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Background The past few decades have seen a marked increase in the macrovascular complications of Type-2 diabetes mellitus (T2DM) such as coronary heart disease, peripheral arterial disease, and cerebrovascular disease. This has been predominantly attributed to the increased atherosclerosis in these patients. Atherosclerosis usually remains an asymptomatic condition and this poses a significant challenge in its early diagnosis and timely intervention. Hence, there is an immediate need for exploring novel tools to aid in the early detection of atherosclerosis, especially in T2DM patients. Osteocalcin (OC), synthesized by osteoblasts, is a protein hormone found in the skeletal system. This protein is considered as a marker for bone density and in recent times has been gaining interest due to its protective role in cerebrovascular diseases(CVD). Methods We conducted a cross-sectional study and evaluated the association between serum OC levels and atherosclerosis in 113 T2DM patients. Carotid intima-media thickness (CC-IMT) was used as an estimate of atherosclerosis and patients were divided into two groups (CC-IMT < 0.9 and ≥ 0.9). Correlation of serum OC levels and glycemic parameters and lipid profiles were studied and compared between both groups. Results There is a significant negative correlation between the CC-IMT estimates and serum OC levels. CC-IMT also has a significant association with other biochemical parameters such as fasting blood sugar, glycated hemoglobin and high-density lipoprotein. Conclusion Although the independent association of serum OC could not be established in the T2DM patient population, overall, the results favor low serum OC as a prognostic marker for atherosclerosis.

Several plant-associated microbes have the capability of ameliorating the adverse effects of salinity stress in plants. Such microbes produce metabolites, including proline, glycine betaine, and secondary compounds, like melatonin, traumatic acid, and β-estradiol, which have been found to have a role in reducing salinity-induced damage in plant cells. While the effects of these metabolites have been studied, their application-related aspects remain underexplored. In this study, we investigated the salinity-stress-alleviating potential of metabolites derived from the endophytic bacterium Bacillus safensis BTL5. The microbial metabolites were extracted using the hexane–chloroform fraction method and identified through LC-HRMS analysis. Four metabolites (traumatic acid, β-estradiol, arbutin, and α-mangostin), along with a fifth compound, melatonin, were initially screened for their salinity alleviation potential. Subsequently, two metabolites, i.e., arbutin and β-estradiol, were evaluated for their impact on growth parameters and enzymatic antioxidant activities under 200 mM salt stress. The results revealed that arbutin and β-estradiol significantly improved plant growth, chlorophyll content, and enzymatic activities while reducing oxidative damage. The dose-dependent effects highlighted optimal concentrations for maximum efficacy from these compounds under elevated salinity. This study signifies the potential of microbial metabolites in enhancing crop resilience to salinity, highlighting their role in sustainable agriculture. The outcomes of this study provide a baseline for the applied use of such microbial metabolites under field conditions.

Emphysematous pyelonephritis (EPN) is a potentially fatal infection of the kidney, predominantly affecting diabetic patients. Despite advances in management, predictors of mortality remain incompletely understood. Analysing the factors associated with a high risk of death can help in identifying high-risk EPN patients and initiating early, aggressive therapy. This study was conducted with the aim of understanding and describing the clinical, biochemical, and microbiological profile of patients diagnosed with EPN and analysing the factors influencing mortality.  A retrospective observational study was conducted at Kasturba Hospital, Manipal (2017-2022). Data from 117 EPN patients were collected and analysed for demographic, clinical, laboratory, microbiological, and radiological characteristics. Primary endpoints used were successful treatment and all-cause mortality to assess markers of mortality. Risk factors for mortality were assessed using independent samples t-tests. Statistical significance was set at p<0.05. A total of 117 patients were included in our study. The mean age was 55.5 years, with a female predominance (56.4%). The most common symptom reported was flank pain (77.8%). Diabetes mellitus (DM) was the most common risk factor identified in 96.6% of patients, and Escherichia coli (E. coli) was the most common isolated organism (54.7%). Most patients had Class 1 (33.3%) and Class 2 (28.2%) disease. The average duration of inpatient treatment was reported to be 17.25 days in our patients. Treatment included antibiotics (94%) and drainage procedures, including DJ stenting (55%). Of the 117 patients, mortality occurred in seven patients (6%). Elevated serum creatinine at admission was significantly associated with mortality (p=0.004), while other laboratory markers (hemoglobin A1c (HbA1c), total leukocyte count (TLC), platelet count, sodium) did not show a significant correlation. Serum creatinine at admission emerged as a key predictor of mortality in EPN, emphasizing the need for early renal function assessment and close monitoring in high-risk patients. The low mortality rate observed in our cohort suggests that early intervention combining empirical antibiotic therapy guided by resistance patterns and minimally invasive drainage techniques may improve survival. Future prospective studies are needed to validate these findings and refine EPN management strategies.

Introduction: Emergency department (ED) flow could be improved with quicker disposition decisions. One possible way to expedite decisions is for triage nurses to make predictions about whether patients require admission to hospital. The information contained in these predictions could be useful for disposition planning and for physician decision-making. Previous studies made use of prospective designs that introduced Hawthorne effects and have demonstrated mixed evidence on whether triage nurse predictions are accurate. We examined the accuracy of triage nurse predictions for patient admission in an ED in southeastern Ontario. Methods: We examined a retrospective sample of 134,891 visits to an ED in Ontario from March 2019 – July 2024. Triage nurses made predictions about admission to hospital for these visits, from which we estimated measures of specificity, sensitivity, positive predictive value, negative predictive value, accuracy, and F1 scores. Results: Of 134,891 visits, 13.7% resulted in hospital admission. We found the accuracy of the nurses in predicting admission to be 85.8% (95% confidence interval [CI] 85.7 - 86.1), while overall sensitivity was 36.6% (95% CI 35.9 - 37.3) and specificity was 93.7% (95% CI 93.5 - 93.8). The positive predictive value of admission was 47.9% (95% CI 47.1 - 48.7), and the negative predictive value of admission was 90.3% (95% CI 90.1 - 90.5). F1 scores were 0.415. These results were relatively stable over time, although there was notable variation in prediction ability between nurses. We also report that some presenting conditions lead to relatively higher prediction accuracy than others and that as overall case severity increases, sensitivity increases and specificity decreases. Conclusion: These results suggest that although nursing staff predictions are insufficient to streamline disposition decisions completely, they could be useful in expediting certain decisions related to hospital admission and resource requirement, thereby improving flow in EDs.

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.

Wood is a wide flexible material appreciated extremely for its cost-effectiveness, great quantity, and biocompatibility. In addition, naturally existing materials possess prominent biomedical applications, and they can withstand efficiently when compared to other materials like glass, steel, and plastics. The present study revealed the prepared chitosan, silver nanoparticles incorporated with Borassus flabellifer trichome, and fabrication of Prosopis juliflora wood-based biomaterial. A characterization study was done by UV-visible spectroscopic analysis, FTIR analysis, and SEM analysis expressing and confirming a significant characteristic and morphological property of the prepared biomaterial.

Zirconium exhibited pseudo-passive behavior in fluorinated nitric acid (11.5 M HNO3 + 0.05 M NaF) as the current density measured from the electrochemical studies was several orders higher than the value in fluoride free nitric acid. Impedance studies on zirconium sample exposed in 11.5 M HNO3 for 240 h confirmed the formation of the passive film with high polarization resistance value and the calculated thickness of the film based on the capacitance value was about ~4.5 nm. On the other hand, in fluorinated nitric acid, the charge transfer resistance value associated with the zirconium dissolution process was dominant when compared to that of the film formation. Results of X-ray photoelectron spectroscopic investigations upheld the presence of ZrOF2 and ZrF4 and indicated that the protective oxide layer growth was restricted by the presence of fluoride ions.

We have investigated the effect of nitrogen (N 2 ) as a carrier gas on the growth of vertical graphene nanosheets (VGN) by plasma-enhanced chemical vapor deposition. It is demonstrated that addition of nitrogen gas with a hydrocarbon precursor can enhance the nucleation and growth rate of graphitic base layer as well as vertical sheets. Additionally, nitrogen gas can simultaneously act as an etchant as well as a dopant. Variation in the density of vertical sheets is found, and it increased initially thereafter decreased at higher nitrogen concentration. Furthermore, VGN exhibit sheet resistance from 0.89 to 1.89 KΩ/□ and mobility from 8.05 to 20.14 cm 2 /V-s, depending on the morphology and type of carrier concentration. These results reveal that the surface morphology and electronic properties of VGN can be tuned by incorporation of nitrogen gas during the growth.

Electrochemical noise (EN) studies were conducted on three austenitic stainless steels (SSs) with different molybdenum contents, 0.02 wt% Mo (Type 304LN SS), 2.53 wt% Mo (Type 316LN SS), and 3.58 wt% Mo (Type 317LN SS), in 0.01 M FeCl3 solution at the corrosion potential (Ecorr) and at a sampling frequency of 1 Hz. The EN data were analyzed using shot noise and wavelet analysis techniques. Current transient analysis showed that the total number of current transients, as well as transients with high current amplitude, decreased with increase in Mo content indicating increased resistance to pitting corrosion. Shot noise analysis revealed higher normalized characteristic charge (q)N at low frequency in Type 304LN SS as compared to Type 316LN SS and Type 317LN SS, implying increase in pitting corrosion resulting from the absence of Mo in this steel. Pit current decreased substantially with increase in Mo content. These results were supported by the standard deviation of partial signal (SDPS) values generated from wavelet analysis. High SDPS values for low-frequency components (d6 to d8) for Type 304LN SS indicated increased pit propagation rate. Higher pitting potentials (Epit) observed for high Mo-containing SSs revealed improved pitting corrosion resistance in these SSs. X-ray photoelectron spectroscopic and laser Raman spectroscopic analyses confirmed the presence of Mo oxides, Cr enrichment (through various Cr spinels), and polymolybdates in the passive films of Type 316LN and Type 317LN SSs, which imparted increased pitting resistance to these steels.