Reversible Covalent Inhibition─Desired Covalent Adduct Formation by Mass Action.

Covalent inhibition has seen a resurgence in the last several years. Although long-plagued by concerns of off-target effects due to nonspecific reactions leading to covalent adducts, there has been success in developing covalent inhibitors, especially within the field of anticancer therapy. Covalent inhibitors can have an advantage over noncovalent inhibitors since the formation of a covalent adduct may serve as an additional mode of selectivity due to the intrinsic reactivity of the target protein that is absent in many other proteins. Unfortunately, many covalent inhibitors form irreversible adducts with off-target proteins, which can lead to considerable side-effects. By designing the inhibitor to form reversible covalent adducts, one can leverage competing on/off kinetics in complex formation by taking advantage of the law of mass action. Although covalent adducts do form with off-target proteins, the reversible nature of inhibition prevents accumulation of the off-target adduct, thus limiting side-effects. In this perspective, we outline important characteristics of reversible covalent inhibitors, including examples and a guide for inhibitor development.

Laryngeal Embryonal Rhabdomyosarcoma: A rare adult neoplasm.

The embryonal subtype of rhabdomyosarcoma is the most frequent, commonly seen in children. However, it is uncommon to discover this subtype in adults, especially in the larynx. We presented a rare case of a 63 years old man who presented with worsening hoarseness, dysphagia for solids, and dyspnea and was later diagnosed with embryonal rhabdomyosarcoma of the larynx.

Preferences for Mobile-Supported e-Cigarette Cessation Interventions Among Young Adults: Qualitative Descriptive Study.

BACKGROUND: Despite the steady rise in electronic cigarette (e-cigarette) uptake among young adults, increasingly more young people want to quit. Given the popularity of smartphones among young adults, mobile-based e-cigarette cessation interventions hold significant promise. Smartphone apps are particularly promising due to their varied and complex capabilities to engage end users. However, evidence around young adults' preferences and expectations from an e-cigarette cessation smartphone app remains unexplored. OBJECTIVE: The purpose of this study was to take an initial step toward understanding young adults' preferences and perceptions on app-based e-cigarette cessation interventions. METHODS: Using a qualitative descriptive approach, we interviewed 12 young adults who used e-cigarettes and wanted to quit. We inductively derived themes using the framework analysis approach and NVivo 12 qualitative data analysis software. RESULTS: All participants agreed that a smartphone app for supporting cessation was desirable. In addition, we found 4 key themes related to their preferences for app components: (1) flexible personalization (being able to enter and modify goals); (2) e-cigarette behavior tracking (progress and benefits of quitting); (3) safely managed social support (moderated and anonymous); and (4) positively framed notifications (encouraging and motivational messages). Some gender-based differences indicate that women were more likely to use e-cigarettes to cope with stress, preferred more aesthetic tailoring in the app, and were less likely to quit cold turkey compared with men. CONCLUSIONS: The findings provide direction for the development and testing of app-based e-cigarette cessation interventions for young adults.

Deep Learning for the Industrial Internet of Things (IIoT): A Comprehensive Survey of Techniques, Implementation Frameworks, Potential Applications, and Future Directions.

The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.

Profile and Predictors of Maternal Quality of Life During Physiological Pregnancy: A Cross-Sectional Analysis.

BACKGROUND: Quality of Life (QoL) and its determinants are significant in all stages of life, including pregnancy. The physical and emotional changes during pregnancy affect the QoL of pregnant women, affecting both maternal and infant health. Hence, assessing the QoL of pregnant women is gaining interest in literature. We, therefore, aimed to describe the QoL of pregnant women during physiological pregnancy and to identify its associated predictors in women attending a public healthcare institute of Quetta city, Pakistan. METHODS: A cross-sectional study was conducted at the Obstetrics and Gynecology Department of Sandeman Provincial Hospital Quetta city, Pakistan. The respondents were asked to answer the Urdu (lingua franca of Pakistan) version of the Quality of Life Questionnaire for Physiological Pregnancy. Data were coded and analyzed by SPPS v 21. The Kolmogorov-Smirnov test was used to establish normality of the data and non-parametric tests were used accordingly. Quality of Life was assessed as proposed by the developers. The Chi-square test was used to identify significant associations and linear regression was used to identify the predictors of QoL. For all analyses, p < 0.05 was taken significantly. RESULTS: Four hundred and three pregnant women participated in the study with a response rate of 98%. The mean QoL score was 19.85 ± 4.89 indicating very good QoL in the current cohort. The Chi-Square analysis reported a significant association between age, education, occupation, income, marital status, and trimester. Education was reported as a positive predictor for QoL (p = 0.006, ß = 2.157). On the other hand, trimester was reported as a negative predictor of QoL (p = 0.013, ß = -1.123). CONCLUSION: Improving the QoL among pregnant women requires better identification of their difficulties and guidance. The current study highlighted educational status and trimester as the predictors of QoL in pregnant women. Health care professionals and policymakers should consider the identified factors while designing therapeutic plans and interventions for pregnant women.

Nanosilver Mitigates Biofilm Formation via FapC Amyloidosis Inhibition.

Multidrug resistance of bacteria is a major challenge due to the wide-spread use of antibiotics. While a range of strategies have been developed in recent years, suppression of bacterial activity and virulence via their network of extracellular amyloid has rarely been explored, especially with nanomaterials. Here, silver nanoparticles and nanoclusters (AgNPs and AgNCs) capped with cationic branched polyethylenimine polymer are synthesized, and their antimicrobial potentials are determined at concentrations safe to mammalian cells. Compared with the ultrasmall AgNCs, AgNPs entail stronger binding to suppress the fibrillization of FapC, a major protein constituent of the extracellular amyloid matrix of Pseudomonas aeruginosa. Both types of nanoparticles exhibit concentration-dependent antibiofilm and antimicrobial properties against P. aeruginosa. At concentrations of 1 × 10-6 m or below, both the bactericidal activity of AgNCs and the antibiofilm capacity of AgNPs are associated with their structure-mediated bio-nano interactions but not ion release. For AgNPs, specifically, their antibiofilm potency correlates with their capacity of FapC fibrillization inhibition, but not with their bactericidal activity. This study demonstrates the antimicrobial potential of safe nanotechnology through the novel route of amyloidosis inhibition.

Polymeric nanocapsules embedded with ultra-small silver nanoclusters for synergistic pharmacology and improved oral delivery of Docetaxel.

Despite of the remarkable cytotoxic and imaging potential of ultra-small metal nanoclusters, their toxicity-free and targeted delivery to cancerous cells remains a substantial challenge that hinders their clinical applications. In this study, a polymeric scaffold was first synthesized by grafting folic acid and thiol groups to chitosan (CS) for cancer cell targeting and improved gastric permeation. Furthermore, silver nanocluster (Ag NCs) were synthesized in situ, within CS scaffold by microwave irradiation and core-shell nanocapsules (NCPs) were prepared with hydrophobic docetaxel (DTX) in the core and Ag NCs embedded CS in the shell. A significant cytotoxicity synergism (~300 folds) was observed for DTX with co-delivery of Ag NCs against breast cancer MDA-MB-231 cells. Following oral administration, the DTX-Ag-NCPs increased bioavailability due to enhanced drug transport across gut (9 times), circulation half-life (~6.8 times) and mean residence time (~6.7 times), as compared to the control DTX suspension. Moreover, 14 days acute oral toxicity of the DTX-Ag-NCPs was performed in mice and evaluated for changes in blood biochemistry parameters, organ to body weight index and histopathology of liver and kidney tissues that revealed no significant evidence of toxicity suggesting the safety and efficiency of the DTX-Ag-NCPs as hybrid nanocarrier for biocompatible delivery of metal nanoclusters.

Cationic Silver Nanoclusters as Potent Antimicrobials against Multidrug-Resistant Bacteria.

Bacterial multidrug resistance (MDR) is a serious healthcare issue caused by the long-term subtherapeutic clinical treatment of infectious diseases. Nanoscale engineering of metal nanoparticles has great potential to address this issue by tuning the nano-bio interface to target bacteria. Herein, we report the use of branched polyethylenimine-functionalized silver nanoclusters (bPEI-Ag NCs) to selectively kill MDR pathogenic bacteria by combining the antimicrobial activity of silver with the selective toxicity of bPEI toward bacteria. The minimum inhibitory concentration of bPEI-Ag NCs was determined against 12 uropathogenic MDR strains and found to be 10- to 15-fold lower than that of PEI and 2- to 3-fold lower than that of AgNO3 alone. Cell viability and hemolysis assays demonstrated the biocompatibility of bPEI-Ag NCs with human fibroblasts and red blood cells, with selective toxicity against MDR bacteria.

Key determinants of selective binding and activation by the monocyte chemoattractant proteins at the chemokine receptor CCR2.

Chemokines and their receptors collectively orchestrate the trafficking of leukocytes in normal immune function and inflammatory diseases. Different chemokines can induce distinct responses at the same receptor. In comparison to monocyte chemoattractant protein-1 (MCP-1; also known as CCL2), the chemokines MCP-2 (CCL8) and MCP-3 (CCL7) are partial agonists of their shared receptor CCR2, a key regulator of the trafficking of monocytes and macrophages that contribute to the pathology of atherosclerosis, obesity, and type 2 diabetes. Through experiments with chimeras of MCP-1 and MCP-3, we identified the chemokine amino-terminal region as being the primary determinant of both the binding and signaling selectivity of these two chemokines at CCR2. Analysis of CCR2 mutants showed that the chemokine amino terminus interacts with the major subpocket in the transmembrane helical bundle of CCR2, which is distinct from the interactions of some other chemokines with the minor subpockets of their receptors. These results suggest the major subpocket as a target for the development of small-molecule inhibitors of CCR2.