Psychosocial outcomes following emergency laparotomy (POLO) study: a study protocol for a multicentre mixed-methods prospective cohort study assessing the psycho-social outcomes following emergency laparotomy in adults.

INTRODUCTION: Morbidity from an emergency laparotomy (EmLap) is difficult to define and poorly understood. Morbidity is a holistic concept, reliant upon an interplay of bio-psychosocial outcomes that evolve long after discharge. To date, no previous study has explored the psychosocial outcomes following EmLap as a collective, nor their change over time. This study aims to describe the holistic morbidity following EmLap within the first year following surgery. METHODS AND ANALYSIS: This is a multicentre, mixed-methods prospective 12-month cohort study with two participant populations: patient participants and family caregivers (FCGs). A target of 160 adult patients who undergo EmLap and can give informed consent will be included in the patient participant group. Patient participants will be asked to complete three patient surveys, incorporating validated patient-reported outcome measures (PROMs) to assess bio-psychosocial outcomes (EuroQol five-dimension five-level (EQ5D-5L), Gastrointestinal Quality Life Index-36, Patient Health Questionnaire-9, Generalised Anxiety Disorder 7, International Trauma Questionnaire, Caregiver Interaction Scale and Fatigue Severity Scale) in the 12 months following surgery. A subgroup of 15 patient participants will be asked to take part in two semistructured interviews at 6 and 12 months. A target of 15 associated family caregivers will be included in the FCG group. FCGs will be asked to take part in a semi-structured interview at 6 months to assess the EmLap impact on the wider support network. The primary outcome will be a change in quality of life (EQ5D-5L) at 12 months. Secondary outcomes will be changes in bio-psychosocial status at 3 and 12 months. Qualitative analysis will allow contextualisation of PROMS and further explore themes of EmLap morbidity. It is anticipated that the results of this study will help inform and develop standards of aftercare for future EmLap patients. ETHICS AND DISSEMINATION: This study has received ethical approval (Wales REC7;12/WA/0297) and will be undertaken in accordance with the principles of Good Clinical Practice. We intend to disseminate study results in peer-reviewed journals and medical conferences, as well as a lay report to study participants. TRIAL REGISTRATION NUMBER: Clinical Trials.gov NCT05281627.

A novel technique for leaf disease classification using Legion Kernels with parallel support vector machine (LK-PSVM) and fuzzy C means image segmentation.

Detection of plant disease and classificationare being investigated in many parts of the worldto save precious medical plants from becoming extinct.Major problem in this task, include the lack of advanced and technology driven solution. Manual identification is often time-consuming and prone to inaccuracies. Therefore, there is an urgent need for an automated and efficient method that can accurately identify and classify plant diseases. This article focuses on detecting the disease through classificationthrough a new technique using leaf images for automatic classification. This paper proposes a novel segmentation technique using Fuzzy C means and Particle Swarm Optimization for effective segmentation of leaf images and feature extraction that can help in classification of disease.The approach emphasizes on the integration of techniques such as image processing, segmentation and feature extraction and finally the classification, which offers a comprehensive solution for the disease detection. The work leverages on the advantages of Legion Kernels and Parallal support vector Machine (LK-PSVM) clubbed with fuzzy C means Image segmentation to offer a framework that can handle diverse leaf images and which can effectively differentiate the type of the disease.The proposed method LK-PSVM combined with Fuzzy C means presents a novel approach that is significantly deviated from the conventional methods of leaf disease classification.The proposed wok brings an integrated framework which can synergistically combine the Legion Kernels with the PSVM technique coupled with Fuzzy C Means Image segmentation which can handle the issue of overlapped data sets and support vector machines are used to handle the situation where the number of dimensions are more than the number of samples, which is more probable in the classification problem under consideration.By integrating these components, the proposed method achieves more accuracy and robustness when compared to the existing methods in the literature. The segmentation is carried out using PSO after pre-processing of images. The Gaussian functions are used to eliminate the background subtraction. Different features of the images are then computed. A total of 55,400 images were used for the experiment consisting of various plants' leaves spreading across 38 labels. A classifier is then proposed using Machine learning methods for the detection of disease in apple fruit leaves. The experiments prove that the proposed method have high degree of classification accuracy when compared to existing methods. The proposed method not only cater to the need in terms of accuracy but also making it scalable for different types of leaves.

Potential magnetic drug targeting with magnetite nanoparticles in cancer treatment by enhancer-modifier natural herb and loaded drug.

In the present study, we prepared magnetite nanoparticles (MNPs) loaded with natural Moringa oleifera (M. olf) herb and Epilim (Ep) drug to evaluate the anti-cancerous activity against brain cancer cells. All the samples were prepared via co-precipitation approach modified with different concentrations of M. olf and Ep drug at room temperature. The MNPs loaded with drug and natural herb were studied in terms of crystal structure, morphology, colloidal stability, size distribution, and magnetic properties. Field emission scanning electron microscopy (FESEM) images exhibited the morphologies of samples with spherical shape as well as the particles size of 9 nm for MNPs and up to 23 nm for its composites. The results of vibrating sample magnetometer (VSM) indicated the magnetization saturation (Ms) of 42.510 emu/g for MNPs. This value reduced to 16-35 emu/g upon loading MNPs with different concentrations of M. olf and Ep. Fourier transform infrared spectroscopy (FTIR) indicated the chemical interaction between the Ep, M.olf and MNPs. Brunauer-Emmett-Teller (BET) analysis confirmed the largest surface area for MNPs (422.61 m2/g) which gradually reduced on addition of M. olf and Ep indicating the successful loading. The zeta potential measurements indicated that the MNPs and MNPs loaded with M. olf and Ep are negatively charged and can be dispersed in the suspension. Furthermore, U87 human glioblastoma cell line was used for the in vitro cellular studies to determine the efficacy of synthesized MNPs against cancer cells. The results confirmed the anti-proliferative activity of the MNPs loaded with M. olf and Ep.

Using PyCaret to model Chlorella vulgaris's growth response to salinity and oil contamination for crude oil bioremediation.

Crude oil spills significantly impact aquatic ecosystems, necessitating innovative remediation strategies. Microalgae-based bioremediation, particularly with Chlorella vulgaris, offers a promising solution. This study introduces a novel framework that evaluates the combined effects of selected environmental stressors on microalgal adaptability, advancing beyond traditional isolated factor analyses. By integrating a factorial experimental design with a machine learning approach using PyCaret AutoML and SHAP values, we provide a detailed examination of how crude oil concentration, salinity, and exposure duration affect C. vulgaris growth. The Extra Trees Regressor model emerged as highly accurate in predicting biomass concentration, a crucial adaptability indicator, achieving an MAE of 0.0202, RMSE of 0.029, and an R² of 0.8875. SHAP analysis highlighted salinity and crude oil as significant growth influencers, with exposure duration playing a minor role. Notably, C. vulgaris exhibited more sensitivity to salinity than to crude oil, indicating potential high-salinity challenges but also a strong tolerance to oil pollutants. These findings enhance our understanding of microalgal responses in polluted environments and suggest improved bioremediation approaches for saline waters affected by oil spills, leveraging the synergy of environmental factors and machine learning insights.

Nanoparticles in cancer theragnostic and drug delivery: A comprehensive review.

This comprehensive review provides an in-depth analysis of how nanotechnology has revolutionized cancer theragnostic, which combines diagnostic and therapeutic methods to customize cancer treatment. The study examines the unique attributes, uses, and difficulties linked to different types of nanoparticles, including gold, iron oxide, silica, Quantum dots, Carbon nanotubes, and liposomes, in the context of cancer treatment. In addition, the paper examines the progression of nanotheranostics, emphasizing its uses in precise medication administration, photothermal therapy, and sophisticated diagnostic methods such as MRI, CT, and fluorescence imaging. Moreover, the article highlights the capacity of nanoparticles to improve the effectiveness of drugs, reduce the overall toxicity in the body, and open up new possibilities for treating cancer by releasing drugs in a controlled manner and targeting specific areas. Furthermore, it tackles concerns regarding the compatibility of nanoparticles and their potential harmful effects, emphasizing the significance of continuous study to improve nanotherapeutic methods for use in medical treatments. The review finishes by outlining potential future applications of nanotechnology in predictive oncology and customized medicine.

Critical Review on the Effect and Mechanism of Realgar Nanoparticles on Lymphoma: State of the Art on In-Vitro Biomedical Studies.

Lymphoma is a malignant tumor caused by abnormal proliferation of lymphocytes in the lymphatic system. Conventional treatments for lymphoma often have limitations, and new therapeutic strategies need to be explored. Realgar is an ancient Chinese medicine that has been used for centuries to treat a variety of ailments due to its therapeutic potential for various diseases, including cancer. However, it is a time-consuming waste and has a low absorption rate in the gastrointestinal tract, so it has the disadvantages of oral dose, potential toxicity, and low bioavailability. Recently, the development of nanotechnology has promoted the nanization of realgar particles, which have better physicochemical properties and higher bioavailability. The antitumor activity of Realgar nanoparticles against lymphoma has been demonstrated in preclinical studies. Realgar nanoparticles exhibit cytotoxic effects by inducing apoptosis and inhibiting the growth and proliferation of lymphoma cells. Moreover, these nanoparticles exert immunomodulatory effects by enhancing the activity of immune cells and promoting the cytotoxicity of T lymphocytes against lymphoma cells. Additionally, realgar nanoparticles have been shown to inhibit tumor angiogenesis, thereby restricting the blood supply and nutrient availability to lymphoma cells. Despite promising preclinical data, further research on the role and mechanism of realgar nanoparticles in the treatment of lymphoma remains to be studied. Moreover, the translation of these findings into clinical practice requires rigorous evaluation through well-designed clinical trials. Realgar nanoparticles hold great potential as a novel therapeutic approach for lymphoma, and their development may contribute to the advancement of precision medicine in the field of oncology.

Liquid phase selective oxidation of veratryl alcohol to veratraldehyde using pure and Mg-doped copper chromite catalysts.

Mg-doped copper chromite (CuCr2O4) nanocomposites were synthesised through conventional technique. The pure and doped CuCr2-xMgx O4 (x = 0.00-0.1, 0.2 and 0.3%) nanocomposites were characterized in terms of their morphology, crystal structure, surface area and catalytic performance. The chemical composition of CuCr2-xMgx O4 was confirmed via FT-IR. The formation of pure and doped catalysts was validated by XRD results. TEM/SEM confirmed the formation of CuCr2-xMgxO4 nanoparticles. Mg-doped samples possess a high specific surface area compared to pure CuCr2O4. Thus, the effects of temperature, solvent, time, oxidant and the amount of catalyst on the oxidation of veratryl alcohol were reported. Furthermore, detailed mechanisms of the catalytic oxidation of veratryl alcohol as well as the reusability and stability of the nanomaterial were investigated. The resulting composites were shown to be effective heterogeneous catalysts for the oxidation of veratryl alcohol.

Reduction of Fossil Fuel Usage and Emissions of Diesel Engines by the Application of Quaternary Blends and Varied Fuel Injection Pressures.

The increasing energy demand has led to the exhaustion of mineral fuel resources and an environmental menace. Biodiesel and alcohol, as oxygenated fuels, offer promising potential for diesel engines. Moreover, the deviation in the fuel injection pressure (IP) favors improvement of the engine performance and reduction of flue gases. The contemporary research aims to explore sustainable biofuel that is an alternative to diesel and to achieve cleaner emissions with enhanced engine performance. The experiment involves testing of a diesel engine tank by quaternary blends comprising diesel, sunflower biodiesel, sunflower oil, and alcohol in the volumetric ratio of 50:25:5:20. The IP was varied from 300, 400, 500, to 600 bar at different engine loads of 10 and 20 N m at 1800 rpm of shaft speed. The quality of the quaternary blend was varied by the inclusion of alcohol having different carbon-chain lengths, namely, ethanol, propanol, butanol, heptanol, and decanol. The effect of alcohol inclusion and variation in the IP led to minimal brake-specific fuel consumption and maximal brake thermal efficiency for blended fuel containing 20% propanol, which was 17.39% lower and 8.70% higher than diesel, respectively. The same composition of the fuel blend offered the lowest smoke and CO2 emissions, which were 92.85 and 27.9% lesser than diesel; moreover, 7.36% lower NO x emission than diesel was achieved.

Harnessing potential role of gangliosides in immunomodulation and cancer therapeutics.

Gangliosides represent glycolipids containing sialic acid residues, present on the cell membrane with glycan residues exposed to the extracellular matrix (ECM), while the ceramides are anchored within the membrane. These molecules play a critical role in pathophysiological processes such as host-pathogen interactions, cell-cell recognition, signal transduction, cell adhesion, motility, and immunomodulation. Accumulated evidence suggests the overexpression of gangliosides on tumor tissues in comparison to healthy human tissues. These tumor-associated gangliosides have been implicated in various facets of tumor biology, including cell motility, differentiation, signaling, immunosuppression, angiogenesis, and metastasis. Consequently, these entities emerge as attractive targets for immunotherapeutic interventions. Notably, the administration of antibodies targeting gangliosides has demonstrated cytotoxic effects on cancer cells that exhibit an overexpression of these glycolipids. Passive immunotherapy approaches utilizing murine or murine/human chimeric anti-ganglioside antibodies have been explored as potential treatments for diverse cancer types. Additionally, vaccination strategies employing tumor-associated gangliosides in conjunction with adjuvants have entered the realm of promising techniques currently undergoing clinical trials. The present comprehensive review encapsulates the multifaceted roles of gangliosides in tumor initiation, progression, immunosuppression, and metastasis. Further, an overview is provided of the correlation between the expression status of gangliosides in normal and tumor cells and its impact on cancer patient survival. Furthermore, the discussion extends to ongoing and completed clinical trials employing diverse strategies to target gangliosides, elucidating their effectiveness in treating cancers. This emerging discipline is expected to supply substantial impetus for the establishment of novel therapeutic strategies.

The emerging role of human papillomavirus in lung cancer.

Lung cancer stands as one of the most lethal diseases and is the foremost cause of cancer-related mortalities worldwide. The pathophysiology of lung cancer is multifaceted, and it includes multiple cell signaling pathways and other complex factors such as oxidative stress and genetics. The association of HPV with lung carcinogenesis was first proposed in 1979, and since then, scientists worldwide have been putting forward several hypotheses to establish a relationship between this virus and lung cancer. Although studies have reported the presence of HPV in lung cancer, the exact mechanism of entry and the route of transmission have not been elucidated clearly till date. Numerous studies across the globe have detected differentially expressed HPV oncoproteins in lung cancer patients and found their association with the critical cell signaling pathways that leads to the development and progression of lung cancer. Many reports have also provided evidence stating the involvement of HPV in determining the survival status of lung cancer patients. The present review recapitulates the studies evincing the association of HPV and lung cancer, its route of transmission and mechanism of action; the detection of the virus and treatment opportunities for HPV-positive lung cancer; and the severity associated with this disease. Therefore, this will provide an explicit idea and would help to develop preventive measures and specific as well as effective treatment for HPV-associated lung carcinogenesis.

Long versus short course anti-microbial therapy of uncomplicated Staphylococcus aureus bacteraemia: a systematic review.

BACKGROUND: Current guidelines recommend at least 2 weeks duration of antibiotic therapy (DOT) for patients with uncomplicated Staphylococcus aureus bacteraemia (SAB) but the evidence for this recommendation is unclear. OBJECTIVES: To perform a systematic literature review assessing current evidence for recommended DOT for patients with SAB. METHODS: The following are the methods used for this study. DATA SOURCES: We searched MEDLINE, ISI Web of Science, the Cochrane Database and clinicaltrials.gov from inception to March 30, 2024. References of eligible studies were screened and experts in the field contacted for additional articles. STUDY ELIGIBILITY CRITERIA: All clinical studies, regardless of design, publication status and language. PARTICIPANTS: Adult patients with uncomplicated SAB. INTERVENTIONS: Long (>14 days; >18 days; 11-16 days) vs. short (≤14 days; 10-18 days; 6-10 days, respectively) DOT with the DOT being defined as the first until the last day of antibiotic therapy. ASSESSMENT OF RISK OF BIAS: Risk of bias was assessed using the ROBINS-I-tool. METHODS OF DATA SYNTHESIS: The primary outcome was 90-day all-cause mortality. Only studies presenting results of adjusted analyses for mortality were included. Data synthesis could not be performed. RESULTS: Eleven nonrandomized studies were identified that fulfilled the pre-defined inclusion criteria, of which three studies reported adjusted effect ratios. Only these were included in the final analysis. We did not find any RCT. Two studies with 1230 patients reported the primary endpoint 90-day all-cause mortality. Neither found a statistically significant superiority for longer (>14 days; 11-16 days) or shorter DOT (≤14 days; 6-10 days, respectively) for patients with uncomplicated SAB. Two studies investigated the secondary endpoint 30-day all-cause mortality (>18 days; 11-16 days vs. 10-18 days; 6-10 days, respectively) and did not find a statistically significant difference. All included studies had a moderate risk of bias. CONCLUSIONS: Sound evidence that supports any duration of antibiotic treatment for patients with uncomplicated SAB is lacking.

Predicting concrete strength early age using a combination of machine learning and electromechanical impedance with nano-enhanced sensors.

To ensure the structural integrity of concrete and prevent unanticipated fracturing, real-time monitoring of early-age concrete's strength development is essential, mainly through advanced techniques such as nano-enhanced sensors. The piezoelectric-based electro-mechanical impedance (EMI) method with nano-enhanced sensors is emerging as a practical solution for such monitoring requirements. This study presents a strength estimation method based on Non-Destructive Testing (NDT) Techniques and Long Short-Term Memory (LSTM) and artificial neural networks (ANNs) as hybrid (NDT-LSTMs-ANN), including several types of concrete strength-related agents. Input data includes water-to-cement rate, temperature, curing time, and maturity based on interior temperature, allowing experimentally monitoring the development of concrete strength from the early steps of hydration and casting to the last stages of hardening 28 days after the casting. The study investigated the impact of various factors on concrete strength development, utilizing a cutting-edge approach that combines traditional models with nano-enhanced piezoelectric sensors and NDT-LSTMs-ANN enhanced with nanotechnology. The results demonstrate that the hybrid provides highly accurate concrete strength estimation for construction safety and efficiency. Adopting the piezoelectric-based EMI technique with these advanced sensors offers a viable and effective monitoring solution, presenting a significant leap forward for the construction industry's structural health monitoring practices.

Unlocking the potential of Berberine: Advancing cancer therapy through chemosensitization and combination treatments.

Despite considerable progress in cancer treatment options, resistance to chemotherapeutic drugs remains a significant challenge. This review focuses on Berberine (BBR), an isoquinoline alkaloid found in various medicinal plants, which has garnered attention in the field of oncology for its anticancer potential either alone or in combination with other compounds and its ability to modulate chemoresistance, acting as a natural chemosensitizer. BBR's ability to modulate chemoresistance is attributed to its diverse mechanisms of action, including inducing DNA breaks, inhibition of drug efflux pumps, modulation of apoptosis and necroptosis, downregulating multidrug resistance genes, enhancing immune response, suppressing angiogenesis and targeting multiple pathways within cancer cells, including protein kinase B/mammalian target of rapamycin (Akt/mTOR), epidermal growth factor receptor (EGFR), mitogen-activated protein kinase (MAPK), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), poly(ADP-ribose) polymerase (PARP1), janus kinase/signal transducers and activators of transcription (JAK-STAT), Wnt/ß-catenin etc. Moreover, BBR, in combination with other compounds, also offers a promising approach to cancer therapy, enforcing its broad-spectrum anticancer effects. Therefore, this review aims to elucidate the intricate mechanism of action of BBR in combinatorial therapy as a potential chemosensitizer to increase the efficiency of several drugs, including cisplatin, doxorubicin, lapatinib, tamoxifen, irinotecan, niraparib, etc. in various cancers. Additionally, this review briefly covers the origin and biological activities of BBR, exploring the specific actions underlying its anticancer effects. Further, pharmacokinetic properties of BBR are also discussed, providing insight into its therapeutic potential and optimization of its use in cancer treatment.

Assessment of hydrocarbon degradation capacity and kinetic modeling of Chlorella vulgaris and Scenedesmus quadricauda for crude oil phycoremediation under mixotrophic conditions.

Crude oil spills imperil aquatic ecosystems globally, prompting innovative solutions such as microalgae-based bioremediation. This study explores the potential of Chlorella vulgaris and Scenedesmus quadricauda, for crude oil spill phycoremediation under mixotrophic conditions and varying crude oil concentrations (0.5-2%). C. vulgaris demonstrated notable resilience, thriving up to 1% crude oil exposure, while S. quadricauda adapted to lower concentrations. Optimal growth for both was observed at 0.5% exposure. Chlorophyll a content in C. vulgaris increases at 0.5% exposure but declines above 1%, while a decline was noticeable in chlorophyll b in treatment groups above 1%. Carotenoid levels varied, displaying the highest levels at higher concentrations above 1.5%. Similarly, S. quadricauda showed increased chlorophyll a content at 0.5% exposure, with stable carotenoid levels and a decline in chlorophyll b content at higher concentrations. GC/MS analyses indicated C. vulgaris efficiently degraded aliphatic compounds like decane and tridecane, surpassing S. quadricauda in degrading both aliphatic and aromatic hydrocarbons. Growth kinetics was best represented by the modified Gompertz and logistic models. These findings highlight the species-specific adaptability and optimal concentration for microalgae to degrade crude oil effectively, advancing phycoremediation processes and strategies critical for environmental restoration.

This study marks the first exploration of both Chlorella vulgaris and the previously unexplored Scenedesmus quadricauda for crude oil phycoremediation potential under mixotrophic conditions. Additionally, it pioneers the modeling and study of algae growth kinetics in response to crude oil exposure. Notably, this research demonstrated the adaptability and efficiency of C. vulgaris in degrading crude oil components under mixotrophic conditions up to a level of 1%, while S. quadricauda showed similar capabilities at a concentration of 0.5%.

Development and characterization of polyethylene oxide and guar gum-based hydrogel; a detailed in-vitro analysis of degradation and drug release kinetics.

Herein, we synthesized hydrogel films from crosslinked polyethylene oxide (PEO) and guar gum (GG) which can offer hydrophilicity, antibacterial efficacy, and neovascularization. This study focuses on synthesis and material/biological characterization of rosemary (RM) and citric acid (CA) loaded PEO/GG hydrogel films. Scanning Electron Microscopy images confirmed the porous structure of the developed hydrogel film matrix (PEO/GG) and the dispersion of RM and CA within it. This porous structure promotes moisture adsorption, cell attachment, proliferation, and tissue layer formation. Fourier Transform Infrared Spectroscopy (FTIR) further validated the crosslinking of the PEO/GG matrix, as confirmed by the appearance of C-O-C linkage in the FTIR spectrum. PEO/GG and PEO/GG/RM/CA revealed similar degradation and release kinetics in Dulbecco's Modified Eagle Medium, Simulated Body Fluid, and Phosphate Buffer Saline (degradation of ∼55 % and release of ∼60 % RM in 168 h.). The developed hydrogel film exhibited a zone of inhibition against Escherichia. coli (2 mm) and Staphylococcus. aureus (9 mm), which can be attributed to the presence of RM in the hydrogel film. Furthermore, incorporating CA in the hydrogel film promoted neovascularization, as confirmed by the Chorioallantoic Membrane Assay. The developed RM and CA-loaded PEO/GG-based hydrogel films offered suitable in-vitro properties that may aid in potential wound healing applications.

Mathematical modeling of heat transfer in tissues with skin tumor during thermotherapy.

The study of thermal therapy to tumors and the response of living cells to this therapy used to treat tumor is very important due to the complexity of heat transfer in biological tissues. In the past few years, there has been a growing interest among clinicians, mathematicians, and engineers regarding the use of computational and mathematical methods to simulate biological systems. Numerous medical proceedings also employ mathematical modeling and engineering techniques as a means to guarantee their safety and evaluate the associated risks effectively. This manuscript provides an analytical solution used for the first time to study the mechanism of biological thermal response during heat therapy on spheroidal skin tumor. The proposed method used a generalized thermoelasticity model with one relaxation time. The influence of relaxation times on the responses of diseased and healthy tissues is studied and interpreted graphically. Also, the impact of different laser irradiance on the thermal profile of the malignant tumor cells over a period of 2 minutes is interpreted graphically. To investigate the transfer of heat within biological tissues during the thermal therapy, the Laplace transform and inverse Laplace transform methods were applied. A comparison of the present generalized thermoelasticity model and different models based on Pennes bioheat transfer PBT shows that our proposed model yields more realistic and accurate predictions. The current model can be used to explain various therapeutic methods.

Nano-integrating green and low-carbon concepts into ideological and political education in higher education institutions through K-means clustering.

Universities and colleges play a pivotal role in the pursuit of a future that is sustainable through their pedagogical efforts and the execution of state-of-the-art research endeavors aimed at mitigating the effects of climate change. Higher Education Institutions (HEIs) serve as crucial catalysts in advancing sustainable development. HEIs are increasingly embracing precise measures to reduce their carbon footprint (CF) while also educating students on global sustainability. These nano-methods provide a quantitative framework for assessing a campus's sustainability efforts in line with Green Campus (GC) initiatives to lower carbon emissions align with GC goals. This study employs K-means clustering to analyze the integration of green and low-carbon principles in higher education political and ideological studies. Its goal is to identify patterns, assess teaching effectiveness, and improve sustainability education, aligning with Green Campus initiatives to enhance institutional contributions to sustainable growth through informed pedagogical strategies. Input data includes curriculum content, teaching methods, student engagement, and institutional goals related to sustainability. Seeking to improve sustainability education align with Green Campus initiatives, higher education can strategically enhance their contributions to long-term sustainability and growth through effective pedagogical approaches. Cluster 3 has the lowest WCSS value of 1200, indicating tighter cohesion and less variability within this cluster compared to Cluster 1 (1500) and Cluster 2 (1800). Cluster 3 stands out with the highest silhouette score of 0.7, suggesting well-defined and distinct clusters, while Cluster 2 has the lowest score of 0.4, indicating some overlap or ambiguity in data points. Cluster 1 has the lowest Davies-Bouldin Index of 0.4, implying better separation between clusters compared to Cluster 2 (0.6) and Cluster 3 (0.5). Cluster 3 is well-defined and cohesive, showing strong integration of green practices. Cluster 1 displays good separation and cohesion, while Cluster 2 requires refinement due to potential overlap in sustainability integration.

Surveillance of catheter-associated bloodstream infections: development and validation of a fully automated algorithm.

BACKGROUND: Most surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU). METHODS: We developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital's data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period. RESULTS: From January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50-70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06-0.41) and 3.86/1000 catheter days (95% CI: 3.17-4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7-96.9), 100% (95% CI 99.5-100), 100% (95% CI 56.5-100), and 99.9% (95% CI 99.2-100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm. CONCLUSIONS: A fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.

SKping cell cycle regulation: role of ubiquitin ligase SKP2 in hematological malignancies.

SKP2 (S-phase kinase-associated protein 2) is a member of the F-box family of substrate-recognition subunits in the SCF ubiquitin-protein ligase complexes. It is associated with ubiquitin-mediated degradation in the mammalian cell cycle components and other target proteins involved in cell cycle progression, signal transduction, and transcription. Being an oncogene in solid tumors and hematological malignancies, it is frequently associated with drug resistance and poor disease outcomes. In the current review, we discussed the novel role of SKP2 in different hematological malignancies. Further, we performed a limited in-silico analysis to establish the involvement of SKP2 in a few publicly available cancer datasets. Interestingly, our study identified Skp2 expression to be altered in a cancer-specific manner. While it was found to be overexpressed in several cancer types, few cancer showed a down-regulation in SKP2. Our review provides evidence for developing novel SKP2 inhibitors in hematological malignancies. We also investigated the effect of SKP2 status on survival and disease progression. In addition, the role of miRNA and its associated families in regulating Skp2 expression was explored. Subsequently, we predicted common miRNAs against Skp2 genes by using miRNA-predication tools. Finally, we discussed current approaches and future prospective approaches to target the Skp2 gene by using different drugs and miRNA-based therapeutics applications in translational research.