 and 12.8 to 37.1 mg/kg, respectively. The soils were moderately to heavily polluted by As, moderately polluted by Cr, Ni, Cu, Zn and Cd, and unpolluted to moderately polluted by Pb. Sampling soils were classified as moderately polluted according to the Nemerow comprehensive pollution index. Lead isotope and Principal Component Analysis (PCA) analysis indicated that swine wastewater irrigation and atmospheric deposition were the primary sources of the heavy metals.

Future malaria environmental suitability in Africa is sensitive to hydrology.

Changes in climate shift the geographic locations that are suitable for malaria transmission because of the thermal constraints on vector Anopheles mosquitos and Plasmodium spp. malaria parasites and the lack of availability of surface water for vector breeding. Previous Africa-wide assessments have tended to solely represent surface water using precipitation, ignoring many important hydrological processes. Here, we applied a validated and weighted ensemble of global hydrological and climate models to estimate present and future areas of hydroclimatic suitability for malaria transmission. With explicit surface water representation, we predict a net decrease in areas suitable for malaria transmission from 2025 onward, greater sensitivity to future greenhouse gas emissions, and different, more complex, malaria transmission patterns. Areas of malaria transmission that are projected to change are smaller than those estimated by precipitation-based estimates but are associated with greater changes in transmission season lengths.

Genomic Profiling of Rare Undifferentiated Sarcomatoid Subtypes of Pancreatic Carcinomas: In Search of Therapeutic Targets.

PURPOSE: The highly aggressive undifferentiated sarcomatoid carcinoma (USC) subtype of pancreatic ductal adenocarcinoma (PDAC) remains poorly characterized because of its rarity. Previous case reports suggest that immune checkpoint inhibitors could be a promising treatment strategy, but the prevalence of established predictive biomarkers of response is largely unknown. The objective of this study was to leverage comprehensive genomic profiling of USC PDAC tumors to determine the prevalence of biomarkers associated with potential response to targeted therapies. METHODS: USC tumors (n = 20) underwent central pathology review by a board-certified gastrointestinal pathologist to confirm the diagnosis. These samples were compared with non-USC PDAC tumors (N = 5,562). Retrospective analysis of DNA and RNA next-generation sequencing data was performed. RESULTS: USC PDACs were more frequently PD-L1+ by immunohistochemistry than non-USC PDAC (63% v 16%, respectively, P < .001). Furthermore, USC PDAC had an increase in neutrophils (8.99% v 5.55%, P = .005) and dendritic cells (1.08% v 0.00%, q = 0.022) and an increased expression of PDCD1LG2 (4.6% v 1.3%, q = 0.001), PDCD1 (2.0% v 0.8%, q = 0.060), and HAVCR2 (45.9% v 21.7%, q = 0.107) than non-USC PDAC. Similar to non-USC PDAC, KRAS was the most commonly mutated gene (86% v 90%, respectively, P = 1). CONCLUSION: To our knowledge, this work represents the largest molecular analysis of USC tumors to date and showed an increased expression of immune checkpoint genes in USC tumors. These findings provide evidence for further investigation into immune checkpoint inhibitors in USC tumors.

Global trends of dioxin and dioxin-like PCBs in animal-origin foods: a systematic review and gap areas.

Dioxins and dioxin-like polychlorinated biphenyls are a group of lipophilic compounds classified under persistent environmental pollutants (POPs). Significant sources of dioxin emissions include industrial effluents, open burning practices, and biomedical and municipal waste incinerators. These emissions will enter the food chain and accumulate in animal-origin foods (AOFs). A systematic review was conducted to analyze the global levels of dioxins and dioxin-like PCBs in AOFs using PRISMA guidelines 2020. The data on the dioxin contamination in AOFs were extracted from 53 publications based on their presence in eggs, meat and meat products, milk and dairy products, marine fish and fish products, and freshwater fish and crabs. A gap analysis was conducted based on the systematic review to understand the grey areas to be focused on the  future. No trend of dioxin contamination in AOFs was observed. A significant gap area was found in the need for nationwide data generation in countries without periodic monitoring of AOFs for dioxin contamination. Source apportionment studies need to be explored for the dioxin contamination of AOFs. Large-scale screening tests of AOFs using DR-CALUX based on market surveys are required for data generation. The outcomes of the study will be helpful for stakeholders and policyholders in framing new policies and guidelines for food safety in AOFs.

Impact of Obesity on Hepatic Drug Clearance: What are the Influential Variables?

Drug clearance in obese subjects varies widely among different drugs and across subjects with different severity of obesity. This study investigates correlations between plasma clearance (CLp) and drug- and patient-related characteristics in obese subjects, and evaluates the systematic accuracy of common weight-based dosing methods. A physiologically-based pharmacokinetic (PBPK) modeling approach that uses recent information on obesity-related changes in physiology was used to simulate CLp for a normal-weight subject (body mass index [BMI] = 20) and subjects with various severities of obesity (BMI 25-60) for hypothetical hepatically cleared drugs with a wide range of properties. Influential variables for CLp change were investigated. For each drug and obese subject, the exponent that yields perfect allometric scaling of CLp from normal-weight subjects was assessed. Among all variables, BMI and relative changes in enzyme activity resulting from obesity proved highly correlated with obesity-related CLp changes. Drugs bound to α1-acid glycoprotein (AAG) had lower CLp changes compared to drugs bound to human serum albumin (HSA). Lower extraction ratios (ER) corresponded to higher CLp changes compared to higher ER. The allometric exponent for perfect scaling ranged from -3.84 to 3.34 illustrating that none of the scaling methods performed well in all situations. While all three dosing methods are generally systematically accurate for drugs with unchanged or up to 50% increased enzyme activity in subjects with a BMI below 30 kg/m2, in any of the other cases, information on the different drug properties and severity of obesity is required to select an appropriate dosing method for individuals with obesity.