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Continuously improving cancer-specific survival puts a growing proportion of cancer patients at risk of major adverse cardiovascular events (MACE), but tailored tools for cardiovascular risk prediction remain unavailable.

Covid-19 infection influenced the screening test rate of breast cancer worldwide due to the quarantine measures, routine procedures reduction, and delay of early diagnosis, causing high mortality risk and severity of the disease. X-ray mammography is the gold standard for diagnosing early signs of breast cancer, and Artificial Intelligence enables the detection of suspicious lesions and classifying them in terms of malignancy. This paper aimed to investigate mass detection and classification in a large-scale OPTIMAM dataset with 6000 cases and extracted 3524 images with masses in the mammograms of the Hologic manufacturer. The methodology of the detection step is to train the RetinaNet architecture of ResNet50, ResNet101, and ResNet152 backbones with three types of initializations by ImageNet and COCO weights and from scratch. The dataset was pre-processed to generate two types of input with entire mammograms and patches, which are stated as the first and the second approaches. The results show that in the first approach, RetinaNet of ResNet50 backbone with ImageNet and COCO weights and ResNet152 with the same weights performed 0.91 True Positive Rate at 0.78 False Positive Per Image, respectively. In contrast, in the second approach, ResNet152 with ImageNet weights reached 0.88 TPR at 0.78 FPPI. In the classification step, the Transfer Learning approach was applied with fine-tuning by adding L2-regularization and class weights to balance class distribution in the datasets.

High-efficacy disease-modifying therapies have been proven to slow disability accrual in adults with relapsing–remitting multiple sclerosis. However, their impact on disability worsening in paediatric-onset multiple sclerosis, particularly during the early phases, is not well understood. We evaluated how high-efficacy therapies influence transitions across five disability states, ranging from minimal disability to gait impairment and secondary progressive multiple sclerosis, in people with paediatric-onset multiple sclerosis.

As the world is becoming more VUCA, only a few studies focus on exploring the plant performance and mapping its pattern in real time. The existing study shows that plant performance can be linked to an S-curve theory. This is mainly due to market turbulence, demand for continuous improvement, and the introduction of emerging technologies. Taking this idea, in this article, we focus on testing the S-curve in the lean deployment of an OEM. The study proposes integrating lean tools with decision-making techniques to achieve productivity benefits. The proposed method uses VSM to portray the changeover activities and standardize them using the SMED technique to reduce the changeover time alongside other continuous improvement suggestions. This provides real-time active solutions for decision making, prediction, and planning of resources. It also assists the industrial units in getting better insights into their productivity improvization and market evaluation trends. The study proposed an IoT-driven ecosystem for effective decision making in the OEM environment. Finally, the study is tested in an automotive OEM that has reduced changeover time by > 75% and an overall productivity increase of 27.31%. Such an ecosystem contributes to organizational transformation and promotes innovation in OEM.

In the era of artificial intelligence, and particularly machine learning and deep learning models, the availability of large datasets is crucial to develop innovative and effective services, especially in the healthcare field. In this context, one essential requirement is access to verified information for contextualising/enriching the data. The SAMBIAS project analysed in this study involves the implementation of a software platform for data sharing in clinical scenarios, with the main objective of providing specific medical datasets to improve the competitiveness of the healthcare organisation from a general point of view. The platform, which is accessible via the web, provides on-demand, augmented sets of clinical situations, based on the enormous amounts of data that are collected by the health information systems of healthcare organisations. The case under investigation here is the Casa di Cura Tortorella s.p.a., Salerno, Italy. The implications of this platform are discussed in terms of more efficient performance.

BackgroundSeizures are reported to be more prevalent in individuals with multiple sclerosis (MS) compared with the general population. Existing data predominantly originate from population-based studies, which introduce variability in methodologies and are vulnerable to selection and reporting biases.MethodsThis meta-analysis aims to assess the incidence of seizures in patients participating in randomised clinical trials and to identify potential contributing factors. Data were extracted from 60 articles published from 1993 to 2022. The pooled effect size, representing the incidence rate of seizure events, was estimated using a random-effect model. Metaregression was employed to explore factors influencing the pooled effect size.ResultsThe meta-analysis included data from 53 535 patients and 120 seizure events in a median follow-up of 2 years. The pooled incidence rate of seizures was 68.0 per 100 000 patient-years, significantly higher than the general population rate of 34.6. Generalised tonic-clonic seizures were the most common type reported, although there was a high risk of misclassification for focal seizures with secondary generalisation. Disease progression, longer disease duration, higher disability levels and lower brain volume were associated with a higher incidence of seizures. Particularly, sphingosine-1-phosphate receptor (S1PR) modulators exhibited a 2.45-fold increased risk of seizures compared with placebo or comparators, with a risk difference of 20.5 events per 100 000 patient-years.ConclusionsPatients with MS face a nearly twofold higher seizure risk compared with the general population. This risk appears to be associated not only with disease burden but also with S1PR modulators. Our findings underscore epilepsy as a significant comorbidity in MS and emphasise the necessity for further research into its triggers, preventive measures and treatment strategies.

In this article, we examine the role of organizational learning on Industry 4.0 (I4.0) deployments for enhancing business performance within the manufacturing industries of developing countries. Based on a survey of 246 manufacturers from 12 developing countries recognized by Deloitte's global manufacturing competitiveness index report, a theoretically derived model was verified using structural equation modeling. This study's results show that I4.0 adoption mediates the impact of I4.0 awareness on organizational learning, and I4.0 adoption and organizational learning serially mediate I4.0 awareness and business performance toward creating smart factories. This study contributes: first, by conceptualization and operationalization of the constructs to clarify the role of organizational learning in I4.0 deployments; second, through an organizational learning hierarchy, it presents I4.0 awareness or familiarity as first-order learning, the experience of adopting I4.0 as the second-order learning, and learning that emerges from cycles of awareness and adoption together (cumulatively) as third-order or meta-learning. This study extends the microfoundations of learning capability in the I4.0 context and opens new agendas for future research and practice. It guides managers to consciously develop higher order learning in I4.0 deployments to safeguard I4.0 investments and enable competitive advantage in digitization.

The rapid advancement of energy-efficient parallel ultra-low-power (ULP) $\mu $ controllers units (MCUs) is enabling the development of autonomous nano-sized unmanned aerial vehicles (nano-UAVs). These sub-10cm drones represent the next generation of unobtrusive robotic helpers and ubiquitous smart sensors. However, nano-UAVs face significant power and payload constraints while requiring advanced computing capabilities akin to standard drones, including real-time Machine Learning (ML) performance and the safe co-existence of general-purpose and real-time OSs. Although some advanced parallel ULP MCUs offer the necessary ML computing capabilities within the prescribed power limits, they rely on small main memories (< 1MB) and $\mu $ controller-class CPUs with no virtualization or security features, and hence only support simple bare-metal runtimes. In this work, we present Shaheen, a 9mm $^{\textbf {2}}~200$ mW SoC implemented in 22nm FDX technology. Differently from state-of-the-art MCUs, Shaheen integrates a Linux-capable RV64 core, compliant with the v1.0 ratified Hypervisor extension and equipped with timing channel protection, along with a low-cost and low-power memory controller exposing up to 512MB of off-chip low-cost low-power HyperRAM directly to the CPU. At the same time, it integrates a fully programmable energy- and area-efficient multi-core cluster of RV32 cores optimized for general-purpose DSP as well as reduced- and mixed-precision ML. To the best of the authors’ knowledge, it is the first silicon prototype of a ULP SoC coupling the RV64 and RV32 cores in a heterogeneous host+accelerator architecture fully based on the RISC-V ISA. We demonstrate the capabilities of the proposed SoC on a wide range of benchmarks relevant to nano-UAV applications including general-purpose DSP as well as inference and online learning of quantized DNNs. The cluster can deliver up to 90GOp/s and up to 1.8TOp/s/W on 2-bit integer kernels and up to 7.9GFLOp/s and up to 150GFLOp/s/W on 16-bit FP kernels.

Critical failure factors (CFFs) are the key factors where “things must go wrong” for the I4.0 implementation to be a failure or that do not meet the intended objectives. CFFs provide a set of indicators or identifiable conditions so that problems can be identified and addressed before they fail. This article uses an exploratory sequential mixed method design to investigate the CFFs for I4.0. In the first explorative qualitative phase, 26 senior managers participated through a purposive sampling frame. The article finds ten CFFs: lack of I4.0 strategy, absence of well-designed smart product/service systems, lack of digital supply chains, leadership is not supportive for I4.0, poor human resource management for I4.0, absence of I4.0 technology transformation management plan, I4.0 is not implemented totally, lack of top management support for I4.0, poor change management strategies in place, and inadequate consideration of sustainability in I4.0 initiative. In the second quantitative phase, 156 senior managers participated in an online survey. Exploratory factor analysis (EFA), confirmatory factor analysis, and ranking of CFFs were carried out. The findings on CFFs will help the practitioners and organizations to identify, monitor, and control these factors while implementing I4.0. Moreover, a good understanding of the failure factors will help organizations to avoid the failure in the implementation of I4.0.

BackgroundThe assessment of treatment response is a crucial step for patients with relapsing-remitting multiple sclerosis on disease-modifying therapies (DMTs). We explored whether a scoring system developed within the MAGNIMS (MRI in Multiple Sclerosis) network to evaluate treatment response to injectable drugs can be adopted also to oral DMTs.MethodsA multicentre dataset of 1200 patients who started three oral DMTs (fingolimod, teriflunomide and dimethyl fumarate) was collected within the MAGNIMS network. Disease activity after the first year was classified by the ‘MAGNIMS’ score based on the combination of relapses (0–≥2) and/or new T2 lesions (<3 or ≥3) on brain MRI. We explored the association of this score with the following 3-year outcomes: (1) confirmed disability worsening (CDW); (2) treatment failure (TFL); (3) relapse count between years 1 and 3. The additional value of contrast-enhancing lesions (CELs) and lesion location was explored.ResultsAt 3 years, 160 patients experienced CDW: 12% of them scored ‘0’ (reference), 18% scored ‘1’ (HR=1.82, 95% CI 1.20 to 2.76, p=0.005) and 37% scored ‘2’ (HR=2.74, 95% CI 1.41 to 5.36, p=0.003) at 1 year. The analysis of other outcomes provided similar findings. Considering the location of new T2 lesions (supratentorial vs infratentorial/spinal cord) and the presence of CELs improved the prediction of CDW and TFL, respectively, in patients with minimal MRI activity alone (one or two new T2 lesions).ConclusionsEarly relapses and substantial MRI activity in the first year of treatment are associated with worse short-term outcomes in patients treated with some of the oral DMTs.

Lithium is regarded as the first-line treatment for bipolar disorder (BD), a severe and disabling mental health disorder that affects about 1% of the population worldwide. Nevertheless, lithium is not consistently effective, with only 30% of patients showing a favorable response to treatment. To provide personalized treatment options for bipolar patients, it is essential to identify prediction biomarkers such as polygenic scores. In this study, we developed a polygenic score for lithium treatment response (Li+PGS) in patients with BD. To gain further insights into lithium’s possible molecular mechanism of action, we performed a genome-wide gene-based analysis. Using polygenic score modeling, via methods incorporating Bayesian regression and continuous shrinkage priors, Li+PGSwas developed in the International Consortium of Lithium Genetics cohort (ConLi+Gen: N= 2367) and replicated in the combined PsyCourse (N= 89) and BipoLife (N= 102) studies. The associations of Li+PGSand lithium treatment response —defined in a continuous ALDA scale and a categorical outcome (good response vs. poor response) were tested using regression models, each adjusted for the covariates: age, sex, and the first four genetic principal components. Statistical significance was determined at P< 0.05. Li+PGSwas positively associated with lithium treatment response in the ConLi+Gen cohort, in both the categorical (P= 9.8 × 10−12, R2= 1.9%) and continuous (P= 6.4 × 10−9, R2= 2.6%) outcomes. Compared to bipolar patients in the 1stdecile of the risk distribution, individuals in the 10thdecile had 3.47-fold (95%CI: 2.22–5.47) higher odds of responding favorably to lithium. The results were replicated in the independent cohorts for the categorical treatment outcome (P= 3.9 × 10−4, R2= 0.9%), but not for the continuous outcome (P= 0.13). Gene-based analyses revealed 36 candidate genes that are enriched in biological pathways controlled by glutamate and acetylcholine. Li+PGSmay be useful in the development of pharmacogenomic testing strategies by enabling a classification of bipolar patients according to their response to treatment.

Purpose: The purpose of this study is to identify the critical readiness factors (CRFs) that mainly affect the implementation of Lean Six Sigma (LSS) in an organization and their interactions, and to develop a model that allows the management to assess the Lean Implementation Readiness Level. Design/methodology/approach: The methodology is separated into two main parts: the literature review and the assessment model development. In the literature review, the main CRFs and their interactions for LSS implementation in Scopus Data Base were identified. The second part of the methodology is the model development. It was built on a stepwise framework that considers the relations among the CRFs and their importance. Moreover, it was used fuzzy-based linguistic variables given by the experts working in the company to consider the actual performance rating of each CRF. The model has been validated in the healthcare sector in nine hospitals. Findings: From the model application, it is possible to note that the most frequent level among the nine hospitals interviewed is “Average Ready”. Also, the most extreme level of readiness occurred ones while the most extreme level of not readiness never occurred. Results show that in 78% of the cases, there would have been a high probability of implementation failure. Also, it was possible to identify for each hospital if the CRFs are good, if they are weak and need to change or if another factor needs to be improved before it and what this factor is. Originality/value: This work proposes a new methodology that eliminates the negative aspects and limitations of the total interpretive structural modeling methodology and the fuzzy logic approach currently applied to evaluate the LSS readiness of a company. The present methodology lies in the fact that it provides a solution not only by defining the weak CRFs but also by giving an indication of priority as it identifies the weak antecedent factors that inhibit the preparedness of the depending factors.

The present study aimed to evaluate the effects of GH treatment on the body composition of children born with SGA.

Suicide ranks among the 10 leading causes of death worldwide; in Italy, almost 4000 persons per year die by suicide. Studies focusing on suicidality in Italian inpatient samples before the COVID-19 pandemic are scant. We, thus, aimed to define sociodemographic and clinical variables associated with suicidal ideation (SI) and deliberate self-harm (DSH) in a sample of inpatients admitted to a Psychiatric Inpatient Unit. This retrospective study was conducted in the Psychiatric Inpatient Unit of the Perugia Hospital, from January 2018 to December 2019. Sociodemographic and clinical characteristics, including diagnostic and treatment features, were collected from the medical records of subjects admitted for suicidality-related phenomena, namely DSH and SI. The prevalence of suicidality-related phenomena in the sample (n= 850) was 14.12% (n=120) and was mainly due to DSH (n= 84; 70%). Subjects hospitalized due to these conditions were more frequently females, separated, and displayed a higher prevalence of personality disorders, especially borderline personality disorder. People in the suicidality-related phenomena subgroup were more often committed involuntarily and reported multiple hospitalizations less frequently than other inpatients. They were receiving community treatment in a higher percentage of cases, and lithium was prescribed more frequently than among inpatients who were hospitalized for reasons other than suicidality. Our study provides a further characterization of psychiatric inpatients who experience SI or perform DSH. Targeted treatment strategies should be considered for subjects suffering from personality disorders who experience suicidality-related phenomena.

A new series of analogues or derivatives of the previously reported PPARα/γ dual agonist LT175 allowed the identification of ligand 10, which was able to potently activate both PPARα and -γ subtypes as full and partial agonists, respectively. Docking studies were performed to provide a molecular explanation for this different behavior on the two different targets. In vivo experiments showed that this compound induced a significant reduction in blood glucose and lipid levels in an STZ-induced diabetic mouse model displaying no toxic effects on bone, kidney, and liver. By examining in depth the antihyperglycemic activity of 10, we found out that it produced a slight but significant inhibition of the mitochondrial pyruvate carrier, acting also through insulin-independent mechanisms. This is the first example of a PPARα/γ dual agonist reported to show this inhibitory effect representing, therefore, the potential lead of a new class of drugs for treatment of dyslipidemic type 2 diabetes.

AbstractThis paper employs a real-time, open-question qualitative survey method to investigate the past, present, and future of the Lean Industry 4.0 field. A major trend is found in researchers moving from technological backgrounds such as engineering, or other STEM subjects, into more socio-technical departments and developing interests in wider areas such as people, supply chain, and sustainability. A holistic perspective of the field is presented including a call to commonise the researcher-favored term “Lean Industry 4.0.” An inclusive and detailed definition of Lean Industry 4.0 takes into account the different survey responses. A wide range of future research gaps is identified within the context of a first-of-its-type future research framework.

AbstractThis study examines the common themes for integrating Industry 4.0 with quality-related operational excellence methodologies to provide a comprehensive overview of “what” and “how” to combine them in an initial integration process. In addition, the gaps in the present literature are aggregated, and a research plan for the future is proposed. The study is based on a systematic review of 37 papers published in academic journals between 2015 and 2021. Unlike previous reviews, this study concentrates on the “what” and “how” level of total quality management, Lean Six Sigma, and business process management as quality-related operational excellence methodologies integrated with Industry 4.0 to provide a practical perspective when executing their integration and implementation. Findings indicate a strong technical and data-driven integration focus across the three themes. Furthermore, modes of action as moderators of success were derived as initial variables to be included in quality-driven Industry 4.0 transitions. Identifying gaps in the present literature and defining a research agenda centered on operational principles opens up opportunities for future study with significant practical value.

In the last decade, Convolutional Neural Networks (CNNs) have been the de facto approach for automated medical image detection. Recently, Vision Transformers have emerged in computer vision as an alternative to CNNs. Specifically, the Shifted Window (Swin) Transformer is a general-purpose backbone that learns attention-based hierarchical features and achieves state-of-the-art performances in a variety of vision tasks. In this work, for the first time, we design and experiment transformer-based models for mass detection in digital mammograms leveraging Swin transformer as a backbone multiscale feature extractor. Experiments on the largest publicly available mammography image database OMI-DB yield a True Positive Rate (TPR) of 75.7%at 0.1 False Positives per Image (FPpI) for the best transformer model, with 2.5%TPR improvement over its convolutional counterpart and a massive 7.4%TPR over the state-of-the-art. We also combine transformer- and convolution-based detectors with weighted box fusion, achieving an additional 2.4%TPR improvement reaching 78.1%TPR at 0.1 FPpI.

In recent years, lean manufacturing (LM) has been used to improve the operational performance of organisations by improving the productivity, quality and profitability of their operations. However, some authors claim that these organisations are struggling to implement and maintain lean initiatives in a sustainable way over time. This paper proposes a list of critical factors for sustaining LM in the long-term. We used a multi-method research approach. First, we generated a list of critical factors for sustaining LM in the long-term using the systematic literature review approach. Subsequently, interviews with experts were used to refine these factors. Following this, two case studies were performed, and the results passed through another round of interviews with experts to ensure the robustness of the results. The main result of the research is a list of 19 LM sustainability factors. To the best of the authors' knowledge, this is the first research to propose a scientifically validated list of critical factors for sustaining LM. The contribution of this work lies in consolidating the lean sustainability factors widely found in the literature. In practical terms, the proposed list can guide managerial efforts towards sustaining lean in the long-term. [Submitted: 22 April 2021; Accepted: 29 December 2021]

Psychiatric symptoms frequently occur in multiple sclerosis (MS), presenting with a complex phenomenology that encompasses a large clinical spectrum from clear-cut psychiatric disorders up to isolated psychopathological manifestations. Despite their relevant impact on the overall disease burden, such clinical features are often misdiagnosed, receive suboptimal treatment and are not systematically evaluated in the quantification of disease activity. The development of psychiatric symptoms in MS underpins a complex pathogenesis involving both emotional reactions to a disabling disease and structural multifocal central nervous system damage. Here, we review MS psychopathological manifestations under a biological perspective, highlighting the pathogenic relevance of synaptic and neural network dysfunction. Evidence obtained from human and experimental disease models suggests that MS-related psychiatric phenomenology is part of a disconnection syndrome due to diffuse inflammatory and neurodegenerative brain damage.

BackgroundThe decline of humoral response to COVID-19 vaccine led to authorise a booster dose. Here, we characterised the kinetics of B-cell and T-cell immune responses in patients with multiple sclerosis (PwMS) after the booster dose.MethodsWe enrolled 22 PwMS and 40 healthcare workers (HCWs) after 4–6 weeks from the booster dose (T3). Thirty HCWs and 19 PwMS were also recruited 6 months (T2) after the first dose. Antibody response was measured by anti-receptor-binding domain (RBD)-IgG detection, cell-mediated response by an interferon (IFN)-γ release assay (IGRA), Th1 cytokines and T-cell memory profile by flow cytometry.ResultsBooster dose increased anti-RBD-IgG titers in fingolimod-treated, cladribine-treated and IFN-β-treated patients, but not in ocrelizumab-treated patients, although antibody titres were lower than HCWs. A higher number of fingolimod-treated patients seroconverted at T3. Differently, T-cell response evaluated by IGRA remained stable in PwMS independently of therapy. Spike-specific Th1-cytokine response was mainly CD4+T-cell-mediated, and in PwMS was significantly reduced (p<0.0001) with impaired IL-2 production compared with HCWs at T3. In PwMS, total Th1 and IFN-γ CD4+T-cell responders to spike protein were increased from T2 to T3.Compared with HCWs, PwMS presented a higher frequency of CD4+and CD8+terminally differentiated effector memory cells and of CD4+effector memory (TEM) cells, independently of the stimulus suggesting the association of this phenotype with MS status. CD4+and CD8+TEMcell frequency was further increased at T3 compared with T2.ConclusionsCOVID-19 vaccine booster strengthens humoral and Th1-cell responses and increases TEMcells in PwMS.

Objective The aetiologic diagnosis of non-traumatic acute myelopathies (AMs), and their differentiation from other mimicking conditions (i.e. ‘mimics’), are clinically challenging, especially in the emergency setting. Here, we sought to identify: (i) red flags suggesting diagnoses alternative to AMs and (ii) clinical signs and magnetic resonance imaging (MRI) features differentiating non-compressive from compressive AMs.Materials and methods We retrospectively retrieved MRI scans of spinal cord dictated at emergency room from January 2016 to December 2020 in the suspicion of AMs. Patients with traumatic myelopathies and those with subacute/chronic myelopathies (i.e. MRI scans acquired >48 h from symptom onset) were excluded from analysis.Results Our search retrieved 105 patients; after excluding 16 cases of traumatic myelopathies and 14 cases of subacute/chronic myelopathies, we identified 30 cases with non-compressive AMs, 30 cases with compressive AMs and 15 mimics. The presence of pyramidal signs (p= 0.012) and/or pain (p= 0.048) correctly identified 88% of cases with AMs. We failed to identify clinical indicators for distinguishing non-compressive and compressive AMs, although cases with inflammatory AMs were younger than cases with all the remaining conditions (p< 0.05). Different MRI patterns could be described according to the final diagnosis: among non-compressive AMs, inflammatory lesions were more often posterior or central; vascular malformation had a fairly widespread distribution; spine ischaemia was more often central. Anterior or lateral compression were more often associated with neoplasms and disc herniation , whereas hemorrhages and infections produced spine compression on all sides.Conclusion We propose a simple clinical indicator (i.e. pyramidal signs and/or pain) to distinguish AMs from their mimics in an emergency setting. Urgent spinal cord MRI remains essential to discriminate compressive and non-compressive aetiologies.

•Neurophysiological tools offer several advantages in diagnosing cognitive impairment in MS patients, including objectivity, cost-effectiveness, and resistance to practice effects compared to neuropsychological tests.•There is a limited body of evidence supporting the effectiveness of non-invasive brain stimulation (NIBS) techniques for treating cognitive impairment in MS.•The P300 component in EEG studies has emerged as a reliable neurophysiological parameter associated with cognitive impairment in MS patients, with potential applications in monitoring, diagnosing, and prognosticating the disease.•Recent research has identified distinct cognitive phenotypes in MS patients, suggesting that future neurophysiological studies should consider these differences to provide more comprehensive insights into cognitive impairment across diverse patient groups.

Genetics and biology may influence the age of onset of anorexia nervosa (AN). The aims of this study were to determine whether common genetic variation contributes to age of onset of AN and to investigate the genetic associations between age of onset of AN and age at menarche.

ABSTRACTIntroductionThe blockade of immune checkpoints, especially the PD-1/PD-L1 pathway with therapeutic antibodies, has shown success in treating cancers in recent years. Seven monoclonal antibodies (mAbs) targeting PD-1 or PD-L1 have been approved by FDA. However, mAbs exhibit several disadvantages as compared to small molecules such as poor permeation, high manufacturing costs, immunogenicity as well as lacking oral bioavailability. Recently, small-molecule inhibitors targeting PD-L1 have been disclosed with the ability to modulate the PD-1/PD-L1 pathway.Areas coveredThe authors reviewed small molecules targeting PD-L1 that block the PD-1/PD-L1 protein–protein interaction for the treatment of various diseases.Expert opinionCompared with mAbs, PD-1/PD-L1 small-molecule inhibitors show several advantages such as improved tissue penetration, low immunogenicity, well-understood formulation and lower manufacturing costs. They can serve as complementary or synergistically with mAbs for immune therapy. However, at this time most of the reported inhibitors are still inferior to therapeutic antibodies in their inhibitory activities due to smaller molecular weight. Therefore, better small molecules need to be developed to improve their potencies. Moreover, although several PD-L1 small-molecule inhibitors have shown excellent preclinical results, their safety and efficacy in the clinic still awaits further validation.

Purpose: This study aims to investigate the impact of traditional teaching and active learning methods in lean management (LM) on the development of both soft and hard skills. Design/methodology/approach: Through a longitudinal study, team members from two different organisations (the administrative sector of a public higher education institution and a public teaching hospital), each adopting different teaching methods to support their LM trainings, were systematically examined at four moments during an 18-month period. How teaching methods impacted team members’ development and knowledge was then assessed using multivariate data analysis techniques. Findings: Results indicated that LM trainings can provide significant impacts when a combination of traditional teaching methods and active learning is adopted. Traditional teaching methods can be a good choice for learning hard skills depending on resources’ availability. However, it is recommended to include active learning methods to assist in the comprehension of more complex and abstract LM concepts (soft skills). Originality/value: Although there exists a large number of publications on the relationship between LM implementation and teaching methods, the number of studies that consider the development of both hard and soft skills is rather limited. This study complements the existing literature on LM by identifying which teaching methods can support the development of hard skills and which the development of soft skills. Such identification facilitates the work of both scholars wishing either to begin or to dig deeper into this sphere and practitioners pursuing the best outcomes from LM.

Purpose: The importance of a lean health-care supply chain is increasingly discussed. However, it is still not very clear how lean practices relate to resilience capabilities, as there are synergies and divergences between them. This study aims at identifying the relationship between lean practices and resilience capabilities in the health-care supply chain. Design/methodology/approach: This study conducted a scoping review based on five databases, which allowed the content analysis of 44 articles. Such analysis allowed the verification of trends and volume of studies on this topic. Further, the descriptive numerical and thematic analyses enabled the proposition of a conceptual framework, relating the adoption of lean practices to the development of resilience capabilities according to the tiers of the health-care supply chain in different value streams. Findings: Three research directions were derived from this scoping review: empirical validation of the contribution of lean practices to resilience capabilities in the health-care supply chain; systemic implementation of lean practices across tier levels of the health-care supply chain; and complementary approaches to lean implementation toward a more resilient health-care supply chain. Practical implications: The understanding of these relationships provides health-care managers arguments to prioritize the application of lean practices to improve desired resilience capabilities in the entire health-care supply chain. Originality/value: To the best of the authors’ knowledge, there is no similar study in the literature.

This article aims at examining the mediating role of resilience development on the association between lean principles adoption and operational performance in healthcare supply chain. This study carried out a survey of 123 Brazilian workers from healthcare supply chain. Two hypotheses were tested: H1 - Lean principles adoption positively impacts the resilience development in healthcare supply chain and H2 - The resilience development positively mediates the effect of lean principles adoption on operational performance in healthcare supply chain. It was carried out a set of Ordinary Least Square (OLS) hierarchical linear regression models to test the hypotheses. The results showed that both hypothesis were supported. Regarding the theoretical contributions, the study brings empirical evidence involving these relationships since these verifications are scarce in the literature. Regarding practical contributions, the results of this research can help managers to establish clearer expectations regarding the lean principles adoption into the healthcare supply chain in an environment with many disruptions. Moreover, lean and resilience are often treated individually and often as opposing forces. Therefore, this study shows that these two paradigms can be developed mutually and with a common goals, as the improvement of operational performance.

This paper aims at investigating the potential of Knowledge Management (KM) as a mediator on the association between Industry 4.0 technologies (I4.0) and operational performance. For that, we gathered data through a survey with 112 managerial-level employees of manufacturers located in Brazil. Information was then analysed by means of multivariate data techniques. This research found that there is indeed a direct and positive relationship between I4.0 technologies, specific KM processes and operational performance. However, our findings reveal that a general approach to KM cannot be defined and specific reflections must be developed with reference to each company, context or situation.

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N= 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosumand in a model including all three PRSs and other covariates, and the PRS of ADHD (β = −0.14; 95% confidence interval [CI]: −0.24 to −0.03; pvalue = 0.010) and MDD (β = −0.16; 95% CI: −0.27 to −0.04; pvalue = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34–1.93; pvalue = 2e−7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD.

Lithium is a first-line medication for bipolar disorder (BD), but only one in three patients respond optimally to the drug. Since evidence shows a strong clinical and genetic overlap between depression and bipolar disorder, we investigated whether a polygenic susceptibility to major depression is associated with response to lithium treatment in patients with BD. Weighted polygenic scores (PGSs) were computed for major depression (MD) at different GWAS pvalue thresholds using genetic data obtained from 2586 bipolar patients who received lithium treatment and took part in the Consortium on Lithium Genetics (ConLi+Gen) study. Summary statistics from genome-wide association studies in MD (135,458 cases and 344,901 controls) from the Psychiatric Genomics Consortium (PGC) were used for PGS weighting. Response to lithium treatment was defined by continuous scores and categorical outcome (responders versus non-responders) using measurements on the Aldascale. Associations between PGSs of MD and lithium treatment response were assessed using a linear and binary logistic regression modeling for the continuous and categorical outcomes, respectively. The analysis was performed for the entire cohort, and for European and Asian sub-samples. The PGSs for MD were significantly associated with lithium treatment response in multi-ethnic, European or Asian populations, at various pvalue thresholds. Bipolar patients with a low polygenic load for MD were more likely to respond well to lithium, compared to those patients with high polygenic load [lowest vs highest PGS quartiles, multi-ethnic sample: OR = 1.54 (95% CI: 1.18–2.01) and European sample: OR = 1.75 (95% CI: 1.30–2.36)]. While our analysis in the Asian sample found equivalent effect size in the same direction: OR = 1.71 (95% CI: 0.61–4.90), this was not statistically significant. Using PGS decile comparison, we found a similar trend of association between a high genetic loading for MD and lower response to lithium. Our findings underscore the genetic contribution to lithium response in BD and support the emerging concept of a lithium-responsive biotype in BD.

Classic major histocompatibility complex class I (MHC-I) presentation relies on shuttling cytosolic peptides into the endoplasmic reticulum (ER) by the transporter associated with antigen processing (TAP). Viruses disable TAP to block MHC-I presentation and evade cytotoxic CD8+T cells. Priming CD8+T cells against these viruses is thought to rely solely on cross-presentation by uninfected TAP-functional dendritic cells. We found that protective CD8+T cells could be mobilized during viral infection even when TAP was absent in all hematopoietic cells. TAP blockade depleted the endosomal recycling compartment of MHC-I molecules and, as such, impaired Toll-like receptor–regulated cross-presentation. Instead, MHC-I molecules accumulated in the ER–Golgi intermediate compartment (ERGIC), sequestered away from Toll-like receptor control, and coopted ER-SNARE Sec22b-mediated vesicular traffic to intersect with internalized antigen and rescue cross-presentation. Thus, when classic MHC-I presentation and endosomal recycling compartment–dependent cross-presentation are impaired in dendritic cells, cell-autonomous noncanonical cross-presentation relying on ERGIC-derived MHC-I counters TAP dysfunction to nevertheless mediate CD8+T cell priming.

Purpose: This study aims to propose a method for measuring lean supply chain management (LSCM) maturity based on the main lean practices and existing waste of a supply chain. Design/methodology/approach: A three-stage approach was developed. First, a thorough literature review was performed to raise concepts and previous findings on maturity models (MMs) and LSCM. This review’s outcomes were then validated by experts in the field using the fuzzy Delphi method (FDM). Subsequently, the proposed model was illustrated and assessed based on a multi-case study. Findings: All companies attained high outcomes in the elimination of the waste pillar. The pillars of logistics management, continuous improvement and information technology management also stood out in the three organizations’ results. The company with the lowest maturity level operates in a make-to-order production policy, which may harm the lean supply in its supply chain. Practical implications: The proposed model can reveal external opportunities and threats and internal strengths and weaknesses in supply chains (SCs). It is also capable of providing a clear roadmap for SC improvement in companies. Originality/value: To the best of the authors’ knowledge, no study to date has proposed a MM in the LSCM context using FDM and considering the crucial relationship between lean practices and wastes.

The necessity to establish novel solutions for decentralized monitoring is attracting attention in all fields of analytical chemistry, i.e., clinical, pharmaceutical, environmental, agri-food. The research around the terms “point-of-need”, “point-of-care”, “lab-on-chip”, “biosensor”, “microfluidics”, etc. is/has been always aimed at the possibility to produce easy-to-use and fast-response devices to be used by nonspecialists. However, the routes to produce the optimal device might be time-consuming and costly. In this Feature, we would like to highlight the role of chemometric-based approaches that are useful in the conceptualization, production, and data analysis in developing reliable portable devices and also decrease the amount of experiments (thus, costs) at the same time. Readers will be provided a concise overview regarding the most employed chemometric tools used for target identification, design of experiments, data analysis, and digitalization of results applied to the development of diverse portable analytical platforms. This Feature provides a tutorial perspective regarding all the major methods and applications that have been currently developed. In particular, the presence of a concise and informative table assists analytical chemists in utilizing the right chemometrics-based tool depending on the architectures and transduction.

Direct extraction and ionization techniques using minute amounts of solvent can be employed for the rapid analysis of chemical components in a sample without any sample preparation steps. This type of approach is important for mass spectrometry imaging of samples with multiple chemical components that have different spatial distributions (i.e., biological tissues). To improve the spatial resolution of such imaging, it is necessary to reduce the solvent volume for extraction and deliver it to the sample surface. This report describes a feedback control system applied to tapping-mode scanning probe electrospray ionization. By combining the measurement technique of capillary probe vibration with the dynamic distance control system between the probe and the sample, the vibration amplitude of the probe is maintained while the probe scans over uneven samples. This method allows simultaneous high-resolution imaging of molecular distribution, surface topography, and amplitude/phase changes in the probe vibration. Such multimodal imaging is demonstrated on rhodamine B thin films in microwells and on a mouse brain tissue section. This technique can generally be applied to examine the multidimensional molecular distribution and the surface profiles of various objects.

Purpose: The purpose of this paper is to classify the main barriers related to the implementation of lean supply chain management (LSCM) and prioritize its main practices. A case study approach was conducted to obtain values for the intensity of the relationship between LSCM practices and barriers to mitigate or even anticipate difficulties in its implementation. Design/methodology/approach: The case study in a company in southern Brazil that is in lean implementation is used for conducting this research. The methodology was structured in three steps, namely, classification of the main barriers through risk analysis, selection of LSCM practices appropriate to the company context and ranking and prioritization of the main practices of LSCM related to the barriers to lean implementation. Findings: As a result, with the combination of two approaches, failure mode and effect analysis (FMEA) and analytical hierarchical process (AHP), it was possible to obtain values for the intensity of the relationship between LSCM practices and barriers. From the 12 barriers, 5 reached a high-risk degree that can be mitigated, and among the 18 practices found, 7 have the potential to be implemented. Originality/value: This research allows direct efforts to continuously improve the supply chain to mitigate or even anticipate difficulties in its implementation. The proposed methodology, combining FMEA and AHP is easy to apply and understand, allowing managers and professionals to replicate it in the context of their supply chain and verify results similar and comparable to those obtained here.

Lean production (LP) is widely acknowledged as an approach to establish an organisational culture of continuous improvement for superior performance results. Despite the fact that there are different LP training courses for facilitating its implementation, the effectiveness of their results is not clearly known. Thus, the aim of this paper is to identify the main LP teaching methods and their learning assessment approaches. For that, we performed a systematic literature review in the main databases and analysed the retrieved articles' content both quantitatively and qualitatively. Such identification helps researchers and practitioners interested in LP implementation to select and adapt the most appropriate LP teaching method according to the desired objectives, since there is no consensus on the best approach.

Background: Angiotensin-converting enzyme 2 (ACE2) is primarily involved in the maturation of angiotensin. It also represents the main receptor for the Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2) that caused a serious epidemic COVID-19. Available evidence indicates that at the cell membrane, ACE2 can form heteromeric complexes with other membrane proteins, including the amino acid transporter B0 AT1 and G protein-coupled receptors (GPCR). Objective: It is well known that during the formation of quaternary structures, the configuration of every single monomer is re-shaped by its interaction pattern in the macromolecular complex. Therefore, it can be hypothesized that the affinity of ACE2 to the viral receptor-binding domain (RBD), when in a heteromeric complex, may depend on the associated partner. Methods: By using established docking and molecular dynamics procedures, the reshaping of monomer was explored in silico to predict possible heterodimeric structures between ACE2 and GPCR, such as angiotensin and bradykinin receptors. The associated possible changes in the binding affinity between the viral RBD and ACE2 when in the heteromeric complexes were also estimated. Results and Conclusion: The results provided support to the hypothesis that the heteromerization state of ACE2 may modulate its affinity to the viral RBD. If experimentally confirmed, ACE2 heteromerization may contribute to explain the observed differences in susceptibility to virus infection among individuals and to devise new therapeutic opportunities.

This study aims at identifying the existing methods for materials planning and control applied in healthcare organisations. An extensive literature review was performed in the main databased based upon two research axes: 1) materials planning and control; 2) health services. Besides the content and bibliometric analysis, literature evidence was analysed from the perspective of two theoretical lenses: types of flow of value and complexity levels of hospital departments. Results from this review allowed the indication of two main research directions: 1) integration among methods for materials planning and control; 2) financial assessment of the implementation of materials planning and control methods in hospitals. The healthcare sector is highly important due to the criticality of it is quality, safety concerning patient care and the high cost involved in its operations. Furthermore, resources are usually limited, complicating its management.

The objective of this research is to design strategies based on lean manufacturing principles in order to improve the efficiency of the production process of a metalworking company. Lean manufacturing is a continuous improvement philosophy that aims to eliminate or reduce, through different tools, all types of waste found in a production process. During the project, a detailed analysis of the production processes was carried out; with operations diagrams, Pareto charts, Ishikawa diagrams, VSM, among others. With these, it was possible to determine the different problems present in anchor rods and clamps production lines. According to the project's progress, each of the improvement strategies was developed, proving its functionality and utility for productivity and cost reduction. Finally, the waste reductions generated by each strategy were calculated, and then the benefits and the investment required for the implementation of the strategies, justified by the cost-benefit analysis.

Purpose: This paper aims at examining the impact that COVID-19 pandemic and its related work implications have on the relationship between lean implementation and service performance. Design/methodology/approach: The author surveyed service organizations that have been implementing lean for at least two years and remotely maintained their activities during the COVID-19 outbreak. Multivariate data techniques were applied to analyze the dataset. This study was grounded on sociotechnical systems theory. Findings: The findings indicate that organizations that have been implementing lean services more extensively are also more likely to benefit from the effects that the COVID-19 had on work environments, especially in the case of home office. Nevertheless, social distancing does not appear to mediate the effects of lean services on both quality and delivery performances. Originality/value: Since the pandemic is a recent phenomenon with unprecedented effects, this research is an initial effort to determine the effect the pandemic has on lean implementation and services' performance, providing both theoretical and practical contributions to the field.

ObjectiveTo explore whether age at onset increased over time despite a shortened interval from the initial clinical demyelinating event to the diagnosis of multiple sclerosis (MS), as promoted by updated diagnostic criteria.MethodsThis was an independent, multicentre, retrospective study based on data from 4345 patients with relapsing-onset MS attending three tertiary MS Clinics in Italy. After stratifying the year of MS onset into four periods (<1991, 1991–2000, 2001–2010, 2011–2021), we analysed the temporal trends in age at onset and interval from onset to diagnosis; we then explored the female-to-male ratio and onset location across different classes of age at onset.ResultsWe observed an increased mean age at onset, and a shortened mean interval to diagnosis over time (p<0.0001). Accordingly, there were more MS onsets at the older age classes of 40-49, 50–59 and ≥60 years (p<0.0001). In cases with age at onset ≥40 years, we also found an increased female-to-male ratio (p=0.007), more frequent spinal cord (p=0.0004) and less frequent supratentorial onset (p=0.008).ConclusionOur study shows a forward shift towards an older age at onset of MS, thus suggesting considerable thought on the place-in-therapy of most currently used disease-modifying treatments, and on the standard of care to an older population.

Elderly patients are under-represented in clinical trials and registries, and a gap of evidence exists for clinical decision making in the setting of acute coronary syndromes (ACS). We aimed to assess the prevalence and independent prognostic impact of valvular heart disease (VHD) diagnosed during the index hospitalization on clinical outcomes among elderly patients with ACS. Included VHDs were moderate-to-severe mitral regurgitation (MR), moderate-to-severe aortic stenosis (AS), or both combined.

For a more comprehensive characterization of molecular heterogeneities of matter, multimodal mass spectrometry imaging must be developed to take advantage of the complementarity of information available through different ionization mechanisms. We report the design, implementation, and performance validation of a laser desorption imaging interface composed of add-on components that adapt a commercial Desorption Electrospray Ionization Mass Spectrometry (DESI–MS) imaging interface for dual imaging of Picosecond Infrared Laser Mass Spectrometry (PIRL–MS) with DESI–MS. The interface utilizes hardware elements and data analysis pipelines already established for DESI–MS imaging, and was further validated in cancer margin assessments using human medulloblastoma cancers. The PIRL–MS images were robust and reproducible across multiple experimental runs on independently prepared xenograft tumors, and could be segmented into cancer and healthy regions in concordance with pathology using a variety of supervised and unsupervised clustering methods. The spectral quality and complexity obtained with this interface were examined with infiltrating and noninfiltrating tumors, and were comparable to other mass spectrometry analysis interfaces. The average PIRL–MS spectra from spatially resolved images could be used for robust cancer m/zmodel building to classify medulloblastoma cancer from healthy tissue without any misclassifications, an observation that held true over close to 70 sampling data points. While the unsupervised spectral analysis methods suggested a slight suppression of signal in the phospholipid range compared to the hand-held configuration, these changes were insufficient to hamper utility in cancer margin assessment with spatially resolved data obtained with our interface. Dual PIRL–MS and DESI–MS imaging of consecutive sections, as suggested by multivariate loading plots, revealed highly complementary molecular information with m/zvalues identifiable with one desorption method sufficient to reveal cancer regions being absent in another, further emphasizing the need for effective hardware and software interfaces for dual mass spectrometry imaging.

ObjectiveThis study aimed to define the minimal evidence of disease activity (MEDA) during treatment that can be tolerated without exposing patients with relapsing-remitting multiple sclerosis at risk of long-term disability.MethodsWe retrospectively collected data of patients followed up to 10 years after starting interferon beta or glatiramer acetate. Survival analyses explored the association between the long-term risk of reaching an Expanded Disability Status Scale≥6.0 and early clinical and MRI activity assessed after the first and second year of treatment. Early disease activity was classified by the so-called ‘MAGNIMS score’ (low: no relapses and <3 new T2 lesions; medium: no relapses and ≥3 new T2 lesions or 1 relapse and 0–2 new T2 lesions; high: 1 relapse and ≥3 new T2 lesions or ≥2 relapses) and the absence or presence of contrast-enhancing lesions (CELs).ResultsAt follow-up, 148/1036 (14.3%) patients reached the outcome: 61/685 (8.9%) with lowscore (reference category), 57/241 (23.7%) with mediumscore (HR=1.94, p=0.002) and 30/110 (27.3%) with highscore (HR=2.47, p<0.001) after the first year of treatment. In the lowscore subgroup, the risk was further reduced in the absence (49/607, 8.1%) than in the presence of CELs (12/78, 15.4%; HR=2.11, p=0.01). No evident disease activity and lowscore in the absence of CELs shared the same risk (p=0.54). Similar findings were obtained even after the second year of treatment.ConclusionsEarly marginal MRI activity of one to two new T2 lesions, in the absence of both relapses and CELs, is associated with a minor risk of future disability, thus representing a simple and valuable definition for MEDA.

In the near future, it is expected that the prevalence of illnesses related to the increasing life expectancies and quality of life, such as neurodegenerative diseases and cardiovascular diseases related to metabolic disorders, will soar to unprecedented levels, leading to high socioeconomic costs. To address this rising threat, natural products are emerging as a novel strategy for the prevention and therapy of these ages- and lifestyle-related diseases, thanks to their high marketability and few side effects. In this patent review, we summarize selected patents for food supplements, functional and fortified foods, filed from 2016 to 2019, categorizing them based on the biological activity of their components.

Over the course of the past decade, peroxisome proliferator-activated receptors (PPARs) have been identified as part of the cannabinoid signaling system: both phytocannabinoids and endocannabinoids are capable of binding and activating these nuclear receptors. Fatty acid amide hydrolase (FAAH) hydrolyzes the endocannabinoid anandamide and other N-acylethanolamines. These substances have been shown to have numerous anticancer effects, and indeed the inhibition of FAAH has multiple beneficial effects that are mediated by PPARα subtype and by PPARγ subtype, especially antiproliferation and activation of apoptosis. The substrates of FAAH are also PPAR agonists, which explains the PPAR-mediated effects of FAAH inhibitors. Much like cannabinoid ligands and FAAH inhibitors, PPARγ agonists show antiproliferative effects on cancer cells, suggesting that additive or synergistic effects may be achieved through the positive modulation of both signaling systems. In this Miniperspective, we discuss the development of novel FAAH inhibitors able to directly act as PPAR agonists and their promising utilization as leads for the discovery of highly effective anticancer compounds.