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The calibration of transfer functions is essential for accurate pavement performance predictions in the Pavement-ME design. Several studies have used the least square approach to calibrate these transfer functions. Least square is a widely used simplistic approach based on certain assumptions. Literature shows that these least square approach assumptions may not apply to the non-normal distributions. This study introduces a new methodology for calibrating the transverse cracking and international roughness index (IRI) models in rigid pavements using maximum likelihood estimation (MLE). Synthetic data for transverse cracking, with and without variability, are generated to illustrate the applicability of MLE using different known probability distributions (exponential, gamma, log-normal, and negative binomial). The approach uses measured data from the Michigan Department of Transportation's (MDOT) pavement management system (PMS) database for 70 jointed plain concrete pavement (JPCP) sections to calibrate and validate transfer functions. The MLE approach is combined with resampling techniques to improve the robustness of calibration coefficients. The results show that the MLE transverse cracking model using the gamma distribution consistently outperforms the least square for synthetic and observed data. For observed data, MLE estimates of parameters produced lower SSE and bias than least squares (e.g., for the transverse cracking model, the SSE values are 3.98 vs. 4.02, and the bias values are 0.00 and −0.41). Although negative binomial distribution is the most suitable fit for the IRI model for MLE, the least square results are slightly better than MLE. The bias values are −0.312 and 0.000 for the MLE and least square methods. Overall, the findings indicate that MLE is a robust method for calibration, especially for non-normally distributed data such as transverse cracking.

Pavement infrastructure is vital in providing services and links between various sectors of society. Therefore, the preservation and maintenance of these roads are critical to attaining a pavement network in good condition throughout its service life. Various performance indicators like the international roughness index (IRI), pavement condition index (PCI), and present serviceability rating (PSR) have been used by the state department of transportation (DOT) and highway agencies for evaluating pavement surface conditions and planning future maintenance strategies. Limited data availability, multiple distresses depending on region, lack of correlation of these condition indices to maintenance strategies, and data collection limitations pose a challenge for applying these indices to local conditions. This paper compares condition indices of different states for rigid pavements. Further, using a specific condition index for local conditions is also highlighted. For this purpose, five states and their corresponding condition indices were evaluated and compared to the Michigan DOT distress index (DI). These states include Virginia, Minnesota, North Dakota, Louisiana, and Oregon. The corresponding distresses of each condition index were converted to make them compatible with the MDOT DI. This study used the MDOT's pavement management system (PMS) database to evaluate each condition index for 433 rigid pavement sections. Each distress index was plotted against MDOT DI and compared using a paired t-test. Results show that the condition indices of Virginia and Minnesota are comparable to DI in terms of the Spearman correlation value. The t-test results show that except for Virgina, condition indices from other states statistically differ from DI. Therefore, one can't use those directly for local conditions in Michigan. This paper presents the evaluation and data requirements for each condition index and its impact on selecting a maintenance treatment.

Introduction: There is a lack of studies examining the most promising blood biomarkers for traumatic brain injury (TBI) in relation to gross pathology types. Research question: To examine whether the admission levels of blood biomarkers can discriminate patients with different combinations of traumatic intracranial findings from patients with negative computed tomography (CT) scans. Material and methods: One hundred thirty patients with all severities of TBI were studied. Seventy-five had CT-positive and 55 CT-negative findings. CT-positive patients were divided into three clusters (CL) using the Helsinki CT score: focal lesions (CL1), mixed lesions (CL2) and mixed lesions + intraventricular haemorrhage (CL3). CT scans were obtained upon admission and blood samples taken within 24 h from admission. S100 calcium-binding protein B (S100B), glial fibrillary acidic protein (GFAP), heart fatty-acid binding protein (H-FABP), neurofilament light (NF-L), interleukin-10 (IL-10), total-tau (t-tau), and β-amyloids 1–40 (Aβ40) and 1–42 (Aβ42) were analysed from plasma samples. CT-negative cluster was used as control. Results: GFAP, Aβ40 and Aβ42 levels differed between the clusters, but not significantly. NF-L and t-tau discriminated CL1 from CT-negative cluster with AUCs of 0.737 and 0.771, respectively. NF-L, t-tau and GFAP discriminated CL2 from CT-negative cluster with AUCs of 0.839, 0.781 and 0.840, respectively. All biomarkers analysed were able to discriminate CL3 and CT-negative cluster. Discussion and conclusion: All studied biomarkers distinguished the most severely injured cluster, CL3, from CT-negative cluster. The results may reflect the severity of TBI but also show that biomarkers have a variable ability to identify patients with combinations of intracranial traumatic lesions in the examined time window.

Background Traumatic brain injury (TBI) is associated with an increased risk of major depressive disorder (MDD) and post-traumatic stress disorder (PTSD). We aimed to identify predictors and develop models for the prediction of depression and PTSD symptoms at 6 months post-TBI.Methods We analysed data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury study. We used linear regression to model the relationship between predictors and depression (Patient Health Questionnaire-9) and PTSD symptoms (PTSD Checklist for Diagnostic and Statistical Manual for Mental Health Disorders Fifth Edition). Predictors were selected based on Akaike’s Information Criterion. Additionally, we fitted logistic models for the endpoints ‘probable MDD’ and ‘probable PTSD’. We also examined the incremental prognostic value of 2–3 weeks of symptoms.Results We included 2163 adults (76% Glasgow Coma Scale=13–15). Depending on the scoring criteria, 7–18% screened positive for probable MDD and about 10% for probable PTSD. For both outcomes, the selected models included psychiatric history, employment status, sex, injury cause, alcohol intoxication and total injury severity; and for depression symptoms also preinjury health and education. The performance of the models was modest (proportion of explained variance=R2 8% and 7% for depression and PTSD, respectively). Symptoms assessed at 2–3 weeks had a large incremental prognostic value (delta R2=0.25, 95% CI 0.24 to 0.26 for depression symptoms; delta R2=0.30, 95% CI 0.29 to 0.31 for PTSD).Conclusion Preinjury characteristics, such as psychiatric history and unemployment, and injury characteristics, such as violent injury cause, can increase the risk of mental health problems after TBI. The identification of patients at risk should be guided by early screening of mental health.

Summary: Background: Post-traumatic stress disorder (PTSD) and depression are common after mild traumatic brain injury (mTBI), but their biological drivers are uncertain. We therefore explored whether polygenic risk scores (PRS) derived for PTSD and major depressive disorder (MDD) are associated with the development of cognate TBI-related phenotypes. Methods: Meta-analyses were conducted using data from two multicenter, prospective observational cohort studies of patients with mTBI: the CENTER-TBI study (ClinicalTrials.gov ID NCT02210221) in Europe (December 2014–December 2017) and the TRACK-TBI study in the US (March 2014–July 2018). In both cohorts, the most common causes of injury were road traffic accidents and falls. Primary outcomes, specifically probable PTSD and depression, were defined at 6 months post-injury using scores ≥33 on the PTSD Checklist-5 and ≥15 on the Patient Health Questionnaire-9, respectively. We calculated PTSD-PRS and MDD-PRS for patients aged ≥17 years who had a Glasgow Coma Scale score of 13–15 upon hospital arrival and assessed their association with PTSD and depression following TBI. We also evaluated the transferability of the findings in a cohort of African Americans. Findings: Overall, 11.8% (219/1869) and 6.7% (124/1869) patients were classified as having probable PTSD and depression, respectively. The PTSD-PRS was significantly associated with higher adjusted odds of PTSD in both cohorts, with a pooled odds ratio (OR) of 1.55 [95% confidence interval (CI) 1.30–1.84, p

Introduction: Post-resuscitation care of cardiac arrest patients may be complicated by systemic inflammation elicited in response to whole-body ischaemia–reperfusion injury. We assessed the association between early WBC with one-year mortality in a large, unselected population of cardiac arrest patients. Methods: We collected a retrospective multicentre cohort of ICU-treated CA patients from the Finnish national ICU registry. We used locally estimated scatterplot smoothing (LOESS) curve to assess the association between the most abnormal WBC of the first 24 h in the ICU with the likelihood of death within a year. Multivariable logistic regression analyses were performed to assess the independent association between WBC and one-year mortality. In nested cohort analysis, we tested the association of delay from collapse to return of spontaneous circulation (ROSC) with WBC in linear regression models. Results: The LOESS curve was U-shaped, with the lowest predicted mortality at 7.5 109/L WBC. Based on this cut-off value, patients were divided into high (≥ 7.5 109/L) and low (< 7.5 109/L) WBC groups. In 4229 patients with high WBC, higher WBC was independently associated with increased one-year mortality (adjusted odds ratio (OR) 1.03 per 109/L, 95 % confidence interval (CI) 1.02–1.04, p

Introduction: The blood-based inflammatory marker, neutrophil-to-lymphocyte ratio (NLR), is a reliable prognostic biomarker for several cancers. Although the literature supports the correlation between preoperative NLR, clinicopathological characteristics, and oncological outcomes in upper tract urothelial carcinoma (UTUC), the cutoff of NLR is still debated. This study aimed to determine the prognostic value of NLR in patients with UTUC. Methods: This was a retrospective analysis of prospectively collected data from July 2012 to December 2022 evaluating patients with UTUC who underwent radical nephroureterectomy (RNU). NLR was calculated using the neutrophil and lymphocyte counts obtained a day before the surgery and the cutoff value was set as 2.5. Kaplan–Meier and Cox’s proportional hazards regression were used to analyze the association between NLR and the oncological outcomes. Results: The study included 91 patients (78 males, 13 females) in the final analysis with a median follow-up of 49 months (8–130). The mean age of the patients with NLR

The current study, guided by resource-based and signaling theories, investigated the mediator's function of employer branding in the connection between human resources practices and employee engagement. Data analysis for this research employed PLS-SEM, with a sample size comprising 220 individuals employed in various IT companies within the Delhi-NCR region. The restraint to this probe is that it is transverse analysis and employs quantitative research methodologies. The outcomes of the present work unveiled a direct and statistically substantial relationship among human resource practices and employee engagement. Furthermore, employer branding was identified as a mediator in the relationship between human resource practices and engagement among employees. This research provides targeted guidance for HR professionals and leaders in the IT sector, presenting in-depth insights into human resources dynamics, specifically focusing on employee engagement. Emphasizing the intermediary role of employer branding, our study empowers managers with actionable insights to enhance HR strategies, fostering heightened workplace satisfaction and increased employee engagement for a resilient and thriving workforce.

Summary: Background: The inflammatory response in patients with traumatic brain injury (TBI) offers opportunities for stratification and intervention. Previous unselected approaches to immunomodulation in patients with TBI have not improved patient outcomes. Methods: Serum and plasma samples from two prospective, multi-centre observational studies of patients with TBI were used to discover (Collaborative European NeuroTrauma Effectiveness Research [CENTER-TBI], Europe) and validate (Transforming Research and Clinical Knowledge in Traumatic Brain Injury [TRACK-TBI] Pilot, USA) individual variations in the immune response using a multiplex panel of 30 inflammatory mediators. Mediators that were associated with unfavourable outcomes (Glasgow outcome score-extended [GOS-E] ≤ 4) were used for hierarchical clustering to identify patients with similar signatures. Findings: Two clusters were identified in both the discovery and validation cohorts, termed early-inflammatory and pauci-inflammatory. The early-inflammatory phenotype had higher concentrations of interleukin-6 (IL-6), IL-15, and monocyte chemoattractant protein 1 (MCP1). Patients with the early-inflammatory phenotype were older and more likely to have an unfavourable GOS-E at 6 months. There were no differences in the baseline injury severity scores between patients in each phenotype. A combined IL-15 and MCP1 signature identified patients with the early-inflammatory phenotype in both cohorts. Inflammatory processes mediated outcomes in older patients with moderate-severe TBI. Interpretation: Our findings offer a precision medicine approach for future clinical trials of immunomodulation in patients with TBI, by using inflammatory signatures to stratify patients. Funding: CENTER-TBI study was supported by the European Union 7th Framework Programme. TRACK-TBI is supported by the National Institute of Neurological Disorders and Stroke.

Summary: Background: Traumatic brain injury is conventionally categorised as mild, moderate, or severe on the Glasgow Coma Scale (GCS). Recently developed biomarkers can provide more objective and nuanced measures of the extent of brain injury. Methods: Exposure–response relationships were investigated in 2479 patients aged ≥16 enrolled in the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) prospective observational cohort study. Neurofilament protein-light (NFL), ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), and glial fibrillary acidic protein (GFAP) were assayed from serum sampled in the first 24 h; concentrations were divided into quintiles within GCS severity groups. Relationships with the Glasgow Outcome Scale-Extended were examined using modified Poisson regression including age, sex, major extracranial injury, time to sample, and log biomarker concentration as covariates. Findings: Within severity groups there were associations between biomarkers and outcomes after adjustment for covariates: GCS 13–15 and negative CT imaging (relative risks [RRs] from 1.28 to 3.72), GCS 13–15 and positive CT (1.21–2.81), GCS 9–12 (1.16–2.02), GCS 3–8 (1.09–1.94). RRs were associated with clinically important differences in expectations of prognosis. In patients with GCS 3 (RRs 1.51–1.80) percentages of unfavourable outcome were 37–51% in the lowest quintiles of biomarker levels and reached 90–94% in the highest quintiles. Similarly, for GCS 15 (RRs 1.83–3.79), the percentages were 2–4% and 19–28% in the lowest and highest biomarker quintiles, respectively. Interpretation: Conventional TBI severity classification is inadequate and underestimates heterogeneity of brain injury and associated outcomes. The adoption of circulating biomarkers can add to clinical assessment of injury severity. Funding: European Union 7th Framework program (EC grant 602150), Hannelore Kohl Stiftung, One Mind, Integra LifeSciences, Neuro-Trauma Sciences, NIHR Rosetrees Trust.

The escalating consumption of crude oil, driven by rapid industrialization, poses a significant threat to terrestrial and marine ecosystems. Addressing the treatment of oily wastewater is imperative, given the environmental consequences and the loss of oil feedstock. Methods such as centrifugation, gravity separation, and chemical–physical membrane separation have limitations in handling stable emulsions, especially microemulsions present in water. In this study, we present an innovative approach utilizing electricity for the coagulation process. Electrocoagulation is employed, and the resulting oil particles are efficiently removed through low-tech physical methods such as sedimentation and filtration. To enhance efficiency and reusability, we introduce chitosan hydrogel-coated aluminium electrodes in the electrocoagulation process. The research demonstrates remarkable success, achieving a 99% efficiency in separating artificial oil–water emulsions. Optimal separation (95.85%) is achieved at a 10 mm inter-electrode distance, and efficiency increases with higher oil content, longer electrocoagulation time (up to 99.15% at 60 min), and increased voltage (up to 85% at 50 V). The chitosan-coated electrodes offer a sustainable and cost-effective solution by improving reusability and mitigating the need for frequent changes. These findings underscore the significance of electrocoagulation, particularly with chitosan-coated aluminium electrodes, as an efficient and eco-friendly method crucial for addressing oil removal in industrial processes. HIGHLIGHTS Chitosan hydrogel-coated aluminium electrodes enable electrocoagulation achieving close to 99% efficiency in separating oil–water emulsions.; Optimal conditions include a 10 mm inter-electrode distance and higher oil content.; Minimal process time.; Voltage (10–50 V).; The hydrogel coating enhances electrode reusability, resolving the issue of frequent replacements.;

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sensors, is vital due to frequent disruptions in hemodynamic regulation across various diseases. These sensors generate continuous high-frequency data streams, including intracranial pressure (ICP) and cerebral perfusion pressure (CPP), providing real-time insights into cerebral function. Analyzing these signals is crucial for understanding complex brain processes, identifying subtle patterns, and detecting anomalies. Computational models play an essential role in linking sensor-derived signals to the underlying physiological state of the brain. Multivariate machine learning models have proven particularly effective in this domain, capturing intricate relationships among multiple variables simultaneously and enabling the accurate modeling of cerebral physiologic signals. These models facilitate the development of advanced diagnostic and prognostic tools, promote patient-specific interventions, and improve therapeutic outcomes. Additionally, machine learning models offer great flexibility, allowing different models to be combined synergistically to address complex challenges in sensor-based data analysis. Ensemble learning techniques, which aggregate predictions from diverse models, further enhance predictive accuracy and robustness. This review explores the use of multivariate machine learning models in cerebral physiology as a whole, with an emphasis on sensor-derived signals related to hemodynamics, cerebral oxygenation, metabolism, and other modalities such as electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) where applicable. It will detail the operational principles, mathematical foundations, and clinical implications of these models, providing a deeper understanding of their significance in monitoring cerebral function.

Background: The coexistence of intracranial aneurysms and dysraphic cysts is a rare manifestation. The response triggered by the cyst and its proinflammatory content could play a role in the rupture or growth of a neighboring aneurysm. Methods: We performed a systematic review according to the PRISMA guidelines searching PubMed, Medline, Google Scholar and Embase. Additionally, we report a case of one patient with both a dermoid cyst and an ipsilateral anterior cerebral artery aneurysm, illustrating a progressive formation of the vascular lesion during the follow-up of the cyst. Results: Out of 103 papers fulfilling the inclusion criteria for the systematic review, 79 articles were selected for eligibility. Finally, a total of 9 cases, including our own case, were considered for the review. We found that all aneurysms were in the anterior circulation and all cysts were supratentorial. The aneurysms or parent vessels were in proximity or in contact with the dysraphic lesion, presenting a strong adherence to its capsule. The 3 dermoids presented with cysts rupture and showed simultaneous or delayed subarachnoid aneurysmal hemorrhage. Two epidermoid cysts were diagnosed after aneurysm rupture, while the others due to focal mass effect, as in the case of the neurenteric cyst. Conclusions: Coexistence of intracranial aneurysm and disraphic cyst is a rare finding with multiple possible presentations. In managing patients harboring disraphic cyst, the presence of the aneurysm should be ruled out during the standard workup. A coexistence of these pathologies could affect their natural histories, and this could justify a more active management protocol.

Early functional outcome assessments of traumatic brain injury (TBI) survivors may underestimate the long-term consequences of TBI. We assessed long-term temporal changes in functional outcome and quality of life in intensive care unit-managed long-term TBI survivors. This prospective, longitudinal study included 180 patients admitted to a single university hospital during 2000?2002 alive at 15 years post-TBI. Baseline characteristics, including imaging information, were collected. Functional outcome was assessed early (6?24 months) and late (15 years) using the Glasgow Outcome Scale (GOS) and the extended GOS (GOSE). Quality of life was measured at 15 years using the EuroQol Five Dimensions Five Levels (EQ-5D-5L) questionnaire. GOS and GOSE were dichotomized into favorable and unfavorable outcome. An index score was computed for EQ-5D-5L results at 15 years by a standardized valuation protocol. Of 180 patients, 118 replied to 15-year questionnaires. Median age at time of injury was 34 years (interquartile range, 19?45). Using the GCS to assess TBI severity, 67% had a moderate-to-severe TBI. Ninety-seven percent had favorable early functional outcome, and 72% had late favorable functional outcome. Logistic regression found higher age, lower GCS, and Marshall CT III to significantly predict late unfavorable functional outcome. Higher age and Marshall CT III were significant predictors of functional outcome deterioration. Median EQ-5D-5L index score for all patients was 0.88 (0.66?1.00) and correlated positively with GOSE. Most long-term TBI survivors with early favorable outcome also have late favorable functional outcome. Higher age and diffuse brain injury are associated with neurological deterioration. Quality of life was strongly linked to functional outcome.

Heavy metal contamination in soils and vegetation poses a significant problem due to its toxicity and persistence. Toxic effects on vegetation include not only impaired growth, reduced yields, and even plant death but also biodiversity loss and ecosystem degradation. Addressing this issue requires comprehensive monitoring and remediation efforts to mitigate the environmental, human health, and ecological impacts. This review examines the state-of-the-art methodologies and advancements in remote sensing applications for detecting and monitoring heavy metal contamination in soil and its subsequent effects on vegetation. By synthesizing the current research findings and technological developments, this review offers insights into the efficacy and potential of remote sensing for monitoring heavy metal contamination in terrestrial ecosystems. However, current studies focus on regression and AI methods to link spectral reflectances and indices to heavy metal concentrations, which poses limited transferability to other areas, times, spectral discretizations, and heavy metal elements. We conclude that one important way forward is the more thorough understanding and simulation of the related physico-chemical processes in soils and plants and their effects on the spectral signatures. This would offer a profound basis for remote sensing applications for individual circumstances and would allow disentangling heavy metal effects from other stressors such as droughts or soil salinity.

Recently, nanoparticles have received considerable attention owing to their efficiency in overcoming the limitations of traditional chemotherapeutic drugs. In our study, we synthesized a vanillic acid nanocomposite using both chitosan and silver nanoparticles, tested its efficacy against lung cancer cells, and analyzed its antimicrobial effects. We used several characterization techniques such as ultraviolet–visible spectroscopy (UV-Vis), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDAX), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) to determine the stability, morphological characteristics, and properties of the biosynthesized vanillic acid nanocomposites. Furthermore, the vanillic acid nanocomposites were tested for their antimicrobial effects against Escherichia coli and Staphylococcus aureus, and Candida albicans. The data showed that the nanocomposite effectively inhibited microbes, but its efficacy was less than that of the individual silver and chitosan nanoparticles. Moreover, the vanillic acid nanocomposite exhibited anticancer effects by increasing the expression of pro-apoptotic proteins (BAX, Casp3, Casp7, cyt C, and p53) and decreasing the gene expression of Bcl-2. Overall, vanillic acid nanocomposites possess promising potential against microbes, exhibit anticancer effects, and can be effectively used for treating diseases such as cancers and infectious diseases.

Background: Kidney failure and nephropathy, defined as a dysfunctional kidney with a decline in ultrafiltration and a rise in blood urea and creatinine levels, are frequently caused by diabetes. Aim: The motive of this research was to evaluate the concentration of blood urea and creatinine in individuals with diabetes, and also to examine the relationship between these parameters and the duration of diabetes as well as glycosylated hemoglobin concentration. Methods: Concentrations of urea and creatinine in the blood were evaluated in samples of Juvenile diabetes and Diabetes mellitus patients attending diabetic clinics as well as non-diabetic patients in a tertiary hospital. For the study, 144 male participants between the ages of 35 and 55 were chosen for each group. All trial participants' fasting blood sugar, post-meal blood sugar parameters, and glycosylated hemoglobin were ascertained. The relationship between blood creatinine and urea levels, glycosylated hemoglobin, and length of illness in all diabetes participants was examined. Results: In the type 1 study group, blood creatinine and urea levels were correlated with glycosylated hemoglobin levels and the length of diabetes, but not with the Diabetes mellitus study group. Serum creatinine (F-value = 50.96) and urea (F-value = 33.4) levels increased statistically significantly in the diabetes groups relative to the control group. Conclusion: Creatinine and urea are straightforward and practical indicators that can be used as predictive assays to evaluate the condition of the kidneys (nephropathy) in individuals with diabetes. Recommendations: Intensive treatment can address elevated HbA1c levels in diabetes, but it may not reverse rising serum urea and creatinine caused by permanent kidney damage. Early detection and intervention are crucial to control glomerular injury and prevent further increases in serum urea and creatinine levels.

Objectives: There exists a gap in understanding TBI prevalence within diverse risk groups in the country. Hence, this comprehensive review and pooled-analysis sought to determine the incidence of tuberculosis infections or TBI in the Indian subcontinent, considering geographical variations, socioeconomic profiles, and specific susceptible groups. Methods: By conducting an extensive review, this study explored Tuberculosis Infection (TBI) prevalence in India from 2013 to 2022. Utilizing databases like Scopus, CINAHL, EMBASE, and MEDLINE, 72 publications underwent an examination adhering to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA). The comprehensive approach included diverse languages and study settings, providing a thorough understanding of TBI prevalence in India. Results: On reviewing 10,515 documents, 72 studies comprising prevalence studies (approx. 42) and long-term studies (approx. 30) were incorporated for this paper. The overall prevalence of Tuberculosis Infection (TBI) in India, sourced from population-based sample studies, was established at 41%. This percentage held steady at 36 % when eliminating highly susceptible groups from the public at large. Regions grappling with heightened active TB burdens, especially in Tamil Nadu and Delhi, demonstrated a corresponding surge in TBI prevalence. Conclusion: The present comprehensive analysis unveiled the significant pervasiveness of Tuberculosis Infections in India, aligning with proactive TB infections, hinting at the potential transformation from latent form to active TB. Notably, this possibility was pronounced in individuals residing in the southern and parts of the country. Addressing these regional variations is crucial for adapting and prioritizing tailored strategies to effectively manage TBI in India. Recommendation: Our study thus recommends prioritizing Tuberculosis Preventive Treatment (TPT) in high Tuberculosis Infection (TBI) prevalence areas, advocating a 'No test, treat only' approach for resource efficiency to support effective programmatic management in pursuit of India's TB elimination goals.

Background: Removal of one or more kidneys may reduce renal parenchyma and GFR. Hence, gaining proper knowledge of clinical symptoms is essential before donating the kidney. The main aim of this study is to analyze the long-term renal and cardiovascular outcomes in living kidney donors. Materials and methods: It was a retrospective, observational study in a single center. A total of 62 living kidney donors from Darbhanga Medical College and Hospital in Laheriasarai, Bihar, from September 2022 to August 2023 were selected for this study. Their initial data (visit 0) during the time of the study to their last nephrological data (visit 1) were obtained and compared. Results: There was a huge drop in glomerular filtration rate which was about 28mL/min in a phase of 82±60 months. One donor underwent chronic kidney failure according to his last nephrological checkup (visit 1). There was no association between the age group and reduced glomerular filtration rate. Conclusion: The decrease in glomerular filtration rate as a consequence of kidney donation may cause an adverse reaction in renal and cardiovascular outcome. Recommendation: The age of the donor does not signify the occurring consequences and so older and healthy donor individuals are also recommended.

Introduction: Diabetes mellitus type 2 typically affects adults when the body doesn't produce enough insulin or grows resistant to it, leading to several micro- and macrovascular. This study used an activity treadmill test to assess the severity of silent myocardial ischemia, a significant macrovascular consequence, in a subset of asymptomatic diabetic outpatients. Material and Methods: The cross-sectional observational research was conducted from September 2022 to September 2023 at the Darbhanga Medical College and Hospital in Laheriasarai, Bihar, India. The study comprised 250 asymptomatic type 2 diabetes mellitus (DM) patients without clinical symptoms of coronary artery disease (CAD) and normal electrocardiograms. Patients with heart failure, angina pectoris, previous myocardial ischemia (MI), or any other chronic ailment were not eligible to participate in the trial. A silent myocardial infarction was identified by CASE/T2100 sr number GE MAC using a treadmill test (TMT) conducted following the Bruce protocol. Results: Silent MI was present in 12.8% of diabetic individuals with positive TMT. Diabetes for more than 10 years (p=0.001), Age (p=0.002), dyslipidemia (p=0.001), and having an HbA1c were all substantially associated with TMT favorable of more than 10% (p=0.001). It was found that TMT-positive individuals had significantly higher levels of triglycerides, LDL cholesterol, and total cholesterol than TMT-negative patients. Compared to TMT-negative patients, those who tested positive for TMT had significantly lower HDL levels. Conclusion: Many people experienced asymptomatic coronary artery disease or silent myocardial infarctions when they had type 2 diabetes. Silent MI was more common in diabetics who were overweight, dyslipidemic, older, and had increased HbA1C levels. When it first started, TMT might be an easy, non-invasive way to identify this. Recommendation: Being physically active helps you manage your diabetes because it increases your body's sensitivity to insulin, the hormone that permits your body's cells to use blood sugar for energy.

Background: The COVID-19 pandemic extends beyond respiratory issues. It impacts various organs, including the kidneys, leading to complications such as acute kidney injury. Objectives: This study aimed to examine the histological characteristics of kidney damage among individuals who succumbed to COVID-19 and to elucidate underlying factors contributing to renal pathology in individuals with COVID-19. Methods: Utilizing a cross-sectional design, data were extracted from the medical records of 301 patients at Darbhanga Medical College and Hospital in Bihar, India. The primary focus was on individuals concurrently afflicted with SARS-CoV-2 infection and various kidney conditions, encompassing acute kidney injury (AKI), chronic kidney disease (CKD), nephritic syndrome, nephrotic syndrome, etc. Diverse data elements, including demographics, clinical outcomes, treatment durations, diagnoses, and laboratory test results, were collected. Results: Key findings from this investigation revealed a positive correlation between elevated creatinine levels and older age, as well as an association between higher initial creatinine levels and shorter treatment duration. Notably, patients who did not survive exhibited significantly higher initial creatinine levels compared to discharged patients. Patients diagnosed with COVID-19 and AKI experienced an extended hospital stay compared to those without AKI. Moreover, individuals with AKI complicating CKD experienced a significantly extended hospitalization period compared to those without AKI. Importantly, individuals with AKI faced a substantially higher risk of mortality compared to subjects who were diagnosed with CKD only. Furthermore, individuals suffering from AKI had a considerably elevated risk of mortality when compared to individuals without CKD or AKI. Conclusion: This study highlights its impact on clinical outcomes, the importance of early creatinine measurement, and the risk associated with advanced age and AKI complications. Recommendation: Consider closely monitoring elderly individuals with COVID-19, as advanced age is identified as a significant risk factor for the development of AKI as a complication of CKD.

Barley is one of the most important cereal crops cultivated in all agricultural regions across the world. Barley is high in proteins, and carbohydrates, including Beta-glucan, lipids, vitamins, and minerals. Barley is gaining popularity among agricultural and culinary experts due to its unique chemical composition and health benefits. Beta-glucans, a kind of soluble fiber found in barley, may help decrease cholesterol levels. Consuming barley could reduce total cholesterol and low-density lipoprotein cholesterol. Barley also contains antioxidant qualities and may help manage blood sugar levels. It is thought to be a beneficial supplement to a balanced diet for persons at risk of cardiovascular disease. Barley has been shown to have antitumor properties through regulating the immune system and limiting cancer cell proliferation and dissemination. Furthermore, barley possesses antioxidant and anti-inflammatory characteristics, which may contribute to its anticancer potential. Barley and barley-based food products have a broad range of applications in a variety of sectors, including food, beverage, bread, animal feed, and cosmetic ingredients. This review investigates the phytochemicals found in barley, such as phenolic acid, flavonoids, lignans, tocols, phytosterols, and folates. This review also highlights the health benefits of barley, such as cholesterol and blood sugar reduction, anticancer activity, anti-oxidant and detoxifying properties, anti-inflammatory and anti-arthritic properties. This paper discusses the several properties, uses of barley, health promotion activity of barley and different aspects, as well as current and future advances in developing various value-added products of barley.

Abstract Background Brain tissue oxygen tension (PbtO2) and cerebrovascular pressure reactivity monitoring have emerged as potential modalities to individualize care in moderate and severe traumatic brain injury (TBI). The relationship between these modalities has had limited exploration. The aim of this study was to examine the relationship between PbtO2 and cerebral perfusion pressure (CPP) and how this relationship is modified by the state of cerebrovascular pressure reactivity. Methods A retrospective multi-institution cohort study utilizing prospectively collected high-resolution physiologic data from the CAnadian High Resolution-TBI (CAHR-TBI) Research Collaborative database collected between 2011 and 2021 was performed. Included in the study were critically ill TBI patients with intracranial pressure (ICP), arterial blood pressure (ABP), and PbtO2 monitoring treated in any one of three CAHR-TBI affiliated adult intensive care units (ICU). The outcome of interest was how PbtO2 and CPP are related over a cohort of TBI patients and how this relationship is modified by the state of cerebrovascular reactivity, as determined using the pressure reactivity index (PRx). Results A total of 77 patients met the study inclusion criteria with a total of 377,744 min of physiologic data available for the analysis. PbtO2 produced a triphasic curve when plotted against CPP like previous population-based plots of cerebral blood flow (CBF) versus CPP. The triphasic curve included a plateau region flanked by regions of relative ischemia (hypoxia) and hyperemia (hyperoxia). The plateau region shortened when cerebrovascular pressure reactivity was disrupted compared to when it was intact. Conclusions In this exploratory analysis of a multi-institution high-resolution physiology TBI database, PbtO2 seems to have a triphasic relationship with CPP, over the entire cohort. The CPP range over which the plateau exists is modified by the state of cerebrovascular reactivity. This indicates that in critically ill TBI patients admitted to ICU, PbtO2 may be reflective of CBF.

Summary: Background: Limited evidence existed on the comparative effectiveness of decompressive craniectomy (DC) versus craniotomy for evacuation of traumatic acute subdural hematoma (ASDH) until the recently published randomised clinical trial RESCUE-ASDH. In this study, that ran concurrently, we aimed to determine current practice patterns and compare outcomes of primary DC versus craniotomy. Methods: We conducted an analysis of centre treatment preference within the prospective, multicentre, observational Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (known as CENTER-TBI) and NeuroTraumatology Quality Registry (known as Net-QuRe) studies, which enrolled patients throughout Europe and Israel (2014–2020). We included patients with an ASDH who underwent acute neurosurgical evacuation. Patients with severe pre-existing neurological disorders were excluded. In an instrumental variable analysis, we compared outcomes between centres according to treatment preference, measured by the case-mix adjusted proportion DC per centre. The primary outcome was functional outcome rated by the 6-months Glasgow Outcome Scale Extended, estimated with ordinal regression as a common odds ratio (OR), adjusted for prespecified confounders. Variation in centre preference was quantified with the median odds ratio (MOR). CENTER-TBI is registered with ClinicalTrials.gov, number NCT02210221, and the Resource Identification Portal (Research Resource Identifier SCR_015582). Findings: Between December 19, 2014 and December 17, 2017, 4559 patients with traumatic brain injury were enrolled in CENTER-TBI of whom 336 (7%) underwent acute surgery for ASDH evacuation; 91 (27%) underwent DC and 245 (63%) craniotomy. The proportion primary DC within total acute surgery cases ranged from 6 to 67% with an interquartile range (IQR) of 12–26% among 46 centres; the odds of receiving a DC for prognostically similar patients in one centre versus another randomly selected centre were trebled (adjusted median odds ratio 2.7, p

Canopy height is an important crop biophysical parameter. It provides information about the crop growth as well as act as an input parameter for biomass and crop yield models. Considering the importance of this parameter, a novel semi-automatic canopy height estimation model has been developed which can work with both georeferenced or non-georeferenced top-of-canopy aerial images. The model employs a Structure-from-Motion algorithm followed by dense point cloud reconstruction and polygon triangulation to obtain polygon meshes which are used for height estimation. The process has been tested on drone-based data collected from a maize crop over the 2018-19 Rabi season from a semi-arid area in central-south India. The ground truth canopy height was measured by manually measuring height of plants using a meter scale. The ground elevation has been modelled using a linear best fit plane and the estimated canopy height was found to have the best R2 value of 0.85 and RMSE values of 14.17 cm.

Epithelioid inflammatory myofibroblastic sarcoma (EIMS) is an aggressive subtype of inflammatory myofibroblastic tumor (IMT) harboring anaplastic lymphoma kinase (ALK) gene fusions and is associated with high risk of local recurrence and poor prognosis. Herein, we present a young, non-smoking male who presented with complaints of cough and dyspnoea and was found to harbor a large right lower lobe lung mass. Biopsy showed a high-grade epithelioid to rhabdoid tumor with ALK and desmin protein expression. The patient initially received 5 cycles of crizotinib and remained stable for 1 year; however, he then developed multiple bony metastases, for which complete surgical resection was performed. Histopathology confirmed the diagnosis of EIMS, with ALK gene rearrangement demonstrated by fluorescence in situ hybridization. Postoperatively, the patient is asymptomatic with stable metastatic disease on crizotinib and has been started on palliative radiotherapy. EIMS is a very rare subtype of IMT that needs to be included in the differential diagnosis of ALKexpressing lung malignancies in young adults.

Abstract Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.

The microbial enhanced oil recovery (MEOR) method is an eco-friendly and economical alternative technology. The technology involves a variety of uncertainties, and its success depends on controlling microbial growth and metabolism. This study is one of a kind that showed successful tertiary recovery of crude oil through indigenous microbial consortia. In this study, a medium was optimized to allow ideal microbial growth under reservoir conditions through RSM. Once the nutrient recipe was optimized, the microbial metabolites were estimated through gas chromatography. The maximum amount of methane gas (0.468 mM) was produced in the TERIW174 sample. The sequencing data set showed the presence of Methanothermobacter sp. and Petrotoga sp. In addition, these established consortia were analyzed for their toxicity, and they appeared to be safe for the environment. Furthermore, a core flood study showed efficient recovery that was ~25 and 34% in TERIW70 and TERIW174 samples, respectively. Thus, both the isolated consortia appeared to be suitable for the field trials.

Brain tissue oxygen tension (PbtO2) has emerged as a cerebral monitoring modality following traumatic brain injury (TBI). Near-infrared spectroscopy (NIRS)-based regional cerebral oxygen saturation (rSO2) can non-invasively examine cerebral oxygen content and has the potential for high spatial resolution. Past studies examining the relationship between PbtO2 and NIRS-based parameters have had conflicting results with varying degrees of correlation. Understanding this relationship will help guide multimodal monitoring practices and impact patient care. The aim of this study is to examine the relationship between PbtO2 and rSO2 in a cohort of TBI patients by leveraging contemporary statistical methods. A multi-institutional retrospective cohort study of prospectively collected data was performed. Moderate-to-severe adult TBI patients were included with concurrent rSO2 and PbtO2 monitoring during their stay in the intensive care unit (ICU). The high-resolution data were analyzed utilizing time series techniques to examine signal stationarity as well as the cross-correlation relationship between the change in PbtO2 and the change in rSO2 signals. Finally, modeling of the change in PbtO2 by the change in rSO2 was attempted utilizing linear methods that account for the autocorrelative nature of the data signals. A total of 20 subjects were included in the study. Cross-correlative analysis found that changes in PbtO2 were most significantly correlated with changes in rSO2 one minute earlier. Through mixed-effects and time series modeling of parameters, changes in rSO2 were found to often have a statistically significant linear relationship with changes in PbtO2 that occurred a minute later. However, changes in rSO2 were inadequate to predict changes in PbtO2. In this study, changes in PbtO2 were found to correlate most with changes in rSO2 approximately one minute earlier. While changes in rSO2 were found to contain information about future changes in PbtO2, they were not found to adequately model them. This strengthens the body of literature indicating that NIRS-based rSO2 is not an adequate substitute for PbtO2 in the management of TBI.

Background and aimPrediction models for patients with traumatic brain injury (TBI) require generalizability and should apply to different settings. We aimed to validate the IMPACT and Helsinki prognostic models in patients with TBI who underwent cranial surgery in a Chinese center.MethodsThis validation study included 607 surgical patients with moderate to severe TBI (Glasgow Coma Scale [GCS] score ≤12) who were consecutively admitted to the Neurotrauma Center of People's Liberation Army (PLANC), China, between 2009 and 2021. The IMPACT models (core, extended and lab) and the Helsinki CT clinical model were used to estimate 6-month mortality and unfavorable outcomes. To assess performance, we studied discrimination and calibration.ResultsIn the PLANC database, the observed 6-month mortality rate was 28%, and the 6-month unfavorable outcome was 52%. Significant differences in case mix existed between the PLANC cohort and the development populations for the IMPACT and, to a lesser extent, for the Helsinki models. Discrimination of the IMPACT and Helsinki models was excellent, with most AUC values ≥0.80. The highest values were found for the IMPACT lab model (AUC 0.87) and the Helsinki CT clinical model (AUC 0.86) for the prediction of unfavorable outcomes. Overestimation was found for all models, but the degree of miscalibration was lower in the Helsinki CT clinical model.ConclusionIn our population of surgical TBI patients, the IMPACT and Helsinki CT clinical models demonstrated good performance, with excellent discrimination but suboptimal calibration. The good discrimination confirms the validity of the predictors, but the poorer calibration suggests a need to recalibrate the models to specific settings.

Abstract Background Cardiac arrest (CA) is a leading cause of death worldwide. As population ages, the need for research focusing on CA in elderly increases. This study investigated treatment intensity, 12-month neurological outcome, mortality and healthcare-associated costs for patients aged over 75 years treated for CA in an intensive care unit (ICU) of a tertiary hospital. Methods This single-centre retrospective study included adult CA patients treated in a Finnish tertiary hospital’s ICU between 2005 and 2013. We stratified the study population into two age groups:

The biophysical properties of a crop are a good indicator of potential crop stress conditions. However, these visible properties cannot indicate areas exhibiting non-visible stress, e.g., early water or nutrient stress. In this research, maize crop biophysical properties including canopy height and Leaf Area Index (LAI), estimated using drone-based RGB images, were used to identify stressed areas in the farm. First, the APSIM process-based model was used to simulate temporal variation in LAI and canopy height under optimal management conditions, and thus used as a reference for estimating healthy crop parameters. The simulated LAI and canopy height were then compared with the ground-truth information to generate synthetic data for training a linear and a random forest model to identify stressed and healthy areas in the farm using drone-based data products. A Healthiness Index was developed using linear as well as random forest models for indicating the health of the crop, with a maximum correlation coefficient of 0.67 obtained between Healthiness Index during the dough stage of the crop and crop yield. Although these methods are effective in identifying stressed and non-stressed areas, they currently do not offer direct insights into the underlying causes of stress. However, this presents an opportunity for further research and improvement of the approach.

Measurements leading to the collapse of states and the non-local quantum correlations are the key to all applications of quantum mechanics as well as in the studies of quantum foundation. The former is crucial for quantum parameter estimation, which is greatly affected by the physical environment and the measurement scheme itself. Its quantification is necessary to find efficient measurement schemes and circumvent the non-desirable environmental effects. This has led to the intense investigation of quantum metrology, extending the Cramér–Rao bound to the quantum domain through quantum Fisher information. Among all quantum states, the separable ones have the least quantumness; being devoid of the fragile non-local correlations, the component states remain unaffected in local operations performed by any of the parties. Therefore, using these states for the remote design of quantum states with high quantum Fisher information can have diverse applications in quantum information processing; accurate parameter estimation being a prominent example, as the quantum information extraction solely depends on it. Here, we demonstrate that these separable states with the least quantumness can be made extremely useful in parameter estimation tasks, and further show even in the case of the shared channel inflicted with the amplitude damping noise and phase flip noise, there is a gain in Quantum Fisher information (QFI). We subsequently pointed out that the symmetric W states, incapable of perfectly teleporting an unknown quantum state, are highly effective for remotely designing quantum states with high quantum Fisher information.

ObjectiveSpine injury is highly prevalent in patients with poly-trauma, but data on the co-occurrence of spine trauma in patients with traumatic brain injury (TBI) are scarce. In this study, we used the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) database to assess the prevalence, characteristics, and outcomes of patients with TBI and a concurrent traumatic spinal injury (TSI).MethodsData from the European multi-center CENTER-TBI study were analyzed. Adult patients with TBI (≥18 years) presenting with a concomitant, isolated TSI of at least serious severity (Abbreviated Injury Scale; AIS ≥3) were included. For outcome analysis, comparison groups of TBI patients with TSI and systemic injuries (non-isolated TSI) and without TSI were created using propensity score matching. Rates of mortality, unfavorable outcomes (Glasgow Outcome Scale Extended; GOSe < 5), and full recovery (GOSe 7–8) of all patients and separately for patients with only mild TBI (mTBI) were compared between groups at 6-month follow-up.ResultsA total of 164 (4%) of the 4,254 CENTER-TBI core study patients suffered from a concomitant isolated TSI. The median age was 53 [interquartile range (IQR): 37–66] years and 71% of patients were men. mTBI was documented in 62% of cases, followed by severe TBI (26%), and spine injuries were mostly cervical (63%) or thoracic (31%). Surgical spine stabilization was performed in 19% of cases and 57% of patients were admitted to the ICU. Mortality at 6 months was 11% and only 36% of patients regained full recovery. There were no significant differences in the 6-month rates of mortality, unfavorable outcomes, or full recovery between TBI patients with and without concomitant isolated TSI. However, concomitant non-isolated TSI was associated with an unfavorable outcome and a higher mortality. In patients with mTBI, a negative association with full recovery could be observed for both concomitant isolated and non-isolated TSI.ConclusionRates of mortality, unfavorable outcomes, and full recovery in TBI patients with and without concomitant, isolated TSIs were comparable after 6 months. However, in patients with mTBI, concomitant TSI was a negative predictor for a full recovery. These findings might indicate that patients with moderate to severe TBI do not necessarily exhibit worse outcomes when having a concomitant TSI, whereas patients with mTBI might be more affected.

Polarimetric synthetic aperture radar remote sensing extracts the information about the target using decomposition models to separate the polarimetric information into single-bounce (contributed by smooth surfaces), double-bounce (contributed by urban structure), and volume (mainly due to vegetation cover) scattering components. The penetration capacity of the electromagnetic wave into the surface increases with the decrease in its frequency. This study explores and compares the polarimetric decomposition models for scattering-based characterization of land use and cover using multifrequency spaceborne synthetic aperture radar sensor datasets that were acquired over San Francisco, CA, USA. The present work compares the scattering parameters of coherent (Pauli), roll-invariant (Barnes), eigenvalue–eigenvector (Cloude), and compact-polarimetric (Raney) decomposition modeling approaches for scattering-based characterization of urban structures, waterbody, and vegetation cover. The land use/cover classification was performed based on the scattering response of the scatterers using a support vector machine classifier. The outputs of the classification approach on multisensor, multifrequency, and multi-polarization polarimetric synthetic aperture radar data have shown reasonable accuracy in classifying the land use and land cover. The decomposition models fail to characterize the oriented urban structures that cause misclassification of urban structures as vegetation. The higher-order roll-invariant decomposition modeling approaches could improve the interpretation of different targets and accuracy in land use and land cover classification.

Introduction: Neurocognitive problems associated with posttraumatic stress disorder (PTSD) can interact with impairment resulting from traumatic brain injury (TBI). Research question: We aimed to identify neurocognitive problems associated with probable PTSD following TBI in a civilian sample. Material and methods: The study is part of the CENTER-TBI project (Collaborative European Neurotrauma Effectiveness Research) that aims to better characterize TBI. For this cross-sectional study, we included patients of all severities aged over 15, and a Glasgow Outcome Score Extended (GOSE) above 3. Participants were assessed at six months post-injury on the PTSD Checklist-5 (PCL-5), the Trail Making Test (TMT), the Rey Auditory Verbal Learning Test (RAVLT) and the Cambridge Neuropsychological Test Automated Battery (CANTAB). Primary analysis was a complete case analysis. Regression analyses were performed to investigate the association between the PCL-5 and cognition. Results: Of the 1134 participants included in the complete case analysis, 13.5% screened positive for PTSD. Probable PTSD was significantly associated with higher TMT-(B-A) (OR ​= ​1.35, 95% CI: 1.14–1.60, p ​

Remote estimation of leaf nitrogen content is a critical requirement for precision farm management. Precise knowledge of nitrogen distribution in the crop enables farmers to decide the fertilisation amount required at specific locations on the farm. Importantly, nitrogen related molecules in plants are transported using water molecules, and water molecules surround the amide bonds (a plant protein created from nitrogen). Consequently, the nitrogen in various crop parts loses its activity in the absence of sufficient water molecules. The association of water molecules around plant proteins makes the optical remote estimation of plant nitrogen challenging as nitrogen and water molecules simultaneously affect the reflectance data. Moreover, the coarse spatial resolution of satellite data and sparse canopy coverage at early growth stages of the crop make it challenging to estimate leaf-level nitrogen contents. Accordingly, this research developed a leaf nitrogen content estimation model using drone-based top-of-canopy 400–1000 nm pure pixel hyperspectral images collected from a maize research farm treated with different water and nitrogen levels. Leaf level spectral signatures were also collected using a field spectroradiometer and used to identify indices more sensitive to nitrogen than water. The leaves were also destructively sampled for obtaining ground truth leaf water and nitrogen content. Red-edge region bands of electromagnetic spectra were identified to be sensitive to leaf nitrogen content. A synthetic data was created using maximum and minimum values of these indices and crop growth stage information, which was further used for training a gradient-boosting machine model to estimate leaf nitrogen content from drone-based hyperspectral images. The estimated leaf nitrogen content values from drone observations were critically analysed with respect to leaf water content values. For water-stressed areas, the model gave an R2 and RMSE of 0.63 and 2.74 mg/g, respectively. However, the model did not perform adequately for well irrigated areas, having an R2 and RMSE of 0.26 and 4.54 mg/g, respectively.

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time.

Introduction: Mucosal derived galactose deficient IgA is central to the pathogenesis of primary IgA nephropathy. Recent reports suggest similar pathogenesis in Henoch Schonlein purpura and secondary IgA nephropathy. Its role in other IgA containing glomerular diseases is still under investigation. It can be detected in glomeruli with the recently described antibody, KM55. We aimed to evaluate the role of KM55 by immunostaining a wide spectrum of IgA containing glomerular diseases. Methods: After standardization and co localization in a case of IgA nephropathy, a spectrum of 60 cases including IgA nephropathy, Henoch Schonlein purpura, chronic liver disease related IgA nephropathy, other secondary IgA Nephropathy, IgA dominant/co dominant membranoproliferative glomerulonephritis and lupus nephritis were subjected to immunofluorescence with KM55. KM55 was used to resolve diagnostic dilemma in cases of IgA deposition with confounding histology. Results: The group of primary IgA Nephropathy (17 cases), Henoch Schonlein purpura (4 cases) and secondary IgA nephropathy (19 cases) including chronic liver disease showed 2 -3+ granular staining with KM55 suggesting mucosal derived IgA. In contrast, cases of IgA dominant/co dominant membranoproliferative glomerulonephritis (8 cases) and lupus nephritis (12 cases) were negative for KM55, suggesting systemic derivation of IgA. In cases of IgA deposition with confounding histology such as membranoproliferative or diffuse endocapillary proliferative pattern, KM55 helped to resolve the diagnosis. Discussion/Conclusion: This cross-sectional study concludes that KM55 is useful in the evaluation of IgA containing glomerular diseases from a pathogenetic perspective, and is a practical tool in resolving differential diagnosis in cases with overlapping histopathologic features.

Remotely sensed estimation of leaf water content (LWC) using optical data at early crop growth stage is important for identification of water-stressed plants. However, its accurate estimation is currently a major challenge due to the coarse spatial and spectral resolution of the available optical data, and the atmospheric impact on satellite-based remotely sensed data. Moreover, during early growth stages the canopy coverage is low, increasing the effect of the bare soil background on low spatial resolution data. Consequently, broadband optical data is insensitive to overtone frequencies of O-H stretching bonds of water molecules. Accordingly, this research developed a new model for estimating LWC based on newly identified, pure-pixel, water sensitive indices from high spatial resolution hyperspectral data. A hand-held field spectroradiometer and drone-based hyperspectral imager were used to collect temporal high spectral resolution hyperspectral data (Range: 400–1000 nm; Bandwidth: ~2.1 nm) at leaf level, together with destructively sampled leaves to measure their LWC using the oven-drying method. The spectroradiometer data were used to explore the wavelengths sensitive to vibrational overtone frequencies of O-H bonds of water molecules present in leaves. A total of seven water-sensitive wavelengths were identified, and corresponding normalised indices created for use with pure pixel narrowband hyperspectral data from vegetation. Farm scale maps of LWC were then created using drone-based hyperspectral data, based on minimum and maximum values of the above indices and ‘days after sowing’ information, through a gradient boost machine (GBM) model. The early growth stage maps of LWC were able to distinguish between water-stressed and well-irrigated plots with an R2 of 0.93 and RMSE of 1.6% (g/g).

Leaf Area Index (LAI) is one of the most important biophysical properties of a crop, used in detecting long-term water stress, estimating biomass, and identifying crop growth stage. Remote sensing based LAI estimation techniques perform well for early growth stages but tend to produce high error during the crop reproductive stage due to canopy closure. Moreover, estimation of the true LAI from individual leaf measurements remains a challenge. Consequently, two alternate methods have been developed and compared for estimating the LAI of a maize crop using top-of-canopy RGB images collected throughout the growing season using a hexacopter. Both methods used the RGB images to estimate the canopy height and the green-canopy cover together with a ‘vertical leaf area distribution factor’ (VLADF) from allometric relations (using crop height from RBG images and days after sowing). The first method used an empirical approach to estimate the LAI from training a linear function of the above inputs to Licor canopy analyser values of LAI. The method was trialled for a research farm located in a semi-arid area of southern peninsula India and found to have validation results with an R2 of 0.84 and RMSE of 0.36 for the unused portion of the Rabi (post-monsoon) season data of 2018–19, and R2 of 0.77 and RMSE of 0.45 for the Rabi 2019–20 season data when compared with Licor LAI values. While seemingly acceptable, the Licor canopy analyser gives a foliage area index and so the accuracy of this model was very low (R2 of 0.56 and RMSE of 1.34) when evaluated with true LAI from manual measurements of the leaf area. Consequently, a refinement was introduced using only VLADF, green-canopy cover estimates from the RBG images, and a field measured top leaf angle. The output derived from this conceptual model had an R2 of ~0.6 and RMSE of 0.73 when compared with the true LAI values. Importantly, the LAI from this conceptual model was found to be unaffected by canopy closure during the reproductive stage of the crop.

Background: Blood biomarkers may enhance outcome prediction performance of head computed tomography scores in traumatic brain injury (TBI).Objective: To investigate whether admission levels of eight different protein biomarkers can improve the outcome prediction performance of the Helsinki computed tomography score (HCTS) without clinical covariates in TBI.Materials and methods: Eighty-two patients with computed tomography positive TBIs were included in this study. Plasma levels of β-amyloid isoforms 1–40 (Aβ40) and 1–42 (Aβ42), glial fibrillary acidic protein, heart fatty acid-binding protein, interleukin 10 (IL-10), neurofilament light, S100 calcium-binding protein B, and total tau were measured within 24 h from admission. The patients were divided into favorable (Glasgow Outcome Scale—Extended 5–8, n = 49) and unfavorable (Glasgow Outcome Scale—Extended 1–4, n = 33) groups. The outcome was assessed 6–12 months after injury. An optimal predictive panel was investigated with the sensitivity set at 90–100%.Results: The HCTS alone yielded a sensitivity of 97.0% (95% CI: 90.9–100) and specificity of 22.4% (95% CI: 10.2–32.7) and partial area under the curve of the receiver operating characteristic of 2.5% (95% CI: 1.1–4.7), in discriminating patients with favorable and unfavorable outcomes. The threshold to detect a patient with unfavorable outcome was an HCTS > 1. The three best individually performing biomarkers in outcome prediction were Aβ40, Aβ42, and neurofilament light. The optimal panel included IL-10, Aβ40, and the HCTS reaching a partial area under the curve of the receiver operating characteristic of 3.4% (95% CI: 1.7–6.2) with a sensitivity of 90.9% (95% CI: 81.8–100) and specificity of 59.2% (95% CI: 40.8–69.4).Conclusion: Admission plasma levels of IL-10 and Aβ40 significantly improve the prognostication ability of the HCTS after TBI.

Introduction Chronic subdural haematomas (CSDHs) are one of the most common neurosurgical conditions. The goal of surgery is to alleviate symptoms and minimise the risk of symptomatic recurrences. In the past, reoperation rates as high as 20%–30% were described for CSDH recurrences. However, following the introduction of subdural drainage, reoperation rates dropped to approximately 10%. The standard surgical technique includes burr-hole craniostomy, followed by intraoperative irrigation and placement of subdural drainage. Yet, the role of intraoperative irrigation has not been established. If there is no difference in recurrence rates between intraoperative irrigation and no irrigation, CSDH surgery could be carried out faster and more safely by omitting the step of irrigation. The aim of this multicentre randomised controlled trial is to study whether no intraoperative irrigation and subdural drainage results in non-inferior outcome compared with intraoperative irrigation and subdural drainage following burr-hole craniostomy of CSDH.Methods and analysis This is a prospective, randomised, controlled, parallel group, non-inferiority multicentre trial comparing single burr-hole evacuation of CSDH with intraoperative irrigation and evacuation of CSDH without irrigation. In both groups, a passive subdural drain is used for 48 hours as a standard of treatment. The primary outcome is symptomatic CSDH recurrence requiring reoperation within 6 months. The predefined non-inferiority margin for the primary outcome is 7.5%. To achieve a 2.5% level of significance and 80% power, we will randomise 270 patients per group. Secondary outcomes include modified Rankin Scale, rate of mortality, duration of operation, length of hospital stay, adverse events and change in volume of CSDH.Ethics and dissemination The study was approved by the institutional review board of the Helsinki and Uusimaa Hospital District (HUS/3035/2019 §238) and duly registered at ClinicalTrials.gov. We will disseminate the findings of this study through peer-reviewed publications and conference presentations.Trial registration number NCT04203550

Abstract Background Organ dysfunction is common after cardiac arrest and associated with worse short-term outcome, but its impact on long-term outcome and treatment costs is unknown. Methods We used nationwide registry data from the intensive care units (ICU) of the five Finnish university hospitals to evaluate the association of 24-h extracerebral Sequential Organ Failure Assessment (24h-EC-SOFA) score with 1-year survival and healthcare-associated costs after cardiac arrest. We included adult cardiac arrest patients treated in the participating ICUs between January 1, 2003, and December 31, 2013. We acquired the confirmed date of death from the Finnish Population Register Centre database and gross 1-year healthcare-associated costs from the hospital billing records and the database of the Finnish Social Insurance Institution. Results A total of 5814 patients were included in the study, and 2401 were alive 1 year after cardiac arrest. Median (interquartile range (IQR)) 24h-EC-SOFA score was 6 (5–8) in 1-year survivors and 7 (5–10) in non-survivors. In multivariate regression analysis, adjusting for age and prior independency in self-care, the 24h-EC-SOFA score had an odds ratio (OR) of 1.16 (95% confidence interval (CI) 1.14–1.18) per point for 1-year mortality. Median (IQR) healthcare-associated costs in the year after cardiac arrest were €47,000 (€28,000–75,000) in 1-year survivors and €12,000 (€6600–25,000) in non-survivors. In a multivariate linear regression model adjusting for age and prior independency in self-care, an increase of one point in the 24h-EC-SOFA score was associated with an increase of €170 (95% CI €150–190) in the cost per day alive in the year after cardiac arrest. In the same model, an increase of one point in the 24h-EC-SOFA score was associated with an increase of €4400 (95% CI €3300–5500) in the total healthcare-associated costs in 1-year survivors. Conclusions Extracerebral organ dysfunction is associated with long-term outcome and gross healthcare-associated costs of ICU-treated cardiac arrest patients. It should be considered when assessing interventions to improve outcomes and optimize the use of resources in these patients.

India is a country of 1.32 billion population with 221 million registered vehicles on the road. Of these, 933950 three wheelers entered service in the year 2015-16 (114% increase from 2005-06). With atmospheric pollution and emission norms in mind, it is essential that we bring down the emission of vehicles. Tail pipe emission reduction is one of the ways to achieve that but, it is a difficult process. This paper explores the alternative. This work describes a methodology for retrofitting the conventional drivetrain of a vehicle with an electric power unit. This work describes the development of a real world drive cycle for three wheeler Autorickshaws in Bengaluru city. For this, the micro trip generation method is employed, which captures the driving conditions encountered by the vehicle on a regular working day. A Bajaj RE 4 stroke CNG vehicle is used as the test vehicle throughout the process. A GPS based data logging system VBOX 3i is used for data acquisition. The vehicle dynamics are simulated to determine the power rating required for the electric motor to retrofit the IC engine using one dimensional longitudinal acceleration analysis. Coast down test results determine aerodynamic drag and rolling resistance coefficients. Dyno test helps us to understand the torque requirements for the electric motor to be retrofitted. The results of the mathematical model and the dyno test are then used to find a suitable electric motor. The adopted methodology in this work can be used to find the suitable power train replacement for any vehicle.

We propose data-driven approaches to water content estimation and ripeness classification of the strawberry fruit. A narrowband hyperspectral spectroradiometer was used to collect reflectance signatures from 43 strawberry fruits at different ripeness levels. Then, the ground truth water content was obtained using the oven-dry method. To estimate the water content, 674 and 698 nm bands were selected to create a normalized difference strawberry water content index. The index was used as an input to a logarithmic model for estimating fruit water content. The model for water content estimation gave a correlation coefficient of 0.82 and Root Mean Squared Error (RMSE) of 0.0092 g/g. For ripeness classification, a Support Vector Machine (SVM) model using the full spectrum as input achieved over 98% accuracy. Our analysis further show that, in the absence of the full spectrum data, using our proposed water content index as input, which uses reflectance values from only two frequency bands, achieved 71% ripeness classification accuracy, which might be adequate for certain applications with limited sensing resources.

Abstract Background Intensive care scoring systems are widely used in intensive care units (ICU) around the world for case-mix adjustment in research and benchmarking. The aim of our study was to investigate the usefulness of common intensive care scoring systems in predicting mid-term mortality in patients with spontaneous intracerebral hemorrhage (ICH) treated in intensive care units (ICU). Methods We performed a retrospective observational study including adult patients with spontaneous ICH treated in Finnish ICUs during 2003–2012. We used six-month mortality as the primary outcome of interest. We used logistic regression to customize Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) II and Sequential Organ Failure Assessment (SOFA) for six-month mortality prediction. To assess the usefulness of the scoring systems, we compared their discrimination and calibration with two simpler models consisting of age, Glasgow Coma Scale (GCS) score, and premorbid functional status. Results Totally 3218 patients were included. Overall six-month mortality was 48%. APACHE II and SAPS II outperformed SOFA (area under the receiver operator curve [AUC] 0.83 and 0.84, respectively, vs. 0.73) but did not show any benefit over the simpler models in terms of discrimination (AUC 0.84, p > 0.05 for all models). SAPS II showed satisfactory calibration (p = 0.058 in the Hosmer-Lemeshow test), whereas all other models showed poor calibration (p

In humans, the glutathione S-transferases (GST) protein family is composed of seven members that present remarkable structural similarity and some degree of overlapping functionalities. GST proteins are crucial antioxidant enzymes that regulate stress-induced signaling pathways. Interestingly, overactive GST proteins are a frequent feature of many human cancers. Recent evidence has revealed that the biology of most GST proteins is complex and multifaceted and that these proteins actively participate in tumorigenic processes such as cell survival, cell proliferation, and drug resistance. Structural and pharmacological studies have identified various GST inhibitors, and these molecules have progressed to clinical trials for the treatment of cancer and other diseases. In this review, we discuss recent findings in GST protein biology and their roles in cancer development, their contribution in chemoresistance, and the development of GST inhibitors for cancer treatment.