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Heavy metal (HM) pollution, specifically cadmium (Cd) contamination, is a worldwide concern for its consequences for plant health and ecosystem stability. This review sheds light on the intricate mechanisms underlying Cd toxicity in plants and the various strategies employed by these organisms to mitigate its adverse effects. From molecular responses to physiological adaptations, plants have evolved sophisticated defense mechanisms to counteract Cd stress. We highlighted the role of phytochelatins (PCn) in plant detoxification, which chelate and sequester Cd ions to prevent their accumulation and minimize toxicity. Additionally, we explored the involvement of glutathione (GSH) in mitigating oxidative damage caused by Cd exposure and discussed the regulatory mechanisms governing GSH biosynthesis. We highlighted the role of transporter proteins, such as ATP-binding cassette transporters (ABCs) and heavy metal ATPases (HMAs), in mediating the uptake, sequestration, and detoxification of Cd in plants. Overall, this work offered valuable insights into the physiological, molecular, and biochemical mechanisms underlying plant responses to Cd stress, providing a basis for strategies to alleviate the unfavorable effects of HM pollution on plant health and ecosystem resilience.

Control of particle motion is generally achieved by applying an external field that acts directly on each particle. Here, we propose a global way to manipulate the motion of a particle by dynamically changing the properties of the fluid in which it is immersed. We exemplify this principle by considering a small particle sinking in an anisotropic fluid whose viscosity depends on the shear axis. In the Stokes regime, the motion of an immersed object is fully determined by the viscosity of the fluid through the mobility matrix, which we explicitly compute for a pushpin-shaped particle. Rather than falling upright under the force of gravity, as in an isotropic fluid, the pushpin tilts to the side, sedimenting at an angle determined by the viscosity anisotropy axis. By changing this axis, we demonstrate control over the pushpin orientation as it sinks, even in the presence of noise, using a closed feedback loop. This strategy to control particle motion, that we dub viscous tweezers, could be experimentally realized in systems ranging from polyatomic fluids under external fields to chiral active fluids of spinning particles by suitably changing their direction of global alignment or anisotropy.

Lithium-ion (Li-ion) batteries are crucial in numerous applications, including portable electronics, electric vehicles, and energy storage systems. Electrochemical Impedance Spectroscopy (EIS) is a powerful technique for characterizing batteries, providing valuable insights into charge transfer kinetics like ion diffusion and interfacial reactions. However, obtaining comprehensive and diverse datasets for battery State of Charge (SoC) studies remains challenging due to the complex nature of battery operations and the time-intensive testing process. This paper presents a novel and original EIS dataset specifically designed for 600 mAh capacity Lithium Iron Phosphate (LFP) batteries at various SoC levels. The dataset includes repeated EIS measurements using different battery discharging cycles, allowing researchers to examine the frequency domain properties and develop data-driven algorithms for assessing battery SoC and predicting performance. The data acquisition system employs a battery specific impedance meter and an electronic load, ensuring accurate and controlled measurements. The dataset, comprising EIS measurements from multiple LFP batteries, serves as a valuable resource for researchers in the fields of battery technology, electrochemistry, power sources, and energy storage. Moreover, industries such as consumer electronics, power systems, and electric transportation can benefit from the dataset's insights for effectively managing rechargeable battery devices. The presented dataset expands the scope of impedance spectroscopy measurements and holds significant potential for future applications and advancements in Li-ion battery technologies.

Background: The antisense oligonucleotide against APOC3 mRNA volanesorsen was recently introduced to treat Familial Chylomicronemia Syndrome (FCS). Cases of decreased platelet count are reported among patients treated with volanesorsen. The aim of the study was to evaluate platelet function and thrombin generation (TG) assessment in FCS patients receiving volanesorsen. We performed a cross-sectional study on FCS patients treated with volanesorsen. Methods: Changes in platelet count PLC were assessed from baseline to Tw12 and Tw36. To assess TG, samples were processed by CAT (with PPP-reagent LOW). The results were expressed by the thrombogram graphic (thrombin variation over time); LagTime; endogenous thrombin potential (ETP); peak; time to reach peak (ttpeak), StartTail and Velocity Index. Platelet aggregation was assessed by testing different agonists using the turbidimetry method. Results: Four FCS patients and four matched healthy controls were included in the present study. Changes in PLC were 30% at Tw12 and 34% at Tw36. Thrombin generation results showed values in the normal range (for patients and controls, respectively, LagTime:10.42 ± 4.40 and 9.25 ± 0.99; ttPeak:14.33 ± 4.01 and 13.10 ± 0.67; StartTail: 32.13 ± 3.54 and 29.46 ± 1.69; Velocity Index: 20.21 ± 3.63 and 33.05 ± 13.21; ETP: 599.80 ± 73.47 and 900.2 ± 210.99; peak value: 76.84 ± 1.07 and 123.30 ± 39.45) and no significant difference between cases and controls. Platelet aggregation test showed values in range, with no significant difference compared to healthy controls. Conclusions: Our study showed for the first time that no significant changes in general hemostasis assessed by TG and in platelet function were observed in FCS patients receiving volanesorsen.

IntroductionThis review aims to analyze the existing literature on local recurrence (LR) in patients undergoing partial nephrectomy (PN) for renal cell carcinoma, identifying relative risk factors, and exploring optimal clinical management strategies.MethodsA comprehensive literature search was conducted across bibliographic databases, primarily focusing on LR rates. Secondary outcomes included evaluation of positive surgical margins (PSM), nephrometry scores, pathological stage (T and grading), perioperative outcomes, time-to-LR, overall survival, and cancer-specific survival.ResultsDue to the heterogeneity, a narrative synthesis was performed. LR rates after PN varied in the literature; with PSM emerging as a significant risk factor. Other LR risk factors included pathological stage, nephrometry scores, and histological variants. However, evidence regarding optimal LR management in the absence of precise indications was lacking.ConclusionLR represents a significant clinical challenge; requiring multidisciplinary assessment and shared decision-making with patients. Given well-established risk factors, clinicians must tailor management strategies to optimize patient outcomes.

ABSTRACT Knee osteoarthritis (KOA) is a disabling inflammatory disease that makes walking and activities of daily living difficult. This condition can reduce functional capacity and increase the risk for surgery. Objective: To know the functional capacity of patients with KOA evaluated by the six-minute walk test (6MWT). Method: This cross-sectional study evaluated age, gender, weight, BMI, pain (VAS 90-100), physical disability (WOMAC 0-96), degree of joint damage by radiographic imaging, and 6MWT. Results: A total of 176 patients referred by Orthopedics were evaluated, with the inclusion of 164 participants. The mean age was 61.89 ± 10.62 years, 81% women, 67% with cardiovascular disease, hypertension and/or diabetes, 81% obese, with moderate pain (VAS 47.74 ± 29.27) and according to WOMAC, most had severe or very severe disability. The distance covered in the 6MWT was 354.03 ± 102.03m, 67% of the predicted distance. The maximum heart rate achieved was 107.27 ± 17.71 bpm, which characterizes 68% of the predicted by age. Only 12% of the sample showed a marked drop in oxygenation in the 6MWT and 40% had a recovery heart rate in the 1st minute below 15 bpm. Conclusion: Patients with KOA, who were evaluated by the 6MWT, have low functional capacity and physical deconditioning. Level of Clinical Evidence III, Case Control Study.

BackgroundThe coproduction of care involves patients and families partnering with their clinicians and care teams, with the premise that each brings their own perspective, knowledge, and expertise, as well as their own values, goals, and preferences, to the partnership. Dashboards can display meaningful patient and clinical data to assess how a patient is doing and inform shared decision-making. Increasing communication between patients and care teams is particularly important for children with chronic conditions. Juvenile idiopathic arthritis (JIA), the most common chronic pediatric rheumatic condition, is associated with increased pain, decreased function, and decreased quality of life.ObjectiveThe aim of this study is to design a dashboard prototype for use in coproducing care in patients with JIA. We evaluated the use and needs of end users, obtained a consensus on the necessary dashboard data elements, and constructed display prototypes to inform meaningful discussions for coproduction.MethodsA human-centered design approach involving parents, patients, clinicians, and care team members was used to develop a dashboard to support the coproduction of care in 4 ambulatory pediatric rheumatology clinics. We engaged a multidisciplinary team (n=18) of patients, parents, clinicians, nurses, and staff during an in-person kick-off meeting followed by biweekly meetings. We also leveraged advisory panels. Teams mapped workflows and patient journeys, created personas, and developed dashboard sketches. The final dashboard components were determined via Delphi consensus voting. Low-tech dashboard testing was completed during clinic visits, and visual display prototypes were iterated by using the Plan-Do-Study-Act methodology. Patients and clinicians were surveyed regarding their experiences.ResultsTeams achieved consensus on what data mattered most at the point of care to support patients with JIA, families, and clinicians collaborating to make the best possible health care decisions. Notable themes included the right data in the right place at the right time, data in once for multiple purposes, patient and family self-management components, and the opportunity for education and increased transparency. A final set of 11 dashboard data elements was identified, including patient-reported outcomes, clinical data, and medications. Important design considerations featured the incorporation of real-time data, clearly labeled graphs, and vertical orientation to facilitate review and discussion. Prototype paper-testing with 36 patients and families yielded positive feedback, with 89% (8/9) to 100% (9/9) of parents (n=9) and 80% (8/10) to 90% (9/10) of clinicians (n=10) strongly agreeing or agreeing that the dashboard was useful during clinic discussions, helped to talk about what mattered most, and informed health care decision-making.ConclusionsWe developed a dashboard prototype that displays patient-reported and clinical data over time, along with medications that can be used during a clinic visit to support meaningful conversations and shared decision-making among patients with JIA, their families, and their clinicians and care teams.

CDKL5 deficiency disorder (CDD) is an X-linked dominant epileptic encephalopathy, characterized by early-onset and drug-resistant seizures, psychomotor delay, and slight facial features. Genomic variants inactivating CDKL5 or impairing its protein product kinase activity have been reported, making next-generation sequencing (NGS) and chromosomal microarray analysis (CMA) the standard diagnostic tests. We report a suspicious case of CDD in a female child who tested negative upon NGS and CMA and harbored an X chromosome de novo pericentric inversion. The use of recently developed genomic techniques (optical genome mapping and whole-genome sequencing) allowed us to finely characterize the breakpoints, with one of them interrupting CDKL5 at intron 1. This is the fifth case of CDD reported in the scientific literature harboring a structural rearrangement on the X chromosome, providing evidence for the hypothesis that this type of anomaly can represent a recurrent pathogenic mechanism, whose frequency is likely underestimated, with it being overlooked by standard techniques. The identification of the molecular etiology of the disorder is extremely important in evaluating the pathological outcome and to better investigate the mechanisms associated with drug resistance, paving the way for the development of specific therapies. Karyotype and genomic techniques should be considered in all cases presenting with CDD without molecular confirmation.

This paper: (1) briefly outlines a study tips presentation that uses both evidence from the cognitive and educational psychology literatures as well as demonstrations to teach students how to study more effectively, and (2) provides empirical evidence about whether this study tips presentation affects students' study habits. We provide a brief overview of the presentation, a handout that summarizes the tips, and a reference list rich with sources that support the efficacy of these study approaches. We also summarize a study we conducted to evaluate the effectiveness of the presentation. Thirty-two students completed a questionnaire about their typical study strategies before and three months following the presentation. Additionally, 102 students who did not attend the presentation (control group) completed the study strategies survey, and their responses were compared to those from 74 students who had attended the presentation sometime between 3 months and 3 years and 3 months earlier. Finally, the 74 presentation attendees rated their memory for, utilization of, and perceived influence of the eight study tips. Results support the efficacy of the "Study Smarter, Not Harder" presentation as a way to improve students' understanding and utilization of effective study approaches.

Although there is a clear relationship between family history (FH) and the risk of gastric cancer (GC), quantification is still needed in relation to different histological types and anatomical sites, and in strata of covariates. The objective was to analyze the risk of GC according to first-degree FH in a uniquely large epidemiological consortium of GC. This investigation includes 5946 cases and 12,776 controls from 17 studies of the Stomach Cancer Pooling (StoP) Project consortium. Summary odds ratios (OR) and the corresponding 95% confidence intervals (CIs) were calculated by pooling study-specific ORs using fixed-effect model meta-analysis techniques. Stratified analyses were carried out by sex, age, tumor location and histological type, smoking habit, socioeconomic status, alcohol intake and fruit consumption. The pooled OR for GC was 1.84 (95% CI: 1.64-2.04; I2 = 6.1%, P heterogeneity = 0.383) in subjects with vs. those without first-degree relatives with GC. No significant differences were observed among subgroups of sex, age, geographic area or study period. Associations tended to be stronger for non-cardia (OR = 1.82; 95% CI: 1.59-2.05 for subjects with FH) than for cardia GC (OR = 1.38; 95% CI: 0.98-1.77), and for the intestinal (OR = 1.92; 95% CI: 1.62-2.23) than for the diffuse histotype (OR = 1.62; 95% CI: 1.28-1.96). This analysis confirms the effect of FH on the risk of GC, reporting an approximately doubled risk, and provides further quantification of the risk of GC according to the subsite and histotype. Considering these findings, accounting for the presence of FH to carry out correct prevention and diagnosis measures is of the utmost importance.

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.

Abstract Hybridisation and introgression of eukaryotic genomes can generate new species or subsume existing ones, with direct and indirect consequences for biodiversity. An understudied component of these evolutionary forces is their potentially rapid effect on host gut microbiomes, and whether these pliable microcosms may serve as early biological indicators of speciation. We address this hypothesis in a field study of angelfishes (genus Centropyge), which have one of the highest prevalence of hybridisation within coral reef fish. In our study region of the Eastern Indian Ocean, the parent fish species and their hybrids cohabit and display no differences in their diet, behaviour, and reproduction, often interbreeding in mixed harems. Despite this ecological overlap, we show that microbiomes of the parent species are significantly different from each other in form and function based on total community composition, supporting the division of parents into distinct species, despite the confounding effects of introgression acting to homogenize parent species identity at other molecular markers. The microbiome of hybrid individuals, on the other hand, are not significantly different to each of the parents, instead harbouring an intermediate community composition. These findings suggest that shifts in gut microbiomes may be an early indicator of speciation in hybridising species.

The analysis of whole genomes of pan‐cancer data sets provides a challenge for researchers, and we contribute to the literature concerning the identification of robust subgroups with clear biological interpretation. Specifically, we tackle this unsupervised problem via a novel rank‐based Bayesian clustering method. The advantages of our method are the integration and quantification of all uncertainties related to both the input data and the model, the probabilistic interpretation of final results to allow straightforward assessment of the stability of clusters leading to reliable conclusions, and the transparent biological interpretation of the identified clusters since each cluster is characterized by its top‐ranked genomic features. We applied our method to RNA‐seq data from cancer samples from 12 tumor types from the Cancer Genome Atlas. We identified a robust clustering that mostly reflects tissue of origin but also includes pan‐cancer clusters. Importantly, we identified three pan‐squamous clusters composed of a mix of lung squamous cell carcinoma, head and neck squamous carcinoma, and bladder cancer, with different biological functions over‐represented in the top genes that characterize the three clusters. We also found two novel subtypes of kidney cancer that show different prognosis, and we reproduced known subtypes of breast cancer. Taken together, our method allows the identification of robust and biologically meaningful clusters of pan‐cancer samples.

Ibrutinib, a tyrosine kinase inhibitor with a broad spectrum of action, has been successfully explored to treat hematological and solid cancers. Herein, we investigated the anti-cancer effect of Ibrutinib on melanoma cell lines. Cytotoxicity was evaluated using the MTT assay. Apoptosis, mitochondrial membrane potential, reactive oxygen species (ROS) production, cell proliferation, and cell cycle stages were determined by flow cytometry. LDH release and Caspase 3/7 activity were determined by colorimetric and luminescent assays, respectively. Cell migration was evaluated by wound scratch assay. Gene expression was determined by real-time PCR. Gene Ontology (GO) enrichment analysis of melanoma clinical samples was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). MTT assays showed that Ibrutinib is toxic for MeWo, SK-MEL-28, and WM164 cells. The annexin V/PI staining, Caspase 3/7 activity, and LDH release in MeWo cells revealed that apoptosis is the primary mechanism of death caused by Ibrutinib. Corroborating such observation, we identified that Ibrutinib treatment impairs the mitochondrial membrane potential of such cells and significantly increases the transcriptional levels of the pro-apoptotic factors ATM, HRK, BAX, BAK, CASP3, and CASP8. Furthermore, Ibrutinib showed antimetastatic potential by inhibiting the migration of MeWo cells. Finally, we performed a functional enrichment analysis and identified that the differential expression of Ibrutinib-target molecules is associated with enrichment of apoptosis and necrosis pathways in melanoma samples. Taken together, our results clearly suggest that Ibrutinib can be successfully explored as an effective therapeutic approach for melanomas.

Phenolic compounds may exert a favorable effect on the risk of several cancer types, including gastric cancer (GC). However, selected polyphenol classes have not been adequately investigated in relation to GC. The aim of this study is to evaluate the association between the intake of polyphenols in relation to GC risk. We used data from the Stomach cancer Pooling (StoP) Project, including 10 studies from six countries (3471 GC cases and 8344 controls). We carried out an individual participant data pooled analysis using a two-stage approach. The summary odds ratios (ORs) of GC for each compound, and the corresponding 95% confidence intervals (95% CI), were computed by pooling study specific ORs obtained through multivariate logistic regression, using random effect models. Inverse associations with GC emerged for total polyphenols (OR = 0.67, 95% CI = 0.54-0.81, for the highest versus lowest quartile of intake), total flavonoids (OR = 0.73, 95% CI = 0.55-0.90), anthocyanidins (OR = 0.74, 95% CI = 0.56-0.92), flavanols (OR = 0.77, 95% CI = 0.66-0.88), flavanones (OR = 0.57, 95%CI = 0.44-0.69), total phenolic acids (OR = 0.75, 95%CI = 0.55-0.94), and hydroxybenzoic acids (OR = 0.73, 95%CI = 0.57-0.89). Results were consistent across strata of age, sex, social class, and smoking habit. Suggestive inverse associations were also found for flavonols (OR = 0.76, 95%CI = 0.51-1.01) and hydroxycinnamic acids (OR = 0.82, 95%CI = 0.58-1.06). Further investigations from longitudinal data are needed to confirm this association.

Topological structures are effective descriptors of the nonequilibrium dynamics of diverse many-body systems. For example, motile, point-like topological defects capture the salient features of two-dimensional active liquid crystals composed of energy-consuming anisotropic units. We dispersed force-generating microtubule bundles in a passive colloidal liquid crystal to form a three-dimensional active nematic. Light-sheet microscopy revealed the temporal evolution of the millimeter-scale structure of these active nematics with single-bundle resolution. The primary topological excitations are extended, charge-neutral disclination loops that undergo complex dynamics and recombination events. Our work suggests a framework for analyzing the nonequilibrium dynamics of bulk anisotropic systems as diverse as driven complex fluids, active metamaterials, biological tissues, and collections of robots or organisms.

Seagrass meadows provide valuable socio-ecological ecosystem services, including a key role in climate change mitigation and adaption. Understanding the natural history of seagrass meadows across environmental gradients is crucial to deciphering the role of seagrasses in the global ocean. In this data collation, spatial and temporal patterns in seagrass meadow structure, biomass and production data are presented as a function of biotic and abiotic habitat characteristics. The biological traits compiled include measures of meadow structure (e.g. percent cover and shoot density), biomass (e.g. above-ground biomass) and production (e.g. shoot production). Categorical factors include bioregion, geotype (coastal or estuarine), genera and year of sampling. This dataset contains data extracted from peer-reviewed publications published between 1975 and 2020 based on a Web of Science search and includes 11 data variables across 12 seagrass genera. The dataset excludes data from mesocosm and field experiments, contains 14 271 data points extracted from 390 publications and is publicly available on the PANGAEA® data repository (https://doi.org/10.1594/PANGAEA.929968; Strydom et al., 2021). The top five most studied genera are Zostera, Thalassia, Cymodocea, Halodule and Halophila (84 % of data), and the least studied genera are Phyllospadix, Amphibolis and Thalassodendron (2.3 % of data). The data hotspot bioregion is the Tropical Indo-Pacific (25 % of data) followed by the Tropical Atlantic (21 %), whereas data for the other four bioregions are evenly spread (ranging between 13 and 15 % of total data within each bioregion). From the data compiled, 57 % related to seagrass biomass and 33 % to seagrass structure, while the least number of data were related to seagrass production (11 % of data). This data collation can inform several research fields beyond seagrass ecology, such as the development of nature-based solutions for climate change mitigation, which include readership interested in blue carbon, engineering, fisheries, global change, conservation and policy.

The detection of contaminants in several environments (e.g., air, water, sewage systems) is of paramount importance to protect people and predict possible dangerous circumstances. Most works do this using classical Machine Learning tools that act on the acquired measurement data. This paper introduces two main elements: a low-cost platform to acquire, pre-process, and transmit data to classify contaminants in wastewater; and a novel classification approach to classify contaminants in wastewater, based on deep learning and the transformation of raw sensor data into natural language metadata. The proposed solution presents clear advantages against state-of-the-art systems in terms of higher effectiveness and reasonable efficiency. The main disadvantage of the proposed approach is that it relies on knowing the injection time, i.e., the instant in time when the contaminant is injected into the wastewater. For this reason, the developed system also includes a finite state machine tool able to infer the exact time instant when the substance is injected. The entire system is presented and discussed in detail. Furthermore, several variants of the proposed processing technique are also presented to assess the sensitivity to the number of used samples and the corresponding promptness/computational burden of the system. The lowest accuracy obtained by our technique is 91.4%, which is significantly higher than the 81.0% accuracy reached by the best baseline method.

The paper proposes a new experimental measurement method for State of Charge (SoC) estimation able to optimize between measurement time and target Accuracy adoptable in Battery Management System (BMS) design where both these parameters are key parameters for the overall management performance. The method is applicable when the SoC is estimated in classes through Electrochemical Impedance Spectroscopy (EIS) and is based on two operating stages: i) the experimental characterization of the real behavior of the considered batteries, ii) a time–Accuracy optimization based on suitable feature selection and Machine Learning approaches. In detail, as for the ii) phase, fixed the number of classes in which the SoC is estimated, the proposed measurement method finds the minimum number of impedance spectrum frequency measurements useful for SoC estimation. This is a big issue for BMS designers since in SoC estimation performed by EIS the measurement time is typically greater than some minutes if no optimization is considered. A possible strategy to reduce the required time for SoC estimation could be using Feature Selection (FS) techniques. In our method the FS is implemented using different combinations of search algorithms and fitness functions. Since FS strongly depends on the experimental set–up, the uncertainty of the measurement system, and the classifier adopted for the data–driving evaluation model, the proposed method is flexible and customizable depending on the specific applications. For this reason, the method is divided into steps where the BMS designers define the requirements based on their needs and their hardware. Also the output FS could be adapted to the different exigencies because the selected features could be different if more emphasis is on increasing classification Accuracy or decreasing the measurement time. Hence, we suggest two application scenarios: in the first one, the only requirement is increasing the classification Accuracy rather than the measurement time optimization. The second scenario has both a classification Accuracy target and lowering the measurement time. We also noted that the proposed method is useful also for increasing the overall classification Accuracy because naturally excludes the features that deceive the classifiers. As an application for the proposed method, we developed an automatic measurement system and performed an experimental campaign on seven Lithium Iron Phosphate batteries. The experimental results show that the proposed approach, for all the considered combinations, significantly reduces the measurement time required for EIS while maintaining high SoC estimation Accuracy. Furthermore, for all the obtained solutions, the effectiveness of our approach was confirmed by the significant savings in measurement time. In particular, the best solution reduces from 4 minutes when using all features to about 1.5 seconds with the selected features.

The problem of detecting illegal pollutants in wastewater is of fundamental importance for public health and security. The availability of distributed, low–cost and low–power monitoring systems, particularly enforced by IoT communication mechanisms and low-complexity machine learning algorithms, would make it feasible and easy to manage in a widespread manner. Accordingly, an End-to-End IoT-ready node for the sensing, local processing, and transmission of the data collected on the pollutants in the wastewater is presented here. The proposed system, organized in sensing and data processing modules, can recognize and distinguish contaminants from unknown substances typically present in wastewater. This is particularly important in the classification stage since distinguishing between background (not of interest) and foreground (of interest) substances drastically improves the classification performance, especially in terms of false positive rates. The measurement system, i.e., the sensing part, is represented by the so-called Smart Cable Water based on the SENSIPLUS chip, which integrates an array of sensors detecting various water-soluble substances through impedance spectroscopy. The data processing is based on a commercial Micro Control Unit (MCU), including an anomaly detection module, a classification module, and a false positive reduction module, all based on machine learning algorithms that have a computational complexity suitable for low-cost hardware implementation.An extensive experimental campaign on different contaminants has been carried out to train machine-learning algorithms suitable for low-cost and low-power MCU. The corresponding dataset has been made publicly available for download. The obtained results demonstrate an excellent classification ability, achieving an accuracy of more than 95% on average, and are a reliable “proof of concept” of a pervasive IoT system for distributed monitoring.

Introduction: Inflammation is a highly prevalent finding in the prostate. Men with inflammation have higher IPSS score and increased prostate size. For men with prostatic inflammation, there is a significantly increased risk of developing acute urinary retention and the need of a surgical approach to the disease. Some laboratory tests (i.e. fibrinogen, C-reactive protein), can play a role in identifying patients at greatest risk of complications and adverse outcomes after surgery. There have been several experiences exploring the role of nutraceutical approach to the prostate inflammation. Aim of our study were to describe the variation in symptoms and inflammatory indexes in men affected by chronic abacterial prostatitis, treated with an herbal extract containing Curcuma Longa 500 mg, Boswellia 300 mg, Urtica dioica 240 mg, Pinus pinaster 200 mg and glycine max 70 mg. Materials and methods: A prospective multicenter study was conducted from February 2021 and March 2022. One hundred patients, with a diagnosis of Chronic Prostatitis were enrolled in a multicentric phase III observational study. They were treated with the herbal extract, one capsule per day, for 60 days. No placebo arm was included. In each patient, inflammatory indexes, PSA, prostate volume, IIEF-5, PUF, uroflowmetry (Qmax), IPSS-QoL, NIH-CPPS were registered and statistically compared at baseline and at the follow up visit. Results: The variation obtained on the inflammation indexes showed a global improvement after treatment, including the PSA reduction. We also recorded a significant improvement on IPSS-QoL, NIH-CPPS, PUF and Qmax scores. Conclusions: The herbal extract considered in our study may represent a promising and safe therapeutic agent leading to a reduction of inflammation markers, and could be used in the treatment of prostatitis and benign prostatic hyperplasia.

Multiple sclerosis (MS) is an autoimmune chronic disease characterized by inflammation and demyelination of the central nervous system (CNS). Despite numerous studies conducted, valid biomarkers enabling a definitive diagnosis of MS are not yet available. The aim of our study was to identify a marker from a blood sample to ease the diagnosis of MS. In this study, since there is evidence connecting the serotonin pathway to MS, we used an ELISA (Enzyme-Linked Immunosorbent Assay) to detect serum MS-specific auto-antibodies (auto-Ab) against the extracellular loop 1 (ECL-1) of the 5-hydroxytryptamine (5-HT) receptor subtype 2A (5-HT2A). We utilized an ELISA format employing poly-D-lysine as a pre-coating agent. The binding of 208 serum samples from controls, both healthy and pathological, and of 104 serum samples from relapsing–remitting MS (RRMS) patients was tested. We observed that the serum-binding activity in control cohort sera, including those with autoimmune and neurological diseases, was ten times lower compared to the RRMS patient cohort (p = 1.2 × 10−47), with a sensitivity and a specificity of 98% and 100%, respectively. These results show that in the serum of patients with MS there are auto-Ab against the serotonin receptor type 2A which can be successfully used in the diagnosis of MS due to their high sensitivity and specificity.

We consider the problem of assessing the non-equilibrium behavior of a system from the study of time series. In particular, we analyze experimental data from a piezoelectric energy harvester driven by broadband random vibrations where the extracted power and the relative tip displacement can be simultaneously measured. We compute autocorrelation and cross-correlation functions of these quantities in order to investigate the system properties under time reversal. We support our findings with numerical simulations of a linear underdamped Langevin equation, which very well describes the dynamics and fluctuations of the energy harvester. Our study shows that, due to the linearity of the system, from the analysis of a single variable, it is not possible to evidence the non-equilibrium nature of the dynamics. On the other hand, when cross-correlations are considered, the irreversible nature of the dynamics can be revealed.