The key drivers of born-sustainable businesses: Evidence from the italian fashion industry

Environmental pollution has become one of the most pressing preoccupations for governments, policymakers, and consumers. For this reason, many companies make constant efforts to comply with international laws and standards on ethics, social responsibility, and environmental protection. Fashion companies are among the main producers of pollution because their manufacturing processes result in highly negative outcomes for the environment. In recent years, numerous fashion industries have been transforming their production policies to be sustainable, while others are already born as sustainable businesses. Based on Resource-Based View (RBV) theory and Natural Resource-Based View theory (NRBV), this paper aims at understanding how internal and external factors stimulate born-sustainable businesses operating in the fashion sector, adopting a multiple case study methodology. Our analysis shows that culture, entrepreneurial orientation of the founders, and the proximity of the suppliers among the internal factors, combined with the increase of green consumers as an external factor, foster the creation of green businesses. At the same time, neither current legislation nor the dynamism and competitiveness of markets have influenced the choice of the companies’ founders to start a business based on green production logic. These results reveal the centrality of the founders’ sensitivity toward green strategies to create a sustainable business. The findings have practical implications because they could support regulatory institutions to introduce some incentives that more clearly encourages companies that choose to adopt sustainable business models from the founding, by acting to the internal and external key factors that drive born-sustainable businesses. This study also provides an extension of the existing literature on sustainable born companies, offering researchers useful information on internal and internal factors that promote the adoption of green policies in the fashion industry

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

Objectives: To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes. Methods: After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC). Results: Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value &lt; 0.05) using the Gradient Boosting classifier. Conclusions: Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.</p

.Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound

a b s t r a c t Background and Objective : Left ventricle (LV) dysfunction always occurs at early heart-failure stages, pro- ducing variations in the LV flow patterns. Cardiac diagnostics may therefore benefit from flow-pattern analysis. Several visualization tools have been proposed that require ultrafast ultrasound acquisitions. However, ultrafast ultrasound is not standard in clinical scanners. Meanwhile techniques that can handle low frame rates are still lacking. As a result, the clinical translation of these techniques remains limited, especially for 3D acquisitions where the volume rates are intrinsically low. Methods : To overcome these limitations, we propose a novel technique for the estimation of LV blood velocity and relative-pressure fields from dynamic contrast-enhanced ultrasound (DCE-US) at low frame rates. Different from other methods, our method is based on the time-delays between time-intensity curves measured at neighbor pixels in the DCE-US loops. Using Navier-Stokes equation, we regularize the obtained velocity fields and derive relative-pressure estimates. Blood flow patterns were characterized with regard to their vorticity, relative-pressure changes (dp/dt) in the LV outflow tract, and viscous energy loss, as these reflect the ejection efficiency. Results : We evaluated the proposed method on 18 patients (9 responders and 9 non-responders) who un- derwent cardiac resynchronization therapy (CRT). After CRT, the responder group evidenced a significant (p &lt; 0.05) increase in vorticity and peak dp/dt, and a non-significant decrease in viscous energy loss. No significant difference was found in the non-responder group. Relative feature variation before and after CRT evidenced a significant difference (p &lt; 0.05) between responders and non-responders for vorticity and peak dp/dt. Finally, the method feasibility is also shown with 3D DCE-US. Conclusions : Using the proposed method, adequate visualization and quantification of blood flow patterns are successfully enabled based on low-rate DCE-US of the LV, facilitating the clinical adoption of the method using standard ultrasound scanners. The clinical value of the method in the context of CRT is also shown

Childhood and adulthood severe stressful experiences and biomarkers related to glucose metabolism: a possible association?

Background: No study investigated the association between stress exposure in different stages of life and metabolic dysfunction.Aim: We explore the association between stress exposure and several biomarkers related to glucose metabolism (insulin, c-peptide, GIP, GLP-1, glucagon) in a group of 72 healthy individuals.Method: We used the Childhood Experience of Care and Abuse-Questionnaire (CECA-Q) and a modified version of the Life Events Scale to define exposure to stress, according to four categories: no exposure to childhood trauma (CT) nor to stressful life events (SLEs) (46%), only to CT (25%), only to SLEs (21%), to both (8%).Results: We found that c-peptide (p = 0.006) and insulin (p = 0.002) levels differed among the four categories: 0.77 ng/ml (SD 0.27) and 0.21 ng/ml (SD 0.06) for none, 0.77 (SD 0.37) and 0.20 (SD 0.08) for only SLEs, 0.88 (SD 0.39) and 0.27 (SD 0.12) for only CT, 1.33 (SD 0.57) and 0.40 (SD 0.28) for both, respectively. The highest levels of biomarkers were found in subjects exposed to both CT and SLEs.Conclusion: Our preliminary results seem to suggest that CT might be specifically associated with a dysfunction of glucose metabolism, which might increase the risk of poorer health outcomes in adulthood. This association seems to be even stronger in individuals additionally exposed to SLEs in adulthood. In conclusion, if confirmed in other studies, subjects exposed to both CT and SLEs appear the most vulnerable individuals, for whom preventative interventions, such as healthy lifestyle education programs, might ameliorate the risk of developing metabolic abnormalities

Prediction of postoperative patient deterioration and unanticipated intensive care unit admission using perioperative factors

BACKGROUND AND OBJECTIVES: Currently, no evidence-based criteria exist for decision making in the post anesthesia care unit (PACU). This could be valuable for the allocation of postoperative patients to the appropriate level of care and beneficial for patient outcomes such as unanticipated intensive care unit (ICU) admissions. The aim is to assess whether the inclusion of intra- and postoperative factors improves the prediction of postoperative patient deterioration and unanticipated ICU admissions. METHODS: A retrospective observational cohort study was performed between January 2013 and December 2017 in a tertiary Dutch hospital. All patients undergoing surgery in the study period were selected. Cardiothoracic surgeries, obstetric surgeries, catheterization lab procedures, electroconvulsive therapy, day care procedures, intravenous line interventions and patients under the age of 18 years were excluded. The primary outcome was unanticipated ICU admission. RESULTS: An unanticipated ICU admission complicated the recovery of 223 (0.9%) patients. These patients had higher hospital mortality rates (13.9% versus 0.2%, p&lt;0.001). Multivariable analysis resulted in predictors of unanticipated ICU admissions consisting of age, body mass index, general anesthesia in combination with epidural anesthesia, preoperative score, diabetes, administration of vasopressors, erythrocytes, duration of surgery and post anesthesia care unit stay, and vital parameters such as heart rate and oxygen saturation. The receiver operating characteristic curve of this model resulted in an area under the curve of 0.86 (95% CI 0.83-0.88). CONCLUSIONS: The prediction of unanticipated ICU admissions from electronic medical record data improved when the intra- and early postoperative factors were combined with preoperative patient factors. This emphasizes the need for clinical decision support tools in post anesthesia care units with regard to postoperative patient allocation.</p

Identification of two regions in the p140Cap adaptor protein that retain the ability to suppress tumor cell properties

p140Cap is an adaptor protein that negatively controls tumor cell properties, by inhibiting in vivo tumor growth and metastasis formation. Our previous data demonstrated that p140Cap interferes with tumor growth and impairs invasive properties of cancer cells inactivating signaling pathways, such as the tyrosine kinase Src or E-cadherin/EGFR cross-talk. In breast cancer p140Cap expression inversely correlates with tumor malignancy. p140Cap is composed of several conserved domains that mediate association with specific partners. Here we focus our attention on two domains of p140Cap, the TER (Tyrosine Enriched Region) which includes several tyrosine residues, and the CT (Carboxy Terminal) which contains a proline rich sequence, involved in binding to SH2 and SH3 domains, respectively. By generating stable cell lines expressing these two proteins, we demonstrate that both TER and CT domains maintain the ability to associate the C-terminal Src kinase (Csk) and Src, to inhibit Src activation and Focal adhesion kinase (Fak) phosphorylation, and to impair in vitro and in vivo tumor cell features. In particular expression of TER and CT proteins in cancer cells inhibits in vitro and in vivo growth and directional migration at a similar extent of the full length p140Cap protein. Moreover, by selective point mutations and deletion we show that the ability of the modules to act as negative regulators of cell migration and proliferation mainly resides on the two tyrosines (Y) inserted in the EPLYA and EGLYA sequences in the TER module and in the second proline-rich stretch contained in the CT protein. Gene signature of cells expressing p140Cap, TER or CT lead to the identification of a common pattern of 105 down-regulated and 128 up-regulated genes, suggesting that the three proteins can act through shared pathways. Overall, this work highlights that the TER and CT regions of p140Cap can efficiently suppress tumor cell properties, opening the perspective that short, defined p140Cap regions can have therapeutic effects

Posturographic Analysis in Patients Affected by Central and Peripheral Visual Impairment

Abstract: Although vision loss is known to affect equilibrium maintenance, postural control in patients affected by low vision has been poorly investigated. We evaluated postural stability and the ability to use visual, proprioceptive and vestibular information in different low vision patterns. Ten adults with normal vision (NC), fourteen adults affected by central visual impairment (CLV) and eight adults affected by peripheral visual impairment (PLV) were enrolled in our study. Patients underwent visual, vestibular and postural evaluation (bedside examination, Computed Dynamic Posturograophy). Motor Control Tests were performed to analyze automatic postural adaptive re- sponses elicited by unexpected postural disturbances. Clinical evaluations did not show abnormality in all patients. In the Sensory Organization Test, CLV and PLV patients performed more poorly in conditions 3–6 and 3–4, as compared to NC subjects. The condition 5 score was significantly lower in the CLV group with respect to the PLV patients. Composite equilibrium scores demonstrated significant differences between low-vision subjects vs. NC subjects. No differences were found for somatosensorial contribution. Visual afferences showed lower values in all visually impaired subjects, while vestibular contribution was lower in the CLV patients as compared to the NC and PLV patients. MCT latencies were significantly worse in the CLV subjects. In the low-vision patients, postural control was modified with a specific pattern of strategy adaptation. Different modulations of postural control and different adaptive responses seemed to characterize CLV patients as compared to PLV subjects