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The extraction of bioactive compounds from food by-products is one of the most important research areas for the nutraceutical, pharmaceutical, and food industries. This research aimed to evaluate the efficiency of Ultrasound-Assisted Extraction (UAE) and Microwave-Assisted Extraction (MAE), either alone or in combination, of phenolic compounds from cocoa bean shells (CBSs). These extraction techniques were compared with conventional methods, such as under simple magnetic stirring and the Soxhlet apparatus. After the preliminary characterization of the gross composition of CBSs, the total polyphenol content and radical scavenging of extracts obtained from both raw and defatted cocoa bean shells were investigated. Quantification of the main polyphenolic compounds was then performed by RP-HPLC-DAD, identifying flavonoids and phenolic acids, as well as clovamide. The application of MAE and UAE resulted in a similar or superior extraction of polyphenols when compared with traditional methods; the concentration of individual polyphenols was variously influenced by the extraction methods employed. Combining MAE and UAE at 90 °C yielded the highest antiradical activity of the extract. Spectrophotometric analysis confirmed the presence of high-molecular-weight melanoidins, which were present in higher concentrations in the extracts obtained using MAE and UAE, especially starting from raw material. In conclusion, these results emphasize the efficiency of MAE and UAE techniques in obtaining polyphenol-rich extracts from CBS and confirm this cocoa by-product as a valuable biomass for the recovery of antioxidant compounds, with a view to possible industrial scale-up.

Upon a diagnosis, the clinical team faces two main questions: what treatment, and at what dose? Clinical trials' results provide the basis for guidance and support for official protocols that clinicians use to base their decisions. However, individuals do not consistently demonstrate the reported response from relevant clinical trials. The decision complexity increases with combination treatments where drugs administered together can interact with each other, which is often the case. Additionally, the individual's response to the treatment varies with the changes in their condition. In practice, the drug and the dose selection depend significantly on the medical protocol and the medical team's experience. As such, the results are inherently varied and often suboptimal. Big data and Artificial Intelligence (AI) approaches have emerged as excellent decision-making tools, but multiple challenges limit their application. AI is a rapidly evolving and dynamic field with the potential to revolutionize various aspects of human life. AI has become increasingly crucial in drug discovery and development. AI enhances decision-making across different disciplines, such as medicinal chemistry, molecular and cell biology, pharmacology, pathology, and clinical practice. In addition to these, AI contributes to patient population selection and stratification. The need for AI in healthcare is evident as it aids in enhancing data accuracy and ensuring the quality care necessary for effective patient treatment. AI is pivotal in improving success rates in clinical practice. The increasing significance of AI in drug discovery, development, and clinical trials is underscored by many scientific publications. Despite the numerous advantages of AI, such as enhancing and advancing Precision Medicine (PM) and remote patient monitoring, unlocking its full potential in healthcare requires addressing fundamental concerns. These concerns include data quality, the lack of well-annotated large datasets, data privacy and safety issues, biases in AI algorithms, legal and ethical challenges, and obstacles related to cost and implementation. Nevertheless, integrating AI in clinical medicine will improve diagnostic accuracy and treatment outcomes, contribute to more efficient healthcare delivery, reduce costs, and facilitate better patient experiences, making healthcare more sustainable. This article reviews AI applications in drug development and clinical practice, making healthcare more sustainable, and highlights concerns and limitations in applying AI., (© 2024. The Author(s).)

Vinegar is a versatile product used for food preservation, cooking, healthcare, and cleaning. In this study, 80 vinegar of different raw materials, aging time, and for the first time by the agronomic method of raw material cultivation were classified by spectralprint techniques with chemometrics. Datasets were obtained by proton nuclear magnetic resonance ( 1 H NMR), Fourier transforms mid-infrared (FT-IR), near-infrared (NIR), and ultraviolet-visible (UV-vis); then evaluated by common dimension (ComDim) and partial least squares-discriminant analysis (PLS-DA). NMR with PLS-DA had the best prediction performance compared to other techniques, with accuracy values between 92.3 and 100 %, followed by FT-IR and UV-vis of 80.8 and 96.0 % and NIR between 69.2 and 84.0 %. The results indicated that the classification of vinegar according to the agronomic cultivation method is more complex than aging time or raw material. However, any of these spectralprint techniques have demonstrated that they can be used in the classification of vinegar., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Despite the recent successes and durable responses with immune checkpoint inhibitors (ICI), many cancer patients, including those with melanoma, do not derive long-term benefits from ICI therapies. The lack of predictive biomarkers to stratify patients to targeted treatments has been the driver of primary treatment failure and represents an unmet medical need in melanoma and other cancers. Understanding genomic correlations with response and resistance to ICI will enhance cancer patients' benefits. Building on insights into interplay with the complex tumor microenvironment (TME), the ultimate goal should be assessing how the tumor 'instructs' the local immune system to create its privileged niche with a focus on genomic reprogramming within the TME. It is hypothesized that this genomic reprogramming determines the response to ICI. Furthermore, emerging genomic signatures of ICI response, including those related to neoantigens, antigen presentation, DNA repair, and oncogenic pathways, are gaining momentum. In addition, emerging data suggest a role for checkpoint regulators, T cell functionality, chromatin modifiers, and copy-number alterations in mediating the selective response to ICI. As such, efforts to contextualize genomic correlations with response into a more insightful understanding of tumor immune biology will help the development of novel biomarkers and therapeutic strategies to overcome ICI resistance.

The concern about the offspring's health is one of the reasons for a reduced family size of women with rheumatic diseases (RD). Increased risk of autoimmune diseases (AD) and neurodevelopmental disorders (ND) has been reported in children born to patients with RD. Within a nationwide survey about reproductive issues of women with RD, we aimed at exploring the long-term outcome of their children. By surveying 398 patients who received their diagnosis of RD during childbearing age (before the age of 45), information about the offspring were obtained from 230 women who declared to have had children. A total of 148 (64.3%) patients were affected by connective tissue diseases (CTD) and 82 (35.7%) by chronic arthritis. Data on 299 children (156 males, 52.1%; mean age at the time of interview 17.1 ± 9.7 years) were collected. Twelve children (4.0%), who were born to patients with CTD in 75% of the cases, were affected by AD (8 cases of celiac disease). Eleven children had a certified diagnosis of ND (3.6%; 6 cases of learning disabilities); 9 of them were born to mothers with CTD (5 after maternal diagnosis). No association was found between ND and prenatal exposure to either maternal autoantibodies or anti-rheumatic drugs. Absolute numbers of offspring affected by AD and ND were low in a multicentre cohort of Italian women with RD. This information can be helpful for the counselling about reproductive issues, as the health outcomes of the offspring might not be an issue which discourage women with RD from having children., (© 2021. The Author(s).)

Objective: The reproductive choices of women affected by rheumatic diseases (RD) can be influenced by several factors, including the quality of physician-patient communication. We conducted a survey on reproductive issues aiming at exploring the unmet needs of women with RD during childbearing age., Methods: We administered 65 multiple-choice and 12 open-answer questions about pregnancy counselling, contraception, use of drugs during pregnancy and other women reproductive issues to 477 consecutive women with RD aged 18-55 years followed-up in 24 rheumatology centres in Italy. Analysis was restricted to 398 patients who received their diagnosis of RD before the age of 45. According to the RD diagnosis, patients were subdivided into 2 groups: connective tissue diseases (n = 249) and chronic arthritis (n = 149)., Results: At the time of interview, women in both groups had a mean age of 40 years. Nearly one third of patients in each group declared not to have received any counselling about either pregnancy desire nor contraception. A smaller family size than desired was reported by nearly 37% of patients, because of concerns related to maternal disease in one fourth of the cases. A "Disease Knowledge Index" (DKI) was created to investigate the degree of patients' information about the implications of their RD on reproductive issues. Having received counselling was associated with higher DKI values and with a positive impact on family planning., Conclusion: Italian women of childbearing age affected by RD reported several unmet needs in their knowledge about reproductive issues. Strategies are needed to implement and facilitate physician-patient communication., (Copyright © 2018. Published by Elsevier Masson SAS.)

Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our understanding of disease pathogenesis and changed the way we diagnose and treat disease leading to more precise, predictable and powerful health care that is customized for the individual patient. Genetic, genomics, and epigenetic alterations appear to be contributing to different diseases. Deep clinical phenotyping, combined with advanced molecular phenotypic profiling, enables the construction of causal network models in which a genomic region is proposed to influence the levels of transcripts, proteins, and metabolites. Phenotypic analysis bears great importance to elucidat the pathophysiology of networks at the molecular and cellular level. Digital biomarkers (BMs) can have several applications beyond clinical trials in diagnostics-to identify patients affected by a disease or to guide treatment. Digital BMs present a big opportunity to measure clinical endpoints in a remote, objective and unbiased manner. However, the use of "omics" technologies and large sample sizes have generated massive amounts of data sets, and their analyses have become a major bottleneck requiring sophisticated computational and statistical methods. With the wealth of information for different diseases and its link to intrinsic biology, the challenge is now to turn the multi-parametric taxonomic classification of a disease into better clinical decision-making by more precisely defining a disease. As a result, the big data revolution has provided an opportunity to apply artificial intelligence (AI) and machine learning algorithms to this vast data set. The advancements in digital health opportunities have also arisen numerous questions and concerns on the future of healthcare practices in particular with what regards the reliability of AI diagnostic tools, the impact on clinical practice and vulnerability of algorithms. AI, machine learning algorithms, computational biology, and digital BMs will offer an opportunity to translate new data into actionable information thus, allowing earlier diagnosis and precise treatment options. A better understanding and cohesiveness of the different components of the knowledge network is a must to fully exploit the potential of it.

Background: There is a pressing need in rheumatoid arthritis (RA) to identify patients who will not respond to first-line disease-modifying anti-rheumatic drugs (DMARD). We explored whether differences in genomic architecture represented by a chromosome conformation signature (CCS) in blood taken from early RA patients before methotrexate (MTX) treatment could assist in identifying non-response to DMARD and, whether there is an association between such a signature and RA specific expression quantitative trait loci (eQTL)., Methods: We looked for the presence of a CCS in blood from early RA patients commencing MTX using chromosome conformation capture by EpiSwitch™. Using blood samples from MTX responders, non-responders and healthy controls, a custom designed biomarker discovery array was refined to a 5-marker CCS that could discriminate between responders and non-responders to MTX. We cross-validated the predictive power of the CCS by generating 150 randomized groups of 59 early RA patients (30 responders and 29 non-responders) before MTX treatment. The CCS was validated using a blinded, independent cohort of 19 early RA patients (9 responders and 10 non-responders). Last, the loci of the CCS markers were mapped to RA-specific eQTL., Results: We identified a 5-marker CCS that could identify, at baseline, responders and non-responders to MTX. The CCS consisted of binary chromosome conformations in the genomic regions of IFNAR1, IL-21R, IL-23, CXCL13 and IL-17A. When tested on a cohort of 59 RA patients, the CCS provided a negative predictive value of 90.0% for MTX response. When tested on a blinded independent validation cohort of 19 early RA patients, the signature demonstrated a true negative response rate of 86 and a 90% sensitivity for detection of non-responders to MTX. Only conformations in responders mapped to RA-specific eQTL., Conclusions: Here we demonstrate that detection of a CCS in blood in early RA is able to predict inadequate response to MTX with a high degree of accuracy. Our results provide a proof of principle that a priori stratification of response to MTX is possible, offering a mechanism to provide alternative treatments for non-responders to MTX earlier in the course of the disease.

Collaboration can be challenging; nevertheless, the emerging successes of large, multi-partner, multi-national cooperatives and research networks in the biomedical sector have sustained the appetite of academics and industry partners for developing and fostering new research consortia. This model has percolated down to national funding agencies across the globe, leading to funding for projects that aim to realise the true potential of genomic medicine in the 21st century and to reap the rewards of 'big data'. In this Perspectives article, the experiences of the RA-MAP consortium, a group of more than 140 individuals affiliated with 21 academic and industry organizations that are focused on making genomic medicine in rheumatoid arthritis a reality are described. The challenges of multi-partner collaboration in the UK are highlighted and wide-ranging solutions are offered that might benefit large research consortia around the world.

Biopharmaceuticals have the potential to raise an immunogenic response in treated individuals, which may impact the efficacy and safety profile of these drugs. As a result, it is essential to evaluate immunogenicity throughout the different phases of the clinical development of a biopharmaceutical, including post-marketing surveillance. Although rigorous evaluation of biopharmaceutical immunogenicity is required by regulatory authorities, there is a lack of uniform standards for the type, quantity, and quality of evidence, and for guidance on experimental design for immunogenicity assays or criteria to compare immunogenicity of biopharmaceuticals. Moreover, substantial technological advances in methods to assess immune responses have yielded higher immunogenicity rates with modern assays, and limit comparison of immunogenicity of biopharmaceuticals outside of head-to-head clinical trials. Accordingly, research programs, regulatory agencies, and clinicians need to keep pace with continuously evolving analyses of immunogenicity. Here, we review factors associated with immunogenicity of biopharmaceuticals, potential clinical ramifications, and current regulatory guidance for evaluating immunogenicity, and discuss methods to assess immunogenicity in non-clinical and clinical studies. We also describe special considerations for evaluating the immunogenicity of biosimilar candidates.

Relapsed/refractory diffuse large B-cell lymphoma (DLBCL) is associated with a poor prognosis. Outcomes are particularly poor following immunochemotherapy failure or relapse within 12 months of induction. We conducted a Phase I/II trial of lenalidomide plus RICE (rituximab, ifosfamide, carboplatin, and etoposide) (RICER) as a salvage regimen for first-relapse or primary refractory DLBCL. Dose-escalated lenalidomide was combined with RICE every 14 d. After three cycles of RICER, patients with chemosensitive disease underwent stem cell collection and consolidation with BEAM [BCNU (carmustine), etoposide, cytarabine, melphalan] followed by autologous stem cell transplantation (autoSCT). Patients who recovered from autoSCT toxicities within 90 d initiated maintenance treatment with lenalidomide 25 mg daily for 21 d every 28 d for 12 months. No dose-limiting or unexpected toxicities occurred with lenalidomide 25 mg plus RICE. Grade 3/4 haematological toxicities resolved appropriately, and planned dose density and dose intensity of RICER were preserved. No lenalidomide or RICE dose reductions were required in any of the three cycles. After two cycles of RICER, nine of 15 patients (60%) achieved a complete response, and two achieved a partial response (13%). Combining lenalidomide with RICE is feasible, and results in promising response rates (particularly complete response rates) in high-risk DLBCL patients., (© 2014 The Authors. British Journal of Haematology published by John Wiley & Sons Ltd.)

We report a case of a 54-year-old female with progressive chronic pain in lower extremity, paraplegia and loss of function of the anal sphincter. MRI revealed an expansive solid intradural and intramedullar lesion located at the T6 to T9 levels. Histology of the lesion showed melanocytes with the results of immunohistochemistry consistent with a melanocytoma. Melanocytic tumors are rare tumors which present a diagnostic and management challenge for the modern neurosurgeon and neuro-oncologist since MRI and CT features are non-specific and there is scant data to standardise best strategic therapy.

We report on a 4-year-old boy with mental retardation, facial and skeletal anomalies, cerebral angiomas, femoral nucleus necrosis, mild biochemical abnormalities. This complex of features resembles the Hall-Riggs syndrome but could represent a novel syndrome.

The importance of biomarker technology and biomarker strategies in pharmaceutical development is still in its infancy, but the impact of biomarkers is already proving to be significant in this field. Strategies for incorporating biomarkers form the basis for translational medicine and also for the industry/regulatory focus on reducing the high attrition rate of drugs often encountered at phase II clinical research. The depth and breadth of knowledge required to successfully implement biomarkers into drug development are generating many collaborative efforts within the pharmaceutical industry, as well as encouraging the involvement of professionals who traditionally have not been part of the drug-development process.

Vav is one of the genetic markers that correlate with the differentiation of hematopoietic cells. In T and B cells, it appears crucial for both development and functions, while, in non-lymphoid hematopoietic cells, Vav seems not involved in cell maturation, but rather in the response of mature cells to agonist-dependent proliferation and phagocytosis. We have previously demonstrated that the amount and the tyrosine phosphorylation of Vav are up-regulated in both whole cells and nuclei of tumoral promyelocytes induced to granulocytic maturation by ATRA and that tyrosine-phosphorylated Vav does not display any ATRA-induced GEF activity but contributes to the regulation of PI 3-K activity. In this study, we report that Vav accumulates in nuclei of ATRA-treated APL-derived cells and that the down-modulation of Vav prevents differentiation of tumoral promyelocytes, indicating that it is a key molecule in ATRA-dependent myeloid maturation. On the other hand, the overexpression of Vav induces an increased expression of surface markers of granulocytic differentiation without affecting the maturation-related changes of the nuclear morphology. Consistent with an effect of Vav on the transcriptional machinery, array profiling shows that the inhibition of the Syk-dependent tyrosine phosphorylation of Vav reduces the number of ATRA-induced genes. Our data support the unprecedented notion that Vav plays crucial functions in the maturation process of myeloid cells, and suggest that Vav can be regarded as a potential target for the therapeutic treatment of myeloproliferative disorders.

The importance of the Vav family of signal transduction molecules in hematopoietic cells has long been acknowledged, even though its role and its regulatory mechanism are not completely understood. We have previously demonstrated that tyrosine-phosphorylated Vav, also located inside the nucleus of myeloid cells, is up-regulated during maturation of promyelocytic precursors induced by all-trans-retinoic acid (ATRA). Here, we report that the tyrosine phosphorylation of Vav during granulocytic maturation is dependent on the tyrosine kinase Syk and is essential for the morphological changes of the cell nucleus. These ATRA-induced events are independent on the guanine nucleotide exchange activity of Vav. We also found that, in differentiating cells, and in both cytoplasmic and nuclear compartments, tyrosine phosphorylated Vav associates with the regulatory subunit of phosphoinositide 3-kinase (PI 3-K). The Vav/p85 interaction is essential for the ATRA-induced PI 3-K activity and for association of PI 3-K with actin, particularly in the nucleus. Our data indicate an unprecedented crucial function for Vav in modulating the morphological maturation process of myeloid cells in a GDP-GTP exchange factor (GEF)-independent manner and suggest a role of Vav as an adaptor protein responsible of targeting PI 3-K to its intranuclear substrates.

We studied the role of CD43 (leukosialin/sialophorin), the negatively charged sialoglycoprotein of leukocytes, in the binding of mycobacteria to host cells. CD43-transfected HeLa cells bound Mycobacterium avium, but not Salmonella typhimurium or Shigella flexneri. Quantitative bacteriology showed that macrophages (M(phi)) from wild-type mice (CD43(+/+)) bound M. avium, Mycobacterium bovis (bacillus Calmette-Guérin), and Mycobacterium tuberculosis (strain H37Rv), whereas M(phi) from CD43 knockout mice (CD43(-/)-) did not. Fluorescence microscopy demonstrated that the associated M. avium had been ingested by the CD43(+/+) M(phi). The inability of CD43(-/)- M(phi) to bind M. avium could be restored by addition of galactoglycoprotein (Galgp), the extracellular mucin portion of CD43. The effect of Galgp is not due to opsonization of the bacteria, but required its interaction with the M(phi) other mucins had no effect. CD43 expression by the M(phi) was also required for optimal induction by M. avium of tumor necrosis factor (TNF)-alpha production, which likewise could be reconstituted by Galgp. In contrast, interleukin (IL)-10 production by M. avium-infected M(phi) was CD43 independent, demonstrating discordant regulation of TNF-alpha and IL-10. These findings describe a novel role of CD43 in promoting stable interaction of mycobacteria with receptors on the M(phi) enabling the cells to respond specifically with TNF-alpha production.

Mycobacterial diseases are a major public health concern. In the case of tuberculosis, the problem has been acerbated due to the emergence of drug-resistant strains of Mycobacterium tuberculosis, and Mycobacterium avium is the major opportunistic pathogen in HIV-1 infection in the United States. M. tuberculosis and M. avium replicate in human macrophages and induce apoptosis. Incubation of freshly added uninfected autologous macrophages with apoptotic M. avium-infected macrophages results in 90% inhibition of bacterial growth. Apoptosis also prevents the release of intracellular components and the spread of mycobacterial infection by sequestering the pathogens within apoptotic bodies. Consistent with the model that host cell apoptosis is a defense mechanism against mycobacteria is the finding that the virulent M. tuberculosis strain H37Rv induces substantially less macrophage apoptosis than the attenuated strain H37Ra. Evasion of apoptosis by this pathogen is achieved by enhanced release of sTNFR2 by H37Rv-infected macrophages and subsequent formation of inactive TNF-alpha-TNFR2 complexes. These observations contribute to the hypothesis that apoptosis of the host macrophage is an important defense mechanism in mycobacterial infections, which prevents the spread of the infection.