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Fictitious products have emerged as a substantial challenge in the manufacturing sector, inflicting adverse consequences on a company's financial health, reputation, and overall prosperity. Thankfully, blockchain technology provides an effective remedy for discerning fake items and verifying the legitimacy of authentic ones, all within a decentralized and widely distributed digital ledger. Quick Response (QR) codes serve as pivotal tools in the fight against fictitious goods, as each product is now furnished with a QR code that acts as a direct bridge to the blockchain system, essentially bestowing each item with a digital identification card. QR code scanners communicate with the blockchain to promptly validate whether a product is genuine or fictitious. Moreover, customers can harness this technology to track a product's journey through the supply chain and authenticate ownership details, akin to a digital breadcrumb trail that securely preserves product information and unique codes as database blocks. Considering the globalized business landscape and the perpetual advancements in technology, industrial manufacturers and distributors are wholeheartedly committed to optimizing their supply chain processes, ensuring they remain one step ahead of fictitious product proliferation and fortifying their operations against the dissemination of spurious items. In this paper we compare Different Fake product identification Methods like Barcodes, QR Codes, RFID Tags, Serial numbers with methods using Blockchain technology which is having high security, transferability & traceability throughout Supply Chain.

Human behavior is notoriously difficult to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring about long-term behavioral changes. During the pandemic, people have been forced to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. A critical question going forward is how these experiences have actually changed preferences and habits in ways that might persist after the pandemic ends. Many observers have suggested theories about what the future will bring, but concrete evidence has been lacking. We present evidence on how much US adults expect their own post-pandemic choices to differ from their prepandemic lifestyles in the areas of telecommuting, restaurant patronage, air travel, online shopping, transit use, car commuting, uptake of walking and biking, and home location. The analysis is based on a nationally representative survey dataset collected between July and October 2020. Key findings include that the “new normal” will feature a doubling of telecommuting, reduced air travel, and improved quality of life for some.

Regenerative agriculture is pivotal for mitigating climate change, with no-tillage practices on cropland being generally effective at raising soil organic carbon (SOC). Yet, our understanding of the compound impact of soil and environmental factors on SOC gain potential after transitioning to no-till practices is still developing. Using imbalanced machine learning classification, here we quantify key thresholds to hierarchically classify SOC gain potential by switching from conventional tillage to long-term no-tillage with residue retention. Our findings reveal that antecedent SOC level exerts the primary influence, with a reduced gain potential for antecedent SOC exceeding 50 tonnes per hectare. Wet climate (Dryness Index < 1.5) and low productivity (net annual primary productivity < 5.5 tonnes per hectare) could further lessen the effectiveness of SOC sequestration. These key thresholds identify vast areas across Africa, Australia, South Asia, Southern Europe, and parts of North and South America as high-potential croplands for carbon sequestration and offer guidelines for assessing the reliability of regenerative agriculture in local and regional contexts.

Materials at the nanoscale exhibit specific physicochemical interactions with their environment. Therefore, evaluating their toxic potential is a primary requirement for regulatory purposes and for the safer development of nanomedicines. In this review, to aid the understanding of nano–bio interactions from environmental and health and safety perspectives, the potential, reality, challenges, and future advances that artificial intelligence (AI) and machine learning (ML) present are described. Herein, AI and ML algorithms that assist in the reporting of the minimum information required for biomaterial characterization and aid in the development and establishment of standard operating procedures are focused. ML tools and ab initio simulations adopted to improve the reproducibility of data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution to safe‐by‐design development in nanotoxicology/nanomedicine are mainly focused. In addition, future opportunities and challenges in the application of ML in nanoinformatics, which is particularly well‐suited for the clinical translation of nanotherapeutics, are highlighted. This comprehensive review is believed that it will promote an unprecedented involvement of AI research in improvements in the field of nanotoxicology and nanomedicine. Machine learning (ML) tools in computational nanotoxicology are adopted to improve the reproducibility of the data for robust quantitative comparisons and to facilitate in silico modeling and meta‐analyses leading to a substantial contribution in nanotoxicology/nanomedicine. Herein, the potential, reality, challenges, and future advances that artificial intelligence (AI) and ML present in advanced material design and toxicity predictions are described.

Background Alzheimer’s disease (AD) is a neurodegenerative disease characterized by Aβ plaques and neurofibrillary tangles. Chronic inflammation and synaptic dysfunction lead to disease progression and cognitive decline. Small extracellular vesicles (sEVs) are implicated in AD progression by facilitating the spread of pathological proteins and inflammatory cytokines. This study investigates synaptic dysfunction and neuroinflammation protein markers in plasma-derived sEVs (PsEVs), their association with Amyloid-β and tau pathologies, and their correlation with AD progression. Methods A total of 90 [AD = 35, mild cognitive impairment (MCI) = 25, and healthy age-matched controls (AMC) = 30] participants were recruited. PsEVs were isolated using a chemical precipitation method, and their morphology was characterized by transmission electron microscopy. Using nanoparticle tracking analysis, the size and concentration of PsEVs were determined. Antibody-based validation of PsEVs was done using CD63, CD81, TSG101, and L1CAM antibodies. Synaptic dysfunction and neuroinflammation were evaluated with synaptophysin, TNF-α, IL-1β, and GFAP antibodies. AD-specific markers, amyloid-β (1–42), and p-Tau were examined within PsEVs using Western blot and ELISA. Results Our findings reveal higher concentrations of PsEVs in AD and MCI compared to AMC ( p  < 0.0001). Amyloid-β (1–42) expression within PsEVs is significantly elevated in MCI and AD compared to AMC. We could also differentiate between the amyloid-β (1–42) expression in AD and MCI. Similarly, PsEVs-derived p-Tau exhibited elevated expression in MCI compared with AMC, which is further increased in AD. Synaptophysin exhibited downregulated expression in PsEVs from MCI to AD ( p  = 0.047) compared to AMC, whereas IL-1β, TNF-α, and GFAP showed increased expression in MCI and AD compared to AMC. The correlation between the neuropsychological tests and PsEVs-derived proteins (which included markers for synaptic integrity, neuroinflammation, and disease pathology) was also performed in our study. The increased number of PsEVs correlates with disease pathological markers, synaptic dysfunction, and neuroinflammation. Conclusions Elevated PsEVs, upregulated amyloid-β (1–42), and p-Tau expression show high diagnostic accuracy in AD. The downregulated synaptophysin expression and upregulated neuroinflammatory markers in AD and MCI patients suggest potential synaptic degeneration and neuroinflammation. These findings support the potential of PsEV-associated biomarkers for AD diagnosis and highlight synaptic dysfunction and neuroinflammation in disease progression.

Background Parkinson’s disease is generally asymptomatic at earlier stages. At an early stage, there is an extensive progression in the neuropathological hallmarks, although, at this stage, diagnosis is not possible with currently available diagnostic methods. Therefore, the pressing need is for susceptibility risk biomarkers that can aid in better diagnosis and therapeutics as well can objectively serve to measure the endpoint of disease progression. The role of small extracellular vesicles (sEV) in the progression of neurodegenerative diseases could be potent in playing a revolutionary role in biomarker discovery. Methods In our study, the salivary sEV were efficiently isolated by chemical precipitation combined with ultrafiltration from subjects (PD = 70, healthy controls = 26, and prodromal PD = 08), followed by antibody-based validation with CD63, CD9, GAPDH, Flotillin-1, and L1CAM. Morphological characterization of the isolated sEV through transmission electron microscopy. The quantification of sEV was achieved by fluorescence (lipid-binding dye-labeled) nanoparticle tracking analysis and antibody-based (CD63 Alexa fluor 488 tagged sEV) nanoparticle tracking analysis. The total alpha-synuclein (α-syn Total ) in salivary sEVs cargo was quantified by ELISA. The disease severity staging confirmation for n  = 18 clinically diagnosed Parkinson’s disease patients was done by 99m Tc-TRODAT-single-photon emission computed tomography. Results We observed a significant increase in total sEVs concentration in PD patients than in the healthy control (HC), where fluorescence lipid-binding dye-tagged sEV were observed to be higher in PD ( p  = 0.0001) than in the HC using NTA with a sensitivity of 94.34%. In the prodromal PD cases, the fluorescence lipid-binding dye-tagged sEV concentration was found to be higher ( p  = 0.008) than in HC. This result was validated through anti-CD63 tagged sEV ( p  = 0.0006) with similar sensitivity of 94.12%. We further validated our findings with the ELISA based on α-syn Total concentration in sEV, where it was observed to be higher in PD ( p  = 0.004) with a sensitivity of 88.24%. The caudate binding ratios in 99m Tc-TRODAT-SPECT represent a positive correlation with sEV concentration ( r  = 0.8117 with p  = 0.0112). Conclusions In this study, for the first time, we have found that the fluorescence-tagged sEV has the potential to screen the progression of disease with clinically acceptable sensitivity and can be a potent early detection method for PD. Graphical Abstract

Obsessive–compulsive (OC) disorder (OCD) is a common and potentially disabling illness with a waxing and waning course. OCD significantly disrupts the quality of life. Selective serotonin reuptake inhibitors (SSRIs) are first‐line pharmacological treatments for OCD and benefit up to half of the patients. Augmentation with low‐dose antipsychotics is an evidence‐based second‐line strategy. Psychotherapy, including cognitive behavior therapy (CBT), is used both as first and second‐line treatment. A significant portion of patients, however, do not respond to conventional treatments. We present a case series on the use of Endoxifen as an augmenting agent in patients with OCD and multiple psychiatric comorbidities who did not respond well to conventional pharmacotherapy.

Modern innovations, like machine learning, genomics, and digital health, are being integrated into medical practice at a rapid pace. Physicians in training receive little exposure to the implications, drawbacks, and methodologies of upcoming technologies prior to their deployment. As a result, there is an increasing need for the incorporation of innovation and technology (I&T) training, starting in medical school. We aimed to identify and describe curricular and extracurricular opportunities for innovation in medical technology in US undergraduate medical education to highlight challenges and develop insights for future directions of program development. A review of publicly available I&T program information on the official websites of US allopathic medical schools was conducted in June 2020. Programs were categorized by structure and implementation. The geographic distribution of these categories across US regions was analyzed. A survey was administered to school-affiliated student organizations with a focus on I&T and publicly available contact information. The data collected included the founding year, thematic focus, target audience, activities offered, and participant turnout rate. A total of 103 I&T opportunities at 69 distinct Liaison Committee on Medical Education-accredited medical schools were identified and characterized into the following six categories: (1) integrative 4-year curricula, (2) facilitated doctor of medicine/master of science dual degree programs in a related field, (3) interdisciplinary collaborations, (4) areas of concentration, (5) preclinical electives, and (6) student-run clubs. The presence of interdisciplinary collaboration is significantly associated with the presence of student-led initiatives (P=.001). \"Starting and running a business in healthcare\" and \"medical devices\" were the most popular thematic focuses of student-led I&T groups, representing 87% (13/15) and 80% (12/15) of respondents, respectively. \"Career pathways exploration for students\" was the only type of activity that was significantly associated with a high event turnout rate of >26 students per event (P=.03). Existing school-led and student-driven opportunities in medical I&T indicate growing national interest and reflect challenges in implementation. The greater visibility of opportunities, collaboration among schools, and development of a centralized network can be considered to better prepare students for the changing landscape of medical practice.

Small extracellular vesicles (sEVs) can provide information about the pathophysiology of the cells; therefore, sEVs have attracted considerable interest as possible diagnostic biomarkers. A key challenge lies in the necessity for simple and cost-effective sEV isolation methods to achieve high purity and yield suitable for research and clinical applications. We are introducing a comprehensive study on isolating sEVs using a novel cocktail strategy that integrates chemical precipitation and ultrafiltration with a two-step filtering process to ensure a highly pure and homogeneous population and further compared with PEG-based precipitation, ultra-centrifugation, and size-exclusion-chromatography columns. The isolated sEVs from each protocol are quantified for size and yield using nanoparticle tracking analysis, morphologically characterized through transmission electron microscopy, and validated by quantifying the expression profiles of sEV surface biomarkers. Furthermore, the study explores the applicability of our method for downstream multi-omics analyses. The results highlight the efficacy of the proposed protocol, demonstrating the ease and efficiency of isolating sEVs from different biofluids with minimal laboratory requirements and confirming the compatibility with multi-omics analyses. These findings position our method as particularly valuable for translational research, offering a promising avenue for advancing the study and application of sEVs in diagnostic and therapeutic research.

Background: Parkinson’s disease (PD) is an increasingly common neurodegenerative condition, which causes movement dysfunction and a broad range of nonmotor symptoms. There is no molecular or biochemical diagnosis test for PD. The miRNAs are a class of small non-coding RNAs and are extensively studied owing to their altered expression in pathological states and facile harvesting and analysis techniques. Methods: A total of 48 samples (16 each of PD, aged-matched and young controls) were recruited. The small extracellular vesicles (sEV) were isolated and validated using western blot, transmission electron microscope, and nanoparticle tracking analysis. Small RNA isolation, library preparation, and small RNA sequencing followed by differential expression and targeted prediction of miRNA were performed. The real-time PCR was performed with the targeted miRNA on PD, aged-matched, and young healthy control of plasma and plasma-derived sEV to demonstrate their potential as a diagnostic biomarker. Results: In RNA sequencing, we identified 14.89% up-regulated (fold change 1.11 to 11.04, p<0.05) and 16.54% downregulated (fold change -1.04 to -7.28, p<0.05) miRNAs in PD and controls. Four differentially expressed miRNAs (miR-23b-3p, miR-29a-3p, miR-19b-3p, and miR-150-3p) were selected. The expression of miR-23b-3p was “upregulated” (p=0.002) in plasma whereas “downregulated” (p=0.0284) in plasma-derived sEVs in PD than age-matched controls. The ROC analysis of miR-23b-3p revealed better AUC values in plasma (AUC= 0.8086, p=0.0029) and plasma-derived sEVs (AUC= 0.7278, p= 0.0483) of PD and age-matched controls. Conclusion: We observed an opposite expression profile of miR-23b-3p in PD and aged-matched healthy control in plasma and plasma-derived sEV fractions, where the expression of miR-23b-3p is increased in PD plasma while decreased in plasma-derived sEVs fraction. We further observed the different miR-23b-3p expression profiles in the young and aged-matched healthy control.