Extracellular Vesicle-Associated Aβ Mediates Trans-Neuronal Bioenergetic and Ca\u3csup\u3e2+\u3c/sup\u3e-Handling Deficits in Alzheimer\u27s Disease Models

Alzheimer’s disease (AD) is an age-related neurodegenerative disorder in which aggregation-prone neurotoxic amyloid β-peptide (Aβ) accumulates in the brain. Extracellular vesicles (EVs), including exosomes, are small 50–150 nm membrane vesicles that have recently been implicated in the prion-like spread of self-aggregating proteins. Here we report that EVs isolated from AD patient cerebrospinal fluid and plasma, from the plasma of two AD mouse models, and from the medium of neural cells expressing familial AD presenilin 1 mutations, destabilize neuronal Ca2+ homeostasis, impair mitochondrial function, and sensitize neurons to excitotoxicity. EVs contain a relatively low amount of Aβ but have an increased Aβ42/ Aβ40 ratio; the majority of Aβ is located on the surface of the EVs. Impairment of lysosome function results in increased generation of EVs with elevated Aβ42 levels. EVs may mediate transcellular spread of pathogenic Aβ species that impair neuronal Ca2+ handling and mitochondrial function, and may thereby render neurons vulnerable to excitotoxicity

Characterization and outcomes of 414 patients with primary SS who developed haematological malignancies

Objective: To characterize 414 patients with primary SS who developed haematological malignancies and to analyse how the main SS- and lymphoma-related features can modify the presentation patterns and outcomes. Methods: By January 2021, the Big Data Sjögren Project Consortium database included 11 966 patients fulfilling the 2002/2016 classification criteria. Haematological malignancies diagnosed according to the World Health Organization (WHO) classification were retrospectively identified. Results: There were 414 patients (355 women, mean age 57 years) with haematological malignancies (in 43, malignancy preceded at least one year the SS diagnosis). A total of 376 (91%) patients had mature B-cell malignancy, nearly half had extranodal marginal zone lymphoma (MZL) of mucosa-associated lymphoid tissue (MALT lymphoma) (n = 197), followed by diffuse large B-cell lymphoma (DLBCL) (n = 67), nodal MZL lymphoma (n = 29), chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) (n = 19) and follicular lymphoma (FL) (n = 17). Rates of complete response, relapses and death were 80%, 34% and 13%, respectively, with a 5-year survival rate of 86.5% after a mean follow-up of 8 years. There were significant differences in age at diagnosis (younger in MALT, older in CLL/SLL), predominant clinical presentation (glandular enlargement in MALT lymphoma, peripheral lymphadenopathy in nodal MZL and FL, constitutional symptoms in DLBCL, incidental diagnosis in CLL/SLL), therapeutic response (higher in MALT lymphoma, lower in DLBCL) and survival (better in MALT, nodal MZL and FL, worse in DLBCL). Conclusion: In the largest reported study of haematological malignancies complicating primary SS, we confirm the overwhelming predominance of B-cell lymphomas, especially MALT, with the salivary glands being the primary site of involvement. This highly-specific histopathological scenario is linked with the overall good prognosis with a 5-year survival rate of nearly 90%

Dermal Patch with Integrated Flexible Heater for on Demand Drug Delivery

Topical administration of drugs and growth factors in a controlled fashion can improve the healing process during skin disorders and chronic wounds. To achieve this goal, a dermal patch is engineered that utilizes thermoresponsive drug microcarriers encapsulated within a hydrogel layer attached to a flexible heater with integrated electronic heater control circuitry. The engineered patch conformally covers the wound area and enables controlled drug delivery by electronically adjusting the temperature of the hydrogel layer. The drugs are encapsulated inside microparticles in order to control their release rates. These monodisperse thermoresponsive microparticles containing active molecules are fabricated using a microfluidic device. The system is used to release two different active molecules with molecular weights similar to drugs and growth factors and their release profiles are characterized. This platform is a key step towards engineering smart and closed loop systems for topical applications

Performance of an AI algorithm during the different phases of the COVID pandemics: what can we learn from the AI and vice versa

Background: Artificial intelligence (AI) has proved to be of great value in diagnosing and managing Sars-Cov-2 infection. ALFABETO (ALL-FAster-BEtter-TOgether) is a tool created to support healthcare professionals in the triage, mainly in optimizing hospital admissions. Methods: The AI was trained during the pandemic's “first wave” (February-April 2020). Our aim was to assess the performance during the “third wave” of the pandemics (February-April 2021) and evaluate its evolution. The neural network proposed behavior (hospitalization vs home care) was compared with what was actually done. If there were discrepancies between ALFABETO's predictions and clinicians' decisions, the disease's progression was monitored. Clinical course was defined as “favorable/mild” if patients could be managed at home or in spoke centers and “unfavorable/severe” if patients need to be managed in a hub center. Results: ALFABETO showed accuracy of 76%, AUROC of 83%; specificity was 78% and recall 74%. ALFABETO also showed high precision (88%). 81 hospitalized patients were incorrectly predicted to be in “home care” class. Among those “home-cared” by the AI and “hospitalized” by the clinicians, 3 out of 4 misclassified patients (76.5%) showed a favorable/mild clinical course. ALFABETO's performance matched the reports in literature. Conclusions: The discrepancies mostly occurred when the AI predicted patients could stay at home but clinicians hospitalized them; these cases could be handled in spoke centers rather than hubs, and the discrepancies may aid clinicians in patient selection. The interaction between AI and human experience has the potential to improve both AI performance and our comprehension of pandemic management

Flexible pH-Sensing Hydrogel Fibers for Epidermal Applications

Epidermal pH is an indication of the skin’s physiological condition. For example, pH of wound can be correlated to angiogenesis, protease activity, bacterial infection, etc. Chronic non-healing wounds are known to have an elevated alkaline environment, while healing process occurs more readily in an acidic environment. Thus, dermal patches capable of continuous monitoring of pH can be used as point-of-care systems for monitoring skin disorder and the wound healing process. Here, we present pH-responsive hydrogel fibers that can be used for long-term monitoring of epidermal wound condition. We load pH-responsive dyes into mesoporous microparticles and incorporate them into hydrogel fibers developed through microfluidic spinning. The fabricated pH-responsive microfibers are flexible and can create conformal contact with skin. The response of pH-sensitive fibers with different compositions and thicknesses are characterized. The suggested technique is scalable and can be used to fabricate hydrogel based wound dressing with a wide range of sizes. Images of the pH-sensing fibers during real-time pH measurement can be captured with a smart phone camera for convenient readout on-site. Through image processing, a quantitative pH map of the hydrogel fibers and the underlying tissue can be extracted. The developed skin dressing can act as a point-of-care device for monitoring the wound healing process