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The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic control for people with different amputation levels of the limb. Indeed, TMR surgery makes it possible to obtain reinnervated areas that act as biological amplifiers of the motor control. On the technological side, a great deal of research has been conducted in order to evaluate various types of myoelectric prosthetic control strategies, whether direct control or pattern recognition-based control. In the literature, different control performance metrics, which have been evaluated on TMR subjects, have been introduced, but no accepted reference standard defines the better strategy for evaluating the prosthetic control. Indeed, the presence of several evaluation tests that are based on different metrics makes it difficult the definition of standard guidelines for comprehending the potentiality of the proposed control systems. Additionally, there is a lack of evidence about the comparison of different evaluation approaches or the presence of guidelines on the most suitable test to proceed for a TMR patients case study. Thus, this review aims at identifying these limitations by examining the several studies in the literature on TMR subjects, with different amputation levels, and proposing a standard method for evaluating the control performance metrics.

Annually, 3000–3500 tons of sea urchins are harvested in the Mediterranean Sea, with only their gonads being consumed (10–30% of the total weight), leaving the rest as waste. This waste, consisting of the skeleton, is rich in biominerals, mainly calcium with a small amount of magnesium, and contains potent antioxidant compounds. Considering the issues of resource overconsumption, and in line with the circular economy concept, this study explored the potential of replacing limestone-derived calcium with sea urchin waste in the diet of laying hens, which require this element to produce eggs. The experiment involved two groups of hens: one receiving a control diet containing limestone-derived calcium, and another fed an experimental diet containing sea urchin waste as alternative source of calcium. Parameters such as egg production, animal welfare, egg quality, and bone breaking strength were assessed. Additionally, the antioxidant activity and carotenoid content of the diets were evaluated, revealing no significant differences between the two groups. Both groups exhibited similar egg production rates; however, the treated group had a lower percentage of discarded eggs and fewer lesions on the head, back, and tail, indicating better animal welfare. The treated group produced eggs with significantly thicker shells, though no significant differences were observed in eggshell weight, breaking strength, ultrastructure. There were also no differences in yolk colour or antioxidant activity between the two groups. Similarly, no significant differences were found in tibia breaking strength, confirming that sea urchin waste can provide bioavailable calcium without compromising bone quality. In conclusion, substituting limestone-derived calcium with sea urchin waste in laying hens’ diets positively affected animal welfare and improved eggshell thickness without compromising egg quality, suggesting that sea urchin waste can be recycled as a valuable alternative to limestone-derived calcium in laying hens’ feed. However, further research is necessary to confirm these findings.

Background Chronic kidney disease (CKD) is estimated that by 2040, it will be the 5th highest cause of years of life lost globally due to the aging population and the rising prevalence of age-related diseases such as hypertension and diabetes. Therefore, a deep comprehension of the biological and molecular mechanisms underlying kidney fibrosis is urgently needed to facilitate the development of new therapeutic strategies that can hinder disease progression and simultaneously address the comorbidities associated with CKD. Main body In the last years numerous studies have elucidated the complex interplay of various cellular and molecular pathways that contribute to the progression of kidney fibrosis. As a result, several promising therapeutic targets have been identified, paving the way for innovative treatment strategies. These include endogenous (Dickkopf-1, Klotho, and secreted frizzled-related proteins) and exogenous (ICG-001 and relaxin) modulators of the WNT/β-catenin pathway; inhibitors of TGF-β (BMP-7, ncRNA, fresolimumab); sphingosine analogs (fingolimod). In addition to these indirect approaches, therapies with direct antifibrotic activity have also gained significant attention in the field. Among these alternatives, SGLT2 inhibitors and finerenone have emerged as particularly promising options due to the favorable outcomes observed in large-scale clinical trials. These studies have highlighted their effectiveness in slowing disease progression and improving patient outcomes. Conclusions Despite the encouraging results, additional research is necessary to fully assess the protective roles of these treatments in various clinical contexts. This review aims to synthesize the available data and current insights into this important issue.

Background In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes. Methods The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the “Elbow classifier”, the “Wrist classifier”, and the “Hand classifier” provided the simultaneous control of the elbow, hand, and wrist joints, respectively. Results Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist). Conclusions In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.

The myoelectric control strategy, based on surface electromyographic signals, has long been used for controlling a prosthetic system with multiple degrees of freedom. Several methods classify gestures and force levels but the simultaneous real-time control of hand/wrist gestures and force levels did not yet reach a satisfactory level of effectiveness. In this work, the hierarchical classification approach, already validated on 31 healthy subjects, was adapted for the real-time control of a multi-DoFs prosthetic system on 15 trans-radial amputees. The effectiveness of the hierarchical classification approach was assessed by evaluating both offline and real-time performance using three algorithms: Logistic Regression (LR), Non-linear Logistic Regression (NLR), and Linear Discriminant Analysis (LDA). The results of this study showed the offline performance of amputees was promising and comparable to healthy subjects, with mean F1 scores of over 90% for the \"Hand/wrist gestures classifier\" and 95% for the force classifiers, implemented with the three algorithms with features extraction (FE). Another significant finding of this study was the feasibility of using the hierarchical classification strategy for real-time applications, due to its ability to provide a response time of 100 ms while maintaining an average online accuracy of above 90%. A possible solution for real-time control of both hand/wrist gestures and force levels is the combined use of the LR algorithm with FE for the \"Hand/wrist gestures classifier\", and the NLR with FE for the Spherical and Tip force classifiers.

Surface electromyography (sEMG) signals represent a promising approach for decoding the motor intention of amputees to control a multifunctional prosthetic hand in a non-invasive way. Several approaches based on proportional amplitude methods or simple thresholds on sEMG signals have been proposed to control a single degree of freedom at time, without the possibility of increasing the number of controllable multiple DoFs in a natural manner. Myoelectric control based on PR techniques have been introduced to add multiple DoFs by keeping low the number of electrodes and allowing the discrimination of different muscular patterns for each class of motion. However, the use of PR algorithms to simultaneously decode both gestures and forces has never been studied deeply. This paper introduces a hierarchical classification approach with the aim to assess the desired hand/wrist gestures, as well as the desired force levels to exert during grasping tasks. A Finite State Machine was introduced to manage and coordinate three classifiers based on the Non-Linear Logistic Regression algorithm. The classification architecture was evaluated across 31 healthy subjects. The \"hand/wrist gestures classifier,\" introduced for the discrimination of seven hand/wrist gestures, presented a mean classification accuracy of 98.78%, while the \"Spherical and Tip force classifier,\" created for the identification of three force levels, reached an average accuracy of 98.80 and 96.09%, respectively. These results were confirmed by Linear Discriminant Analysis (LDA) with time domain features extraction, considered as ground truth for the final validation of the performed analysis. A Wilcoxon Signed-Rank test was carried out for the statistical analysis of comparison between NLR and LDA and statistical significance was considered at < 0.05. The comparative analysis reports not statistically significant differences in terms of F1Score performance between NLR and LDA. Thus, this study reveals that the use of non-linear classification algorithm, as NLR, is as much suitable as the benchmark LDA classifier for implementing an EMG pattern recognition system, able both to decode hand/wrist gestures and to associate different performed force levels to grasping actions.

Background In the last two decades, many studies based on omics technologies have contributed to defining the clinical, immunological, and histological fingerprints of chronic antibody-mediated rejection (CAMR), the leading cause of long-term kidney allograft failure. However, the full biological machinery underlying CAMR has only been partially defined, likely due to the fact thatsingle-omics technologies capture only specific aspects of the biological system and fail to provide a comprehensive understanding of this clinical complication. Methods This study integrated mass spectrometry-based proteomic profiling of serum samples from 19 patients with clinical and histological evidence of CAMR and 26 kidney transplant recipients with normal graft function and histology (CTR) with transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) from an independent cohort of 10 CAMR and 8 CTR patients. Data analysis was conducted using unsupervised hierarchical clustering (multidimensional scaling with k-means) and Spearman’s correlation test. Partial least squares discriminant analysis (PLS-DA) with the importance in projection (VIP) score identified key proteins differentiating CAMR from CTR. ELISA was used to validate the omics results. Results Proteomic analysis identified 18 proteins that significantly differentiated CAMR from CTR ( p  < 0.01): five were more abundant (CHI3L1, LYZ, PRSS2, CPQ, IGLV3-32), while 13 were less abundant (SERPINA5, SERPING1, KNG1, CAMP, VNN1, BTD, WDR1, PON3, AHNAK2, MELTF, CA1, CD44, CUL1). Transcriptomic profiling revealed 6 downregulated and 33 upregulated genes in CAMR versus CTR ( p  < 0.01). Notably, only 2 biological elements were significantly deregulated in both omics analyses: chitinase-3-like protein 1 (CHI3L1) and plasma protease inhibitor C1 (SERPING1). CHI3L1, previously associated with the severity of tissue damage in kidney diseases, was up-regulated in CAMR in both transcriptomics and proteomics, while SERPING1, a serine esterase inhibitor that blocks the classical and lectin pathway of complement, was up-regulated in CAMR in transcriptomics but down-regulated in proteomics. ELISA validated the omics results, and the ROC curve showed that CHI3L1 has good discrimination power between CAMR and CTR (AUC of ROC curve of 0.81). Conclusions Our multi-omics data, although performed in a relatively small cohort of patients, revealed new systemic biological elements involved in the pathogenesis of CAMR and identified CHI3L1 as a new potential biomarker and/or therapeutic target for this important clinical complication. Future validation of these findings in larger patient cohorts should be conducted to better evaluate their clinical utility.

Advances in kidney transplantation have made significant progress, yet challenges remain in managing both the pre- and post-transplantation phases, which have a direct impact on long-term allograft survival and comorbidities experienced by kidney transplant recipients (KTRs). Among the common immunosuppression-related complications, malignancies are a notable concern, and endocrine tumors are frequently observed. These tumors exhibit heterogeneous pathogenesis, prognosis, and treatment responses but existing literature is limited, and prevalence studies often compare KTRs to the general population. Thyroid cancers (particularly papillary thyroid cancer) have a high incidence in KTRs, whereas rare endocrine malignancies (such as neuroendocrine tumors, adrenal cortical carcinomas, pheochromocytomas, paragangliomas, and parathyroid carcinoma) are mostly reported in isolated case reports, and no clinical trials have been performed to assess the impact of different immunosuppressive treatments on their onset and development. However, current guidelines for the management of post-transplant malignancies suggest reducing or withdrawing immunosuppressive therapy whereas a switch from calcineurin inhibitors to mammalian target of rapamycin (mTOR) inhibitors is currently not recommended due to limited supporting data. Notably, the pathogenic role of transplantation and the timeline for endocrine malignancies onset in KTRs are poorly defined. To address these challenges, a multicenter and interdisciplinary approach is critical to improve our understanding of the epidemiology and pathogenesis of endocrine malignancies in KTRs. Additionally, specific guidelines for early diagnosis and treatment are necessary to ensure safe and effective management of these tumors in this vulnerable population. This mini-review aims to synthesize the available data and current insights into this important issue.

Egg quality is crucial to productivity and laying hens' health. However, hens' aging, oxidative stress, and metabolic disorders (e.g., liver steatosis) can impair egg production and quality during the production cycle. Nutritional interventions may help preserve productivity under these conditions. Among plant extracts, milk thistle ( L.) and artichoke ( L.) are noteworthy for their bioactive compounds with hepatoprotective and antioxidant properties. This study evaluated the effectiveness of a combined extract of milk thistle and artichoke (PHYTO-LAYER™), standardized in silibinin (2.4 g/L) and chlorogenic acid (2.2 g/L), in maintaining or improving egg quality, lipid oxidation, and antioxidant capacity in caged-laying hens exposed to hepatic and metabolic stress. A total of 792 Lohmann LSL-White hens (41 weeks old) were randomly assigned to two groups, control and treated (396 hens per group). The treated group received the products via drinking water at a dose of 1 mL/L, intermittently for 7 weeks (7 consecutive days every 2 weeks). Sampling occurred at five time points (T0-T5). At T0, T3, and T5, 60 eggs per group were collected for quality indices evaluation, while 13 hens per group were sampled for serum biochemical investigations. PHYTO-LAYER™ improved egg, yolk, and albumen weight (  < 0.000), eggshell thickness (  < 0.000), and the total polyphenol content (  < 0.026), with an enhancement of yolk antioxidant capacity (  < 0.024). However, the Haugh unit of treated eggs was reduced (  < 0.000). Egg quality often deteriorates during late production stages due to oxidative stress and hens' aging. Given the antioxidant potential of silibinin and chlorogenic acid, their combined intermittent administration supports and maintains the egg quality in caged-laying hens exposed to metabolic stress and after the peak of production. However, further studies could be of interest to verify whether similar changes in egg-quality indices are observed with other phytoextract administration protocols, such as continuous administration, and at different administered doses.

Cardiovascular (CV) disease is the leading cause of death in chronic kidney disease (CKD) and end-stage renal disease (ESRD). A key driver in this pathology is increased aortic stiffness, which is a strong, independent predictor of CV mortality in this population. Aortic stiffening is a potentially modifiable biomarker of CV dysfunction and in risk stratification for patients with CKD and ESRD. Previous work has suggested that therapeutic modification of aortic stiffness may ameliorate CV mortality. Nevertheless, future clinical implementation relies on the ability to accurately and reliably quantify stiffness in renal disease. Pulse wave velocity (PWV) is an indirect measure of stiffness and is the accepted standard for non-invasive assessment of aortic stiffness. It has typically been measured using techniques such as applanation tonometry, which is easy to use but hindered by issues such as the inability to visualize the aorta. Advances in cardiac magnetic resonance imaging now allow direct measurement of stiffness, using aortic distensibility, in addition to PWV. These techniques allow measurement of aortic stiffness locally and are obtainable as part of a comprehensive, multiparametric CV assessment. The evidence cannot yet provide a definitive answer regarding which technique or parameter can be considered superior. This review discusses the advantages and limitations of non-invasive methods that have been used to assess aortic stiffness, the key studies that have assessed aortic stiffness in patients with renal disease and why these tools should be standardized for use in clinical trial work.