High Susceptibility Research Articles

The role of STK11/LKB1 in cancer biology: implications for ovarian tumorigenesis and progression

STK11 (serine-threonine kinase 11), also known as LKB1 (liver kinase B1) is a highly conserved master kinase that regulates cellular metabolism and polarity through a complex signaling network involving AMPK and 12 other AMPK-related kinases. Germline mutations in LKB1 have been causatively linked to Peutz-Jeghers Syndrome (PJS), an autosomal dominant hereditary disease with high cancer susceptibility. The identification of inactivating somatic mutations in LKB1 in different types of cancer further supports its tumor suppressive role. Deleterious mutations in LKB1 are frequently observed in patients with epithelial ovarian cancer. However, its inconsistent effects on tumorigenesis and cancer progression suggest that its functional impact is genetic context-dependent, requiring cooperation with other oncogenic lesions. In this review, we summarize the pleiotropic functions of LKB1 and how its altered activity in cancer cells is linked to oncogenic proliferation and growth, metastasis, metabolic reprogramming, genomic instability, and immune modulation. We also review the current mechanistic understandings of this master kinase as well as therapeutic implications with particular focus on the effects of LKB1 deficiency in ovarian cancer pathogenesis. Lastly, we discuss whether LKB1 deficiency can be exploited as an Achilles heel in ovarian cancer.

Ultra-efficient Ru and Nb Co-Modified CeO2 Catalysts for Catalytic Oxidation of 1,2-Dichloroethane.

The oxidation of chlorinated volatile organic compounds on CeO2 is hindered by its high susceptibility to chlorine poisoning, resulting in a reduced efficiency and stability. In this study, Ru- and Nb-co-modified CeO2 catalysts were designed to achieve excellent activity, stability, and CO2 selectivity in the catalytic oxidation of 1,2-dichloroethane (EDC). The formation of Nb-O-Ce bonds was observed to enhance the surface acidic sites, thereby improving HCl selectivity and reducing the production of chlorinated byproducts. Meanwhile, it inhibits the formation of Ru-O-Ce and promotes the generation of highly dispersed RuO2 particles on the surface, enhancing the redox properties and mobility of the surface oxygen, thus increasing CO2 selectivity. In situ diffuse reflectance infrared Fourier transform spectroscopy results revealed that chlorine species preferentially attach to Nb species rather than to oxygen vacancies on the Ru/Nb/CeO2 catalyst. This allows more alkane groups to oxidize to formate on the oxygen vacancies, reducing byproduct concentration. Additionally, the oxidation of alkane groups to carboxylic acids is initiated on the Nb species, completing a comprehensive oxidation process under the synergistic effect of RuO2.

The Use of Geographic Information Systems and Multi-Criteria Decision-Making Methods in the Creation of Forest Fire Susceptibility Maps: A Literature Review

Forest fires are natural disasters that cause significant environmental, economic, and social damage worldwide. This study provides a literature review examining how Geographic Information Systems (GIS) and Multi-Criteria Decision-Making (MCDM) methods are utilized in the prevention of forest fires and the identification of high-risk areas. GIS, as a system used for the collection and analysis of spatial data, enables the consideration of various factors influencing fire risk, such as climate change, topography, vegetation, and weather conditions. The spatial analysis capabilities offered by GIS play a critical role in identifying regions with high fire susceptibility when generating fire risk maps. Additionally, MCDM methods contribute significantly to the decision-making process by allowing the evaluation of multiple criteria in fire risk analysis. Logistic Regression and Frequency Ratio, which are frequently employed in the literature, are widely used in fire risk analysis and improve the accuracy of susceptibility maps. Furthermore, MCDM methods have been proven effective in estimating the likelihood of forest fire occurrences and identifying fire-prone areas. The integration of GIS and MCDM methods allows for more precise identification of risk zones and supports the development of fire prevention strategies. This literature review highlights the advantages of utilizing GIS and MCDM in the production of forest fire susceptibility maps and suggests that these methods may have broader applications in future research. The effective use of technology in combating forest fires enhances the accuracy of fire risk assessments, contributing significantly to environmental protection efforts.

Sustainable Grease Lubrication Induced by Graphene Oxide and LiFePO4

This study investigates the possibility of modifying lubricating greases by 2D graphene oxide (GO) and lithium iron phosphate (LiFePO4) to enhance their tribological performance. GO's layered structure plays a crucial role in reducing friction by facilitating easy sliding between rubbing surfaces, making it an effective solid lubricant. The impact of these additives on friction reduction, wear resistance, and the tribolayer formation ability is evaluated by ball‐on‐disc tribometry with an emphasis on durability and sustainability. Comparative results reveal that unmodified greases exhibit a high susceptibility to wear, including oxide formation and severe plastic deformation. In contrast, greases with GO show a notable reduction in friction, while the addition of LiFePO4 contributes to the formation of a continuous protective layer that substantially reduces abrasive wear. The combination of GO and LiFePO4 results in a homogeneous, durable tribofilm that effectively protects surfaces against severe wear providing long‐term friction stability. Although lab‐scale LiFePO4 is used, the findings suggest a great potential for extending the useful life of recycled LiFePO4 from lithium‐ion batteries, similar to commercial LiFePO4 thus confirming significant improvements in the grease performance but also highlighting the potential to advance toward more sustainable solutions in industrial applications and electric vehicles.

Prediction of the potential Brazilian distribution for invasive mollusc Melanoides tuberculata (Müller, 1774), including a new record from Northern Coastal Region

The freshwater snail Melanoides tuberculata (Müller, 1774) native from Africa and Asia, recently spreads out over the world. Records of invasive species are extremely important, because it helps in future invasions mitigation. Here, we report for the first time the occurrence of M. tuberculata in a protected area of Northern Brazilian Coast and use predictive distribution modeling to know the potential Brazilian distribution for this species. The gastropod M. tuberculata were collected in the municipalities of Tutóia and Santo Amaro - Maranhão State. According to the results of the predictive map distribution for Brazil, the regions with the highest susceptibility are the northeast, southeast and midwest. Preciptation of month and Isothermalithy were de variables that most contribute to the species’ potential distribution. This species presented high to medium suitability of occurrence in 32 national protected areas. Futher studies are recommended to monitor the growth and dispersion, in order to determine its real impact.

Ultra‐Low Threshold Voltage in N‐Type Organic Electrochemical Transistors Enabled by Organic Mixed Ionic‐Electronic Conductors with Dual Electron‐Withdrawing Substitutions

AbstractAchieving low threshold voltage (Vth) in organic electrochemical transistors (OECTs) is essential for minimizing power consumption and enhancing sensitivity in bioelectronic devices. However, obtaining OECT materials with ultra‐low Vth, close to 0 V for n‐type conjugated polymers remains challenging. Here, a conjugated polymer FBDOPV‐CNTVT is introduced, which features a rigid backbone structure and high electron deficiency, leading to an exceptionally low lowest unoccupied molecular orbital (LUMO) energy level of −4.67 eV, achieved through dual electron‐withdrawing substitutions. With its ultra‐low LUMO energy level, FBDOPV‐CNTVT exhibits high susceptibility to electrochemical doping, even demonstrating efficient doping near 0 V. Consequently, the OECT device employing FBDOPV‐CNTVT as the active material shows an ultra‐low Vth of 7.5 mV, setting a new record for the Vth of n‐type OECT devices. Furthermore, FBDOPV‐CNTVT exhibits a µC* value of 6.13 F cm−1 V−1 s−1 and retains ≈85% of its current after 2000 s cycling. This study highlights the potential of conjugated polymers with dual electron‐withdrawing substitutions to achieve ultra‐low LUMO energy levels, effectively reducing the Vth of n‐type OECT devices and promising advancements in bioelectronics.

Machine learning-based assessment of regional-scale variation of landslide susceptibility in central Vietnam.

Recurrent landslide events triggered by typhoons and tropical storms over Vietnam pose a longstanding threat to the nation's population and infrastructure. Changes in hydroclimatic conditions, especially the growing intensity and frequency of storms, have elevated landslide susceptibility in many parts of the country. This research examines the spatio-temporal variations in landslide susceptibility across central Vietnam over several years, using multi-temporal landslide inventories from Typhoon Ketsana (2009), Tropical Storm Podul (2013), and Typhoon Molave (2020). Additionally, the research explores the impact of individual landslide causative factors on the probabilistic occurrences of landslides. The post-event landslide susceptibility models of these three climate extreme events were developed using nine causative factors and a Random Forest machine learning algorithm. The results indicate a notable areal expansion of high to very high landslide susceptibility in the northern and eastern regions and a moderate reduction in the central and southern areas during the post-Molave period compared to the post-Ketsana period. These changes may be early indicators of increasing landslide susceptibility in response to changing hydro-climatic conditions. The research found that annual average rainfall and topographic elevation are the two most important variables influencing landslide prediction, showing a nonlinear relationship with landslide probability. The landslide susceptibility models achieved high Area Under the Receiver Operating Characteristic Curve (AUC) (>95%), accuracy (>89%), and sensitivity (>90%) scores, signifying the robustness of the models. Additionally, the uncertainty of the models was quantified and spatially mapped. This multi-temporal analysis of landslide susceptibility is crucial for understanding the regional susceptibility trends and identifying areas with increasing, decreasing, and consistently high susceptibility to landslides. These insights are invaluable for prioritizing mitigation and risk reduction strategies in landslide-prone regions and guiding appropriate land use planning.

Microbial remediation of heavy metal-polluted water

Heavy metal pollution in water has become a global environmental problem, threatening aquatic ecosystems and human health. Physical and chemical methods can effectively remove heavy metal pollutants, while their applications are limited due to the high costs, complex operation, and susceptibility to secondary pollution. Bioremediation is the most promising method for eliminating toxic pollutants. Microorganisms including bacteria, fungi, and algae can convert toxic heavy metals into less toxic forms, which has become an effective and environmentally friendly solution for the remediation of heavy metal pollution in water environments. This paper expounds the toxicity and mechanism of heavy metal pollution, microbial remediation mechanisms, and primary microbial remediation strategies, providing a reference for the removal or reduction of metal pollutants in water environments as well as the development of related technologies.

A smarter approach to liquefaction risk: harnessing dynamic cone penetration test data and machine learning for safer infrastructure

This paper presents a novel approach for assessing liquefaction potential by integrating Dynamic Cone Penetration Test (DCPT) data with advanced machine learning (ML) techniques. DCPT offers a cost-effective, rapid, and adaptable method for evaluating soil resistance, making it suitable for liquefaction assessment across diverse soil conditions. This study establishes a threshold criterion based on the ratio of the penetration rate to the dynamic resistance (e/qd), where values exceeding four indicate high liquefaction susceptibility. ML models, including Support Vector Machine (SVM) optimized with Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Genetic Algorithm (GA), and Firefly Algorithm (FA), were employed to predict the e/qd ratio using key geotechnical parameters, such as fine content, peak ground acceleration, reduction factor, and penetration rate. The SVM-PSO model demonstrated superior performance, with high R2 values of 0.999 and 0.989 in the training and testing phases, respectively. The proposed methodology offers a sustainable and accurate approach for liquefaction assessment, reducing the environmental impact of geotechnical investigations, while ensuring reliable predictions. This study bridges the gap between field testing and advanced computational techniques, providing a powerful tool for geotechnical engineers to assess liquefaction risks and design resilient infrastructures.

Development of an Intravenously Stable Disulfide-Rich Peptide for the Treatment of Chemotherapy-Induced Neuropathic Pain.

α-conotoxins (α-Ctxs), a class of disulfide-rich conopetides, are excellent drug leads due to their small size, high selectivity, and potency for specific membrane receptors and ion channels involved in pain transmission. However, their high susceptibility to proteolytic degradation limits their therapeutic potential. In this study, we designed and synthesized a series of conformationally stable analogues of α-Ctx Mr1.1[S4Dap] using various structural optimization strategies. The Mr1.1[S4Dap, C16Pen] analogue maintained potency at human α9α10 nicotinic acetylcholine receptors, with a half-maximal inhibitory concentration (IC50) of 4 nM. It exhibited over a 5-fold increase in serum stability compared to Mr1.1[S4Dap], without disrupting its overall conformation. Furthermore, intravenous application of Mr1.1[S4Dap, C16Pen] showed potent analgesic activity in oxaliplatin-induced cold allodynia, indicating a high potential for drug development. Overall, the results from this study provide valuable insights for optimizing the serum stability of disulfide-rich peptides in future therapeutic applications.

CRBN modulates synuclein fibrillation via degradation of DNAJB1 in mouse model of Parkinson disease

Cereblon (CRBN) is a substrate recruiter for CRL4CRBN E3 ubiquitin ligase system playing a plethora of pivotal roles for biological systems. Here, we identified DNAJB1 (DJ1) as endogenous substrate of CRBN and report how CRBN influences the aggregation and toxicity of alpha-synuclein (α-SYN) via modulation of DJ1. CRBN interferes with molecular activities of DJ1 in vitro, in cells, and in vivo resulting in a reduced disaggregation of α-SYN fibrils, increased formation of preformed fibrils (PFFs) of α-SYN, and high susceptibility of mice to MPTP and PFF-induced neurotoxicity. Depletion of Crbn improves the behavioral and biochemical responses of mice towards neurotoxic insult. Finally, we designed a peptide inhibitor to inhibit the recruitment of DJ1 to CRBN for ubiquitination, resulting in an enhanced supply of DJ1 to counteract the toxicity of aggregated α-SYN. Our data has important implications for development of CRBN-targeting therapies that could prevent or delay progression of neurodegenerative synucleinopathy.

Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia

Abstract Due to favorable natural conditions and human impact, the territory of North Macedonia is very susceptible to natural hazards. Steep hillslopes combined with soft rocks (schists on the mountains; sands and sandstones in depressions), erodible soils, semiarid continental climate, and sparse vegetation cover give a high potential for soil erosion and landslides. For this reason, this study presents a multi-hazard approach to geohazard modeling on the national extent in the example of North Macedonia. Utilizing Geographic Information Systems, relevant data about the entire research area were employed to analyze and assess soil erosion and susceptibility to landslides and identify areas prone to both hazards. Using the Gavrilović Erosion Potential Method (EPM), an average value of 0.36 was obtained for the erosion coefficient Z, indicating low to moderate susceptibility to erosion. However, a significant area of the country (9.6%) is susceptible to high and excess erosion rates. For the landslide susceptibility assessment (LSA), the Analytical hierarchy process approach is combined with the statistical method (frequency ratio), showing that 29.3% of the territory belongs to the zone of high and very high landslide susceptibility. Then, the accuracy assessment is performed for both procedures (EPM and LSA), showing acceptable reliability. By overlapping both models, a multi-hazard map is prepared, indicating that 22.3% of North Macedonia territory is highly susceptible to erosion and landslides. The primary objective of multi-hazard modeling is to identify and delineate hazardous areas, thereby aiding in activities to reduce the hazards and mitigate future damage. This becomes particularly significant when considering the impact of climate change, which is associated with increased landslide and erosion susceptibility. The approach based on a national level presented in this work can provide valuable information for regional planning and decision-making processes.

Management of the Neoleucinodes elegantalis (Lepidoptera: Crambidae) using different techniques

Tomato (Solanum lycopersicum) is one of the world's most widely cultivated vegetable crops. However, tomato cultivation requires intensive management due to its high susceptibility to pests and diseases that can compromise and increase production costs. Therefore, this research aimed to compare different tactics and methods for monitoring and managing the small tomato borer, Neoleucinodes elegantalis, in the field. The experiments were conducted in the 2012 and 2013 harvests on a rural property in the Conceição do Castelo - ES municipality. The evaluated systems were: 1. Monitoring associated with the release of the parasitoid Trichogramma and the biological product based on Bacillus thuringiensis (Bt) (Trich + Agree), 2. Monitoring associated with Bt and bagging of bunches (Bagg + Agree), 3. Monitoring associated with bagging of fruits and release of Trichogramma (Bagg + Tricho), 4. Monitoring associated with the release of the parasitoid Trichogramma (Mon + Trich), 5. Release of the parasitoid Trichogramma + control level (Trich + CL) and 6. Conventional System (Conv). In both harvests, it was found that the highest incidence of borer occurred in the conventional method, which was statistically superior to other management systems. The Trich + CL and Bagg + Trich methods stood out the most in terms of reducing the incidence of drills. It was concluded that, regardless of the growing season and treatments, all combinations of N. elegantalis management techniques presented significantly superior results to the conventional system. Thus, using these methods and control tactics can minimize environmental impacts and promote more sustainable agriculture.

Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China

In recent decades, global warming has significantly altered both the spatial and temporal distribution of rainfall patterns. This change has heightened the risk of rainfall-induced landslides, which are the most prevalent natural disasters in the mountainous regions of southwestern China. These events pose unpredictable and severe threats to the region, making it essential to forecast future rainfall trends and assess how landslide susceptibility will respond to these changes. Understanding these dynamics is crucial for developing effective strategies to mitigate and adapt to the changing rainfall patterns that influence landslides. This study focuses on Sichuan Province, China, and uses annual cumulative rainfall (ACR) as a key dynamic variable to create landslide susceptibility maps (LSMs). The goal is to explore the evolving relationship between rainfall and landslide susceptibility and use future rainfall projections to predict these risks. To achieve this, a historical landslide geospatial database was compiled across five temporal categories: 2000, 2001–2005, 2006–2010, 2011–2015, and 2016–2020. The extreme learning machine (ELM) was applied to generate LSMs for the years 2000 to2020, while an elasticity framework was used to assess how sensitive landslide susceptibility is to rainfall variations. To project future scenarios, a long short-term memory (LSTM) model was employed to project the ACR for 2030, using monthly rainfall data from 2000 to 2020. This projected ACR was then used to estimate future landslide susceptibility. Results showed a marked increase in high landslide susceptibility areas: 5.6 % by 2005, 0.3 % by 2010, 0.2 % by 2015, and 12.9 % by 2020, all relative to the year 2000. The elasticity analysis revealed that from 2000 to 2020, a 1 % change in rainfall would cause an average 1.35 % change in landslide susceptibility. Looking forward to 2030, the projected rise in ACR is expected to lead to a 2.44 % increase in areas of high landslide susceptibility. Multiple validation techniques were applied to ensure reliability and robustness of these findings.

Issues with Cefiderocol Testing: Comparing Commercial Methods to Broth Microdilution in Iron-Depleted Medium-Analyses of the Performances, ATU, and Trailing Effect According to EUCAST Initial and Revised Interpretation Criteria.

The rise of multi-drug-resistant Gram-negative bacteria necessitates the development of new antimicrobial agents. Cefiderocol shows promising activity by exploiting bacterial iron transport systems to penetrate the outer membranes of resistant pathogens. This study evaluates the efficacy of cefiderocol testing methods and trailing effect impact using a ComASP® Cefiderocol panel, disk diffusion (DD), and MIC test strips (MTS) compared to iron-depleted broth microdilution (ID-BMD). A total of 131 Gram-negative strains from clinical samples was tested by commercial methods and the gold standard. Results were interpreted as per 2024 and 2023 EUCAST guidelines. ID-BMD revealed high cefiderocol susceptibility among Enterobacterales and Pseudomonas aeruginosa, with one Klebsiella pneumoniae isolate being resistant. Acinetobacter baumannii exhibited higher MIC values, particularly considering trailing effects that complicated MIC readings. ComASP® showed 97% categorical agreement (CA) and 66% essential agreement (EA) with ID-BMD for Enterobacterales but failed to detect the resistant K. pneumoniae. DD tests demonstrated variable CA (72% or 93%), and 38% or 34% of strains within the ATU according to EUCAST Breakpoint Tables v13.0 and 14.0, respectively, with major errors only. MTS for P. aeruginosa had 100% CA but 44% EA, and often underestimated MIC values. The study emphasizes the need for standardized criteria to address trailing effects and ATU and highlights the discrepancies between testing methods. While cefiderocol resistance remains rare, accurate susceptibility testing is crucial for its effective clinical use. The findings suggest that current commercial tests have limitations, necessitating careful interpretation and potential supplementary testing to guide appropriate antibiotic therapy.

Nanotechnology strategy for inhibition of PARP1 and IL-17A-associated with neurotoxicity in rats exposed to hospital wastewater.

Hospital wastewater (HWW) poses a serious hazard to human health security concerning its high susceptibility to neurodegeneration. Water sources and ecosystems are exposed to a complicated pollution load from a variety of refractory organics and pharmaceutical active composites. This study evaluates the treated newly developed nanocomposite (NiFe2O4) HWW on the neural injury induced by HWW action in rats. Three groups of male Wistar rats were distributed, with eight rats in each: group I: tap water served as a control; group II: HWW; and group III: nano-HWW. Each group was intragastrical administrated with each type of water (2.5 ml/100 g b.wt/6 h) for 28 consecutive days. The open field test and Morris Water Maze assessed behavioral activity and spatial learning 2 days before the last day. The research demonstrated that HWW treated with nanocomposite (NiFe2O4) may exert decreased risks of the neural impairment effect of HWW. This improvement was achieved by reducing the neurotoxicity by lowering nitric oxide contents, lipid peroxidation, acetylcholinesterase, interleukin-17A (IL-17A), and poly(ADP-ribose) polymerase1(PARP1) while restoring the antioxidant biomarkers and neurotransmitter levels (β-endorphin, norepinephrine, dopamine, and serotonin) of the treated groups in the cortex and brainstem and enhancement of the histopathology of the cortex as well. In conclusion, this study introduced a newly developed nanotechnology application for treating HWW to protect from neural injury. The findings of this research have significant value for policymakers, Ministry of Health management, and environmental organizations in their selection of suitable techniques and procedures to optimize hospital wastewater treatment efficiency.

Exome Sequence Data of Eight SLC Transporters Reveal That SLC22A1 and SLC22A3 Variants Alter Metformin Pharmacokinetics and Glycemic Control.

Background: Type 2 diabetes (T2D) is one of the leading causes of mortality and is a public health challenge worldwide. Metformin is the first-choice treatment for T2D; its pharmacokinetics (PK) is facilitated by members of the solute carrier (SLC) superfamily of transporters, it is not metabolized, and it is excreted by the kidney. Although interindividual variability in metformin pharmacokinetics is documented in the Mexican population, its pharmacogenomics is still underexplored. We aimed to identify variants in metformin SLC transporter genes associated with metformin PK and response in Mexican patients. Methods: Using exome data from 2217 Mexican adults, we identified 86 biallelic SNVs in the eight known genes encoding SLC transporters, with a minor allele frequency ≥ 1%, which were analyzed in an inadequate glycemic control (IGC) association study in T2D metformin treated patients. Metformin PK was evaluated in a pediatric cohort and the effect of associated SNVs was correlated. Results: Functional annotation classified two SNVs as pathogenic. The association study revealed two blocks associated with IGC. These haplotypes comprise rs622591, rs4646272, rs4646273, and rs4646276 in SLC22A1; and rs1810126 and rs668871 in SLC22A3. PK profiles revealed that homozygotes of the SLC22A1 haplotype reached lower plasma metformin concentrations 2 h post administration than the other groups. Conclusions: Our findings highlight the potential of pharmacogenomics studies to enhance precision medicine, which may involve dosage adjustments or the exploration of alternative therapeutic options. These hold significant implications for public health, particularly in populations with a high susceptibility to develop metabolic diseases, such as Latin Americans.

National trends in type 2 diabetes mellitus stratified by central adiposity using waist-to-height ratio in South Korea, 2005–2022

Studies investigating the association between type 2 diabetes mellitus and central adiposity are lacking. Therefore, this study aimed to investigate trends in type 2 diabetes mellitus stratified by central adiposity using waist-to-height ratio (WHtR). Trends in type 2 diabetes mellitus were examined by central adiposity, using WHtR, with data from the Korea National Health and Nutrition Examination Survey (2005–2022). Individuals aged 30 years and over who participated in the survey were selected. Type 2 diabetes mellitus was identified based on serum glucose or HbA1c levels, the use of diabetes medications, or a prior diagnosis by a physician. Weighted β-coefficients or odd ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess changes in disease prevalence. A total of 79,368 participants were included in the database (female: 45,163 [56.9%]). from 2005 to 2022, the prevalence of type 2 diabetes mellitus increased from 3.3 to 5.8% in the healthy central adiposity group, from 11.2 to 17.1% in the increased central adiposity group, and from 18.0 to 26.7% in the high central adiposity group. Males, older population, lower education level, lower household income, and smoking are associated with a higher risk of type 2 diabetes. In the high central adiposity group, overweight and obese individuals had higher susceptibility than underweight or normal-weight individuals, with ORs of 5.85 (95% CI, 2.54–13.47) and 8.24 (3.79–17.94), respectively. The prevalence of type 2 diabetes mellitus has increased in all central adiposity groups in the past decade. This underscores the need for tailored interventions to address disparities and improve diabetes management in at-risk populations.

Antibiotic Susceptibility among Infective Endocarditis Population: Syndromic Antibiogram Evaluation at Indonesian National Cardiovascular Center

Infective Endocarditis (IE) is a life-threatening disease with a high mortality rate (25%). Laboratory approaches to support the diagnosis of IE especially blood and/or cardiac vegetation culture, are crucial for providing definitive therapy. However, negative culture is frequently observed, therefore empirical use of antibiotics seems unavoidable and may lead to increasing antimicrobial resistance. This study aims to observe the prevalence of antibiotic susceptibility among the IE population using the WISCA approach. This retrospective study observed medical record data of all inpatients diagnosed with IE according to ICD-10 at Cardiovascular Center Harapan Kita Hospital (NCCHK) between January 2018 and December 2022. Patient data were combined with bacterial identification and antibiotic susceptibility test data from the laboratory information system, and evaluated based on period (year). A total of 67,858 inpatients and 1.1% (n=772) were diagnosed with IE. Successful culture growth (blood and/or cardiac vegetation specimen) was between 36.8% and 70% annually. Streptococcus viridans group (82/237, 34.6%) and Coagulase-negative Staphylococci (57/237, 24.1%) were dominantly observed among the IE population. Low susceptibility among Penicillin (34.3%) towards Gram-positive bacteria, as first-line treatment option in IE. Meanwhile, high susceptibility range was observed in Ceftriaxone (95.7%), Gentamicin (80.4%), Rifampicin (84.4%), and Vancomycin (98.7%) as second-line treatment in IE. Despite the low prevalence, the occurrence of antimicrobial resistance in IE has become a priority concern. Continuing the syndromic antibiogram is mandatory to assist the trend of empirical antibiotic usage and refine the established local treatment guidelines.

Chronic oral toxicity protocol for adult solitary bees (Osmia bicornis L.): Reduced survival under long-term exposure to a “bee-safe” insecticide

Pollinators are essential for crop productivity. Yet, in agricultural areas, they may be threatened by pesticide exposure. Current pesticide risk assessments predominantly focus on honey bees, with a lack of standardized protocols for solitary bees. This study addresses this gap by developing a long-term oral exposure protocol tailored for O. bicornis. We conducted initial trials to determine optimal container sizes and feeding methods, ensuring high survival rates and accurate syrup consumption measurements. A validation test involving five laboratories was then conducted with the insecticide Flupyradifurone (FPF). Control mortality thresholds were set at ≤ 15% at 10 days. Three laboratories achieved ≤10%, demonstrating the protocol's effectiveness in maintaining healthy test populations. The seasonal timing of experiments influenced control mortality, underscoring the importance of aligning tests with the natural flight period of the population used. Our findings revealed dose-dependent effects of FPF on syrup consumption, showing stimulatory effects at lower concentrations and inhibitory effects at higher ones. The 10-day median lethal daily dose (LDD50) of FPF for O. bicornis (531.92 ng/bee/day) was 3.4-fold lower than that reported for Apis mellifera (1830 ng/bee/day), indicating Osmia's higher susceptibility. Unlike other insecticides, FPF did not exhibit time-reinforced toxicity. This study introduces a robust protocol for chronic pesticide exposure in solitary bees, addressing a critical gap in current risk assessment. Based on its low risk to honey bees and bumblebees, FPF is approved for application during flowering. However, our results suggest that it may threaten Osmia populations under realistic field conditions. Our findings underscore the need for comparative toxicity studies to ensure comprehensive protection of all pollinators and the importance of accounting for long term exposure scenarios in risk assessment. By enhancing our understanding of chronic pesticide effects in solitary bees, our study should contribute to the development of more effective conservation strategies and sustainable agricultural practices.