Higher Probability Of Survival Research Articles

Machine learning approaches to predict the 1-year-after-initial-AMI survival of elderly patients

BackgroundWhile multiple randomized controlled trials (RCTs) are available, their results may not be generalizable to older, unhealthier or less-adherent patients. Observational data can be used to predict outcomes and evaluate treatments; however, exactly which strategy should be used to analyze the outcomes of treatment using observational data is currently unclear. This study aimed to determine the most accurate machine learning technique to predict 1-year-after-initial-acute-myocardial-infarction (AMI) survival of elderly patients and to identify the association of angiotensin-converting- enzyme inhibitors and angiotensin-receptor blockers (ACEi/ARBs) with survival.MethodsWe built a cohort of 124,031 Medicare beneficiaries who experienced an AMI in 2007 or 2008. For analytical purposes, all variables were categorized into nine different groups: ACEi/ARB use, demographics, cardiac events, comorbidities, complications, procedures, medications, insurance, and healthcare utilization. Our outcome of interest was 1-year-post-AMI survival. To solve this classification task, we used lasso logistic regression (LLR) and random forest (RF), and compared their performance depending on category selection, sampling methods, and hyper-parameter selection. Nested 10-fold cross-validation was implemented to obtain an unbiased estimate of performance evaluation. We used the area under the receiver operating curve (AUC) as our primary measure for evaluating the performance of predictive algorithms.ResultsLLR consistently showed best AUC results throughout the experiments, closely followed by RF. The best prediction was yielded with LLR based on the combination of demographics, comorbidities, procedures, and utilization. The coefficients from the final LLR model showed that AMI patients with many comorbidities, older ages, or living in a low-income area have a higher risk of mortality 1-year after an AMI. In addition, treating the AMI patients with ACEi/ARBs increases the 1-year-after-initial-AMI survival rate of the patients.ConclusionsGiven the many features we examined, ACEi/ARBs were associated with increased 1-year survival among elderly patients after an AMI. We found LLR to be the best-performing model over RF to predict 1-year survival after an AMI. LLR greatly improved the generalization of the model by feature selection, which implicitly indicates the association between AMI-related variables and survival can be defined by a relatively simple model with a small number of features. Some comorbidities were associated with a greater risk of mortality, such as heart failure and chronic kidney disease, but others were associated with survival such as hypertension, hyperlipidemia, and diabetes. In addition, patients who live in urban areas and areas with large numbers of immigrants have a higher probability of survival. Machine learning methods are helpful to determine outcomes when RCT results are not available.

Explainable AI in Diagnosing and Anticipating Leukemia Using Transfer Learning Method

White blood cells (WBCs) are blood cells that fight infections and diseases as a part of the immune system. They are also known as “defender cells.” But the imbalance in the number of WBCs in the blood can be hazardous. Leukemia is the most common blood cancer caused by an overabundance of WBCs in the immune system. Acute lymphocytic leukemia (ALL) usually occurs when the bone marrow creates many immature WBCs that destroy healthy cells. People of all ages, including children and adolescents, can be affected by ALL. The rapid proliferation of atypical lymphocyte cells can cause a reduction in new blood cells and increase the chances of death in patients. Therefore, early and precise cancer detection can help with better therapy and a higher survival probability in the case of leukemia. However, diagnosing ALL is time-consuming and complicated, and manual analysis is expensive, with subjective and error-prone outcomes. Thus, detecting normal and malignant cells reliably and accurately is crucial. For this reason, automatic detection using computer-aided diagnostic models can help doctors effectively detect early leukemia. The entire approach may be automated using image processing techniques, reducing physicians' workload and increasing diagnosis accuracy. The impact of deep learning (DL) on medical research has recently proven quite beneficial, offering new avenues and possibilities in the healthcare domain for diagnostic techniques. However, to make that happen soon in DL, the entire community must overcome the explainability limit. Because of the black box operation's shortcomings in artificial intelligence (AI) models' decisions, there is a lack of liability and trust in the outcomes. But explainable artificial intelligence (XAI) can solve this problem by interpreting the predictions of AI systems. This study emphasizes leukemia, specifically ALL. The proposed strategy recognizes acute lymphoblastic leukemia as an automated procedure that applies different transfer learning models to classify ALL. Hence, using local interpretable model-agnostic explanations (LIME) to assure validity and reliability, this method also explains the cause of a specific classification. The proposed method achieved 98.38% accuracy with the InceptionV3 model. Experimental results were found between different transfer learning methods, including ResNet101V2, VGG19, and InceptionResNetV2, later verified with the LIME algorithm for XAI, where the proposed method performed the best. The obtained results and their reliability demonstrate that it can be preferred in identifying ALL, which will assist medical examiners.

Prediction and Identification of GPCRs Targeting for Drug Repurposing in Osteosarcoma.

BackgroundOsteosarcoma (OS) is a malignant bone tumor common in children and adolescents. The 5-year survival rate is only 67-69% and there is an urgent need to explore novel drugs effective for the OS. G protein-coupled receptors (GPCRs) are the common drug targets and have been found to be associated with the OS, but have been seldom used in OS.MethodsThe GPCRs were obtained from GPCRdb, and the GPCRs expression profile of the OS was downloaded from the UCSC Xena platform including clinical data. 10-GPCRs model signatures related to OS risk were identified by risk model analysis with R software. The predictive ability and pathological association of the signatures in OS were explored by bio-informatics analysis. The therapeutic effect of the target was investigated, followed by the investigation of the targeting drug by the colony formation experiment were.ResultsWe screened out 10 representative GPCRs from 50 GPCRs related to OS risk and established a 10-GPCRs prognostic model (with CCR4, HCRTR2, DRD2, HTR1A, GPR158, and GPR3 as protective factors, and HTR1E, OPN3, GRM4, and GPR144 as risk factors). We found that the low-risk group of the model was significantly associated with the higher survival probability, with the area under the curve (AUC) of the ROC greater than 0.9, conforming with the model. Moreover, both risk-score and metastasis were the independent risk factor of the OS, and the risk score was positively associated with the metastatic. Importantly, the CD8 T-cells were more aggregated in the low-risk group, in line with the predict survival rate of the model. Finally, we found that DRD2 was a novel target with approved drugs (cabergoline and bromocriptine), and preliminarily proved the therapeutic effects of the drugs on OS. These novel findings might facilitate the development of OS drugs.ConclusionThis study offers a satisfactory 10-GPCRs model signature to predict the OS prognostic, and based on the model signature, candidate targets with approved drugs were provided.

Survival Outcomes of Lung Recipients with Body Mass Index ≥35 kg/m2

<h3>Purpose</h3> Transplant consensus statements identify advanced lung disease patients with a body mass index (BMI) ≥35 kg/m² as unlikely candidates for lung transplantation (LT); however, such patients with an otherwise acceptable risk profile tend to cluster in large volume centers. We reviewed our experience with long-term survival of overweight and obese lung transplant recipients (LTRs). <h3>Methods</h3> Of 1004 LTRs from 2007-2021, patients were excluded for BMI <25 kg/m² (n=410), retransplant (n=30), or missing data (n=3); remaining LTRs were divided by listing BMI: overweight (OWT, 25-29.9 kg/m²), obesity class-I (C-I, 30-34.9 kg/m²), and obesity class-II (C-II, ≥35 kg/m²). The primary endpoint was survival. <h3>Results</h3> In total, 561 LTRs were divided into OWT (n=337), C-I (n=208), and C-II (n=16). Subjects in C-II were younger than those in OWT (p<0.01), tended to be the sickest (median LAS 40 [37, 56]), and underwent LT fairly quickly after listing (median 19 [9, 45] days). Of note, the median BMI of C-II patients (35.9 [35, 36] kg/m²) was at the low end of the class. Length of stay for LT was comparable between groups. Compared to OWT, the risk of death at 60 days after LT was 6-fold higher in C-I and 11-fold higher in C-II [p=0.03 and p<0.01, respectively]. At 60 days after LT, OWT had the highest probability of survival followed by C-I and C-II [99.4% vs 96.6% vs 93.8%, respectively, p=0.02]; however, there was no difference between C-I and C-II. Furthermore, survival at 1 and 5 years was similar between groups (Figure 1). <h3>Conclusion</h3> Carefully selected candidates with a BMI slightly higher than 35 kg/m², no other contraindications to LT, and rapidly decompensating lung disease were a small proportion of the overall cohort with an unrealistic amount of time to lose weight. Despite higher perioperative mortality, LTRs with a listing BMI ≥35 kg/m² may have near-equivalent long-term survival as those with BMI 30-34.9 kg/m². In otherwise strong candidates, severe obesity should not be a significant predictor of long-term mortality. Further validation is needed.

Comparing Survival and Cause-Specific Mortality of Different Translocation Release Methods for Desert Bighorn Sheep

Historically, desert bighorn sheep (Ovis canadensis mexicana) were prevalent throughout the Trans-Pecos region of Texas. However, they were extirpated by the 1960s due to unregulated hunting, habitat loss, predation, and disease transmission from livestock. Restoration efforts have been successfully conducted by the Texas Parks and Wildlife Department to increase population numbers of resident (i.e., animals that currently populate a region of interest) desert bighorn sheep at Black Gap Wildlife Management Area (BGWMA) through the use of translocations. Because there is a lack of knowledge on alternative release methods for large mammal translocations, our goals were to monitor cause-specific mortality and postrelease survival of desert bighorn sheep translocated during 2017. Survival estimates of desert bighorn sheep were compared amongst resident, hard-released, and soft-released individuals throughout the study. In winter 2017–2018, we radio-collared and released 30 resident (8 M, 22 F) and 70 within-state-translocated (36 M, 34 F) desert bighorn to BGWMA. Of the 70 translocated individuals, 28 (12 M, 16 F) were hard released (i.e., released immediately onto the landscape) and 42 (24 M, 18 F) were soft released (i.e., released into an enclosure before onto the landscape). Resident desert bighorn had the highest probability of survival over time (Ŝ = 0.83), followed by hard-released (Ŝ = 0.67) and then soft-released (Ŝ = 0.54) individuals. To date, 26 mortalities (13 M, 13 F) were recorded. Of those mortalities, 4 were residents (15%), 6 were hard released (23%), and 16 were soft released (62%). The soft release is thought to be a better strategy for translocating large mammals; however, in this study, it did not improve survival. Survival is potentially influenced by acclimation time and individual exit strategy from the soft-release pen, which should be managed for future restoration efforts. Incorporating a flushing-method exit strategy would aid in removing soft-released individuals from the high-fenced pen simultaneously and may increase survival estimates. This could potentially allow individuals to form larger groups when exiting the enclosure and entering the new habitat. The soft-release method is also more costly to implement, which could be challenging for wildlife managers.

Different effects of warming treatments in forests versus hedgerows on the understorey plant Geum urbanum

ABSTRACT The effectiveness of hedgerows as functional corridors in the face of climate warming has been little researched. Here we investigated the effects of warming temperatures on plant performance and population growth of Geum urbanum in forests versus hedgerows in two European temperate regions. Adult individuals were transplanted in three forest–hedgerow pairs in each of two different latitudes, and an experimental warming treatment using open‐top chambers was used in a full factorial design. Plant performance was analysed using mixed models and population performance was analysed using Integral Projection Models and elasticity analyses. Temperature increases due to open‐top chamber installation were higher in forests than in hedgerows. In forests, the warming treatment had a significant negative effect on the population growth rate of G. urbanum. In contrast, no significant effect of the warming treatment on population dynamics was detected in hedgerows. Overall, the highest population growth rates were found in the forest control sites, which was driven by a higher fecundity rather than a higher survival probability. Effects of warming treatments on G. urbanum population growth rates differed between forests and hedgerows. In forests, warming treatments negatively affected population growth, but not in hedgerows. This could be a consequence of the overall lower warming achieved in hedgerows. We conclude that maintenance of cooler forest microclimates coul, at least temporarily, moderate the species response to climate warming.

Intensity of perioperative analgesia but not pre-treatment pain is predictive of survival in dogs undergoing amputation plus chemotherapy for extremity osteosarcoma.

The purpose of this bi‐institutional retrospective study was to determine whether, in dogs treated with limb amputation and adjunctive chemotherapy for osteosarcoma, oncologic outcomes are impacted by either: (1) baseline cancer pain severity, or (2) the approaches used for perioperative pain management. Data were extracted from the medical records of 284 dogs that underwent both limb amputation and chemotherapy (carboplatin and/or doxorubicin) between 1997 and 2017 for localized (non‐metastatic) osteosarcoma of the appendicular skeleton. Kaplan–Meier survival curves and Cox proportional hazard (PH) models were used to determine the impact that retrospectively scored baseline pain levels (high vs. low) and various analgesic and local anaesthetic treatments had on both metastasis‐free survival and all‐cause mortality. For the entire population, the median disease free interval and median overall survival times were 253 and 284 days, respectively. Baseline pain was rated as “low” in 84 dogs, and “high” in 190 dogs; pain severity had no detectable effect on either metastasis‐free survival or all‐cause mortality. When accounting for the potential influences of known prognostic factors, dogs treated with what was characterized as a high‐intensity perioperative analgesic plan (including both a non‐steroidal anti‐inflammatory drug [NSAID] and a bupivacaine‐eluting soaker catheter placed at the amputation site) had a higher probability of survival than dogs treated with a low‐intensity perioperative analgesic plan (neither an NSAID, nor a soaker catheter); the median overall survival times were 252 and 378 days, respectively (hazard ratio: 2.922; p = .020).

Can Lung Ultrasound Be the Ideal Monitoring Tool to Predict the Clinical Outcome of Mechanically Ventilated COVID-19 Patients? An Observational Study.

Background: During the COVID-19 pandemic, lung ultrasound (LUS) has been widely used since it can be performed at the patient’s bedside, does not produce ionizing radiation, and is sufficiently accurate. The LUS score allows for quantifying lung involvement; however, its clinical prognostic role is still controversial. Methods: A retrospective observational study on 103 COVID-19 patients with respiratory failure that were assessed with an LUS score at intensive care unit (ICU) admission and discharge in a tertiary university COVID-19 referral center. Results: The deceased patients had a higher LUS score at admission than the survivors (25.7 vs. 23.5; p-value = 0.02; cut-off value of 25; Odds Ratio (OR) 1.1; Interquartile Range (IQR) 1.0−1.2). The predictive regression model shows that the value of LUSt0 (OR 1.1; IQR 1.0–1.3), age (OR 1.1; IQR 1.0−1.2), sex (OR 0.7; IQR 0.2−3.6), and days in spontaneous breathing (OR 0.2; IQR 0.1–0.5) predict the risk of death for COVID-19 patients (Area under the Curve (AUC) 0.92). Furthermore, the surviving patients showed a significantly lower difference between LUS scores at admission and discharge (mean difference of 1.75, p-value = 0.03). Conclusion: Upon entry into the ICU, the LUS score may play a prognostic role in COVID-19 patients with ARDS. Furthermore, employing the LUS score as a monitoring tool allows for evaluating the patients with a higher probability of survival.

Survival analysis of African catfish and Nile tilapia briefly exposed to complex pesticide mixtures

BackgroundPulse exposures are the consequences of the intermittent release of pollutants in the environment. Brief exposure of aquatic organisms to high concentrations of pesticides simultaneously occurs, particularly in small watercourses during high flows. The effects of pulse exposure often include effects occurring during and after the exposure. Despite this, routine toxicity tests procedures often ignore brief exposure scenarios and the role of time in toxicity. We conducted a pulse toxicity test by briefly exposing African catfish and Nile tilapia fingerlings to pesticide mixtures of atrazine, mancozeb, chlorpyrifos, and lambda-cyhalothrin. The study aimed to estimate pesticide mixture interaction in pulse-exposed fish and elucidate the influence of species differences on the response of fish to the pesticide mixture.ResultsDespite the similarity in fingerlings weight, African catfish had a significantly higher survival probability than Nile tilapia after exposure to atrazine-mancozeb mixture. However, the survival probability of African catfish and Nile tilapia fingerlings were similar after exposure to atrazine-chlorpyrifos, atrazine-lambda cyhalothrin, mancozeb-chlorpyrifos, mancozeb-lambda cyhalothrin, chlorpyrifos-lambda cyhalothrin, and quaternary mixture (p > 0.05). The survival probability of exposed fingerlings was significantly lower for continuous than pulse exposure to the mixtures (p < 0.01). Nevertheless, the survival probability of 60 min of pulse exposure to 13.49 mg/L mancozeb-lambda cyhalothrin was similar to continuous exposure for 96 h. Atrazine-mancozeb, atrazine-chlorpyrifos, atrazine-lambda cyhalothrin, mancozeb-chlorpyrifos, mancozeb-lambda cyhalothrin, and the quaternary pesticide mixture were antagonists in African catfish but not in Nile tilapia. At the same time, chlorpyrifos-lambda-cyhalothrin was antagonistic in Nile tilapia but not African catfish.ConclusionsPesticide mixture interaction was antagonist but specie-dependent. Innate intrinsic and extrinsic deterministic factors and, to a limited extent, stochastic processes may have influenced the survival probability of African catfish, and Nile tilapia pulsed exposed to complex pesticide mixtures. Pulse toxicity assessment using survival analysis is relevant in ecotoxicology as it enables the study of factors that can influence pulse toxicity.

Leaf functional traits predict shade tolerant tree performance in cloud forest restoration plantings

Abstract Restoration of tropical montane cloud forest (TMCF) landscapes is urgently required. Assisting the regeneration of endangered and shade tolerant tree species is essential for both the recovery of this vulnerable group and of ecological processes. However, there is limited species‐specific information regarding tree performance under different disturbance conditions with which to implement effective interventions. We assessed the performance of shade tolerant tree seedlings in restoration plantings under different disturbance settings and determined whether leaf mass area (LMA) and leaf dry mass content (LDMC)—functional traits typically associated with resource capture or stress tolerance—could serve as predictors of survival and growth among species. Since conservative leaf morphological traits can maximize survival, we expected species with higher LMA and LDMC to present higher survival. For a set of eight native cloud forest species, a total of 2,202 seedlings were planted in four pastures, five secondary forests and three forests subjected to traditional selective logging, in TMCF landscapes in Eastern Mexico. Seedling survival was high after 3 years: 62% in pastures, 80% in secondary forests and 88% in logged forests. Growth rates were lowest in pastures, followed by secondary forests and highest in logged forests. LMA was a strong predictor of seedling survival in all of the environments; tree species with higher LMA presented greater survival. LDMC was related to seedling survival in the three environments, although to a lesser extent than LMA. In the pastures, higher LMA and LDMC were linked to lower growth. Synthesis and applications. This study supports the potential of shade tolerant tree species in restoration efforts to assist the recovery of this important functional group and to accelerate succession across altered environments. Our results support the notion that conservative leaf functional traits are linked to a higher probability of survival, not only in the shaded understorey, but also under high solar radiation in transformed habitats. Leaf mass area (LMA) in particular is a reliable predictor of seedling survival for shade tolerant species. Species selection based on LMA could thus improve restoration initiative outcomes: tree species with high LMA present higher survival probability and can be introduced into pastures, secondary forests and selectively logged forests.

M6A Regulator-Mediated Methylation Modification Patterns and Tumor Microenvironment Cell-Infiltration Characterization in Head and Neck Cancer.

Background: Recently, RNA modifications have emerged as essential epigenetic regulators of gene expression. However, the mechanism of how RNA N 6-methyladenosine (m6A) modification interacts with tumor microenvironment (TME) infiltration remains obscure. Methods: A total of 876 head and neck cancer samples considering 21 m6A regulators were included and analyzed to determine the m6A modification patterns. These modification patterns were then correlated with TME immune cell-infiltrating characteristics. A scoring system, the m6Ascore, was constructed using principal component analysis algorithms to quantify m6A modification of tumors. Results: Three m6A modification patterns were identified, with TME infiltrating characteristics highly consistent with tumors with three distinct immune phenotypes, including immune-inflamed, immune-exclude, and immune-desert phenotypes. It was demonstrated that the identification of the m6A modification patterns via m6Ascore could predict tumor progression, subtypes, TME stromal activity, variation of relevant genes, and patient prognosis. Low m6Ascore, identified to be an inflamed phenotype, is found to be associated with low stroma activity and tumor mutation burden, high survival probability, increased tumor neoantigen burden, and enhanced response to anti-PD-1/L1 immunotherapy. The therapeutic advantages and clinical benefits of patients with low m6Ascore were further verified in two immunotherapy cohorts. Conclusion: This study identified the significant role that the m6A modification played in the formation of TME characteristics. A more comprehensive understanding of the m6A modification patterns and their correlation with TME infiltration will contribute to the discovery of immunotherapy strategies with better efficacy.

The association between epicardial adipose tissue and prognosis in heart failure with preserved and reduced ejection fraction

Abstract Funding Acknowledgements Type of funding sources: None. Background. Recent evidence shows that increased epicardial adipose tissue (EAT) thickness is associated with metabolic syndrome, microvascular dysfunction and enhanced pericardial restraint. Purpose. We measured echocardiography-derived EAT thickness in a population of heart failure (HF) patients with reduced (HFrEF) and preserved (HFpEF) ejection fraction to examine the relationship between EAT and prognosis at clinical follow-up. Methods. We prospectively enrolled 393 consecutive HF outpatients (205 HFrEF, 188 HFpEF) who had been referred to our hospital due to dyspnoea and/or effort intolerance. We performed a resting clinical and biohumoral evaluation, followed by combined cardiopulmonary-echocardiography exercise stress. We considered a composite endpoint of cardiovascular death and HF-related hospitalization during follow-up. Results. Patients with HFpEF displayed greater EAT thickness (median 8 mm, interquartile range [IQR] 4–12 mm) than HFrEF (median 3 mm, IQR 2–6 mm; p &amp;lt; 0.0001). During a median follow-up of 20.9 months (IQR 15-25 months), 34 cardiovascular deaths and 146 HF hospitalizations were reported, with no significant differences between the two HF phenotypes. EAT was shown to predict adverse events independently from body mass index, waist circumference and other well-established prognostic markers in HF (such as NT-proBNP and peak oxygen consumption). The risk of adverse events increased with increasing EAT thickness in HFpEF and with EAT thinning in HFrEF. Kaplan-Meier analyses for the composite endpoint showed that in HFpEF, the survival probability was significantly lower in patients with thicker EAT than those with thinner EAT. In HFrEF, conversely, patients with increased EAT thickness had a higher survival probability than those with reduced EAT thickness (Figure 1). Conclusion. EAT accumulation is increased in HFpEF compared to HFrEF and carries different prognostic meanings in the two subsets. In HFpEF, EAT thickening portends adverse outcomes, which may be due to the secretion of pro-inflammatory and pro-atherogenic adipokines and increased mechanical restraint. In HFrEF, EAT thinning is associated with a worse prognosis, probably reflecting a more advanced catabolic state (e.g., cardiac cachexia). Larger studies are needed to determine whether or not EAT has a causal role in influencing progression and survival in the different HF phenotypes. Abstract Figure 1

Unique population dynamics of Japanese field vole: Winter breeding and summer population decline

AbstractIt is common knowledge that winter temperatures influence the life history of small mammals. Cold temperatures necessitate increased energy requirements for survival, and recent studies indicate that snow cover can have both negative and positive influences. With each new observation, we develop a more comprehensive understanding of the mechanisms that influence small mammal populations. Here we report on our recent study on Japanese field vole Microtus montebelli, which reaches its peak in population during the early spring and its low during the autumn. To understand the population dynamics of these voles, we conducted a capture‐mark‐recapture survey, then estimated the seasonal abundance, recruit, capture probabilities, and survival probabilities using the Bayesian hierarchical model. We also analyzed the impact of mammalian generalist predator visits on the survival probabilities. Our data indicates that the early spring peak in population is due to intensive winter breeding and the highest survival probabilities during the periods of deep snow cover. When snow cover reaches a certain depth, the circumstances can combine to raise survival probability and favor breeding. During the breeding season in May and June, on the contrary, the survival probability reached its lowest, resulting in a decrease in population despite breeding. The low survival probability between spring and autumn could be attributed to the impact of generalist predators, and low vegetation may have amplified the effect. In summary, the deep snow cover and generalist predators were considered to be the key factors shaping this unique population dynamics in this orchard area.

Robust Two-Stage Location Allocation for Emergency Temporary Blood Supply in Postdisaster

Disaster medical rescue in China mainly adopts the “on-site rescue” model. Whether the location of emergency temporary blood supply sites is reasonable or not directly affects the rescue efficiency. The paper studies the robust location-allocation for emergency temporary blood supply after disaster. First, the factors of several candidate sites were quantified by the entropy-based TOPSIS method, and 12 candidate blood supply sites with higher priority were selected according to the evaluation indicators. At the same time, the uncertainty of blood demand at each disaster site increased the difficulty of decision-making, and then, a robust location model (MIRP) was constructed with minimum cost with time window constraints. It is also constrained by the uncertain demand for blood in three scenarios. Second, the survival probability function was introduced, and the time window limit was given at the minimum cost to maximize the survival probability of the suffered people. Finally, the numerical example experiments demonstrate that the increase in demand uncertainty and survival probability cause the MIRP model to generate more costs. Compared with the three MIRP models, the MIRP-ellipsoid set model gained better robustness. Also, given the necessary restrictions on the time window, the cost can be reduced by about 13% with the highest survival probability. Decision-makers can select different combinations of uncertainty levels and demand disturbance ratios and necessary time constraints to obtain the optimal location-allocation solution according to risk preference and actual conditions.

Ethical-professional decision-making in exceptional situations given the Covid-19 pandemic: a scoping review

Objective: to present an overview of the ethical-professional decision-making process in exceptional situations at the beginning of the COVID-19 pandemic. Method: this is a scoping review, including documents published between December&nbsp;2019 and July&nbsp;2020 in the following databases: Google Scholar, PubMed, Scopus, SciELO, CINAHL and BVS. The PRISMA-ScR Checklist was adopted to present the review. Results: a total of 28 documents were selected, organized into five categories: Technical-Medical-Scientific Requirement, Justice and Equality, Health History, Commission for Shared Decision-Making and Severe Respiratory Condition. Conclusion: the criteria indicated to prioritize the care process targeted at critically-ill patients with COVID-19 were as follows: technical-medical-scientific parameters; severity of the clinical condition; older age; being a health professional; presence of incurable underlying diseases; carrying out draws; and patients with a higher survival probability.&nbsp

Ethical-professional decision-making in exceptional situations given the Covid-19 pandemic: a scoping review

Objective: to present an overview of the ethical-professional decision-making process in exceptional situations at the beginning of the COVID-19 pandemic. Method: this is a scoping review, including documents published between December&nbsp;2019 and July&nbsp;2020 in the following databases: Google Scholar, PubMed, Scopus, SciELO, CINAHL and BVS. The PRISMA-ScR Checklist was adopted to present the review. Results: a total of 28 documents were selected, organized into five categories: Technical-Medical-Scientific Requirement, Justice and Equality, Health History, Commission for Shared Decision-Making and Severe Respiratory Condition. Conclusion: the criteria indicated to prioritize the care process targeted at critically-ill patients with COVID-19 were as follows: technical-medical-scientific parameters; severity of the clinical condition; older age; being a health professional; presence of incurable underlying diseases; carrying out draws; and patients with a higher survival probability.&nbsp;

Nutritional symbionts enhance structural defence against predation and fungal infection in a grain pest beetle.

ABSTRACTMany insects benefit from bacterial symbionts that provide essential nutrients and thereby extend the hosts’ adaptive potential and their ability to cope with challenging environments. However, the implications of nutritional symbioses for the hosts’ defence against natural enemies remain largely unstudied. Here, we investigated whether the cuticle-enhancing nutritional symbiosis of the saw-toothed grain beetle Oryzaephilus surinamensis confers protection against predation and fungal infection. We exposed age-defined symbiotic and symbiont-depleted (aposymbiotic) beetles to two antagonists that must actively penetrate the cuticle for a successful attack: wolf spiders (Lycosidae) and the fungal entomopathogen Beauveria bassiana. While young beetles suffered from high predation and fungal infection rates regardless of symbiont presence, symbiotic beetles were able to escape this period of vulnerability and reach high survival probabilities significantly faster than aposymbiotic beetles. To understand the mechanistic basis of these differences, we conducted a time-series analysis of cuticle development in symbiotic and aposymbiotic beetles by measuring cuticular melanisation and thickness. The results reveal that the symbionts accelerate their host's cuticle formation and thereby enable it to quickly reach a cuticle quality threshold that confers structural protection against predation and fungal infection. Considering the widespread occurrence of cuticle enhancement via symbiont-mediated tyrosine supplementation in beetles and other insects, our findings demonstrate how nutritional symbioses can have important ecological implications reaching beyond the immediate nutrient-provisioning benefits.

Increase in the Complement Activation Product C4d and the Terminal Complement Complex sC5b-9 Is Associated with Disease Severity and a Fatal Outcome in Necrotizing Soft-Tissue Infection

The hyperinflammatory burden is immense in necrotizing soft-tissue infection (NSTI). The complement system is a key during the innate immune response and may be a promising target to reduce the inflammatory response, potentially improving the clinical outcome. However, complement activation and its association to disease severity and survival remain unknown in NSTI. Therefore, we prospectively enrolled patients with NSTI and sampled blood at admission and once daily for the following 3 days. Plasma C4c, C4d, C3bc, and C3dg and the terminal complement complex (TCC) were evaluated using ELISA techniques. In total, 242 patients were included with a median age of 62 years, with a 60% male predominance. All-cause 30-day mortality was 17% (95% confidence interval [CI] 13–23) with a follow-up of >98%. C4c and C3dg were negatively correlated with Simplified Acute Physiology Score II (Rho −0.22, p < 0.001 and Rho −0.17, p = 0.01). Patients with septic shock (n = 114, 47%) had higher levels of baseline TCC than those in non-shock patients (18 vs. 14, p < 0.001). TCC correlated with the Sequential Organ Failure Assessment (SOFA) score (Rho 0.19, p = 0.004). In multivariate Cox regression analysis (adjusted for age, sex, comorbidity, and SOFA score), high baseline C4d (>20 ng/mL) and the combination of high C4d and TCC (>31 arbitrary units/mL) were associated with increased 30-day mortality (hazard ratio [HR] 3.26, 95% CI 1.56–6.81 and HR 5.12, 95% CI 2.15–12.23, respectively). High levels of both C4d and TCC demonstrated a negative predictive value of 0.87. In conclusion, we found that in patients with NSTI, complement activation correlated with the severity of the disease. High baseline C4d and combination of high C4d and TCC are associated with increased 30-day mortality. Low baseline C4d or TCC indicates a higher probability of survival.

Cardiac Transplantation in Danon Disease

Danon disease (DD) is a rare X-linked dominant cardioskeletal myopathy caused by mutations in the lysosome-associated membrane protein-2 (LAMP-2) gene that is usually lethal without cardiac transplantation. The purpose of this study was to characterize post-transplant outcomes in a large cohort of patients with DD who underwent cardiac transplantation. The clinical phenotype and outcome data of patients with DD who underwent cardiac transplantation (n = 38; 19 males and 19 females) were obtained from 8 centers. Study outcomes included graft survival, defined as death or retransplantation, and episodes of acute cellular and antibody-mediated rejection and cardiac allograft vasculopathy at 1 year. Median follow-up time after transplantation for the entire cohort was 4.4 years (IQR: 1.5-12.8 years). The median age at transplant for the cohort was 20.2 years (15.8-27.9 years), with no difference in age between sexes. Median pretransplant left-ventricular ejection fraction for the entire cohort was 30% (range 11%-84%). Males had higher pretransplant aspartate aminotransferase, alanine aminotransferase and creatine phosphokinase levels than females (P < 0.001). There were 2 deaths in the entire cohort and 2 retransplants. There was no difference in actuarial graft survival between males and females (P = 0.8965); the estimated graft survival was 87.1% (95%CI: 63.6%-95.9%) at 5 years. One episode (2.7%) of antibody-mediated rejection, grade 2, and 7 episodes (19%) of acute cellular rejection, grade 2 or 3, were reported in patients who survived to discharge (6 females and 1 male; P = 0.172). Heart transplantation outcomes are acceptable in DD with high probabilities of 5-year graft survival for males and females suggesting that cardiac transplantation is an effective treatment option for DD patients.

Angiogenic factors could help us to define patients obtaining complete response with undetectable minimal residual disease in untreated CLL patients treated by FCR: results from the CLL2010FMP, a FILO study

Angiogenesis is in a constant balance between pro and anti-angiogenic factors. Neoangiogenesis, implicated in metastatic spreading is characterized in solid cancers, but fairly new in chronic lymphocytic leukemia (CLL). We hypothesize that secretion of angiogenic factors could be correlated to the pathogenesis of CLL, and therefore predict the outcome of patients. We investigated concentrations of 22 cytokines and chemokines in 137 non-del 17p B-CLL patients, treated with a fludarabine-cyclophosphamide-rituximab (FCR)-based regimen. We constructed a biomarker index defining different risk groups based on lymphocyte count, the intensity of CD20 antigen on CD19+ cells, Ang-2, and PDGF-BB plasma concentrations at diagnosis. Four groups were defined, exhibiting specific molecular signatures and correlated with progression-free survival of patients. Our results suggest that we can determine at diagnosis of non-del 17p B-CLL patients, those with a very high probability of progression-free survival, independently of IGVH mutational status and residual disease at the end of treatment.