Remediation of pharmacophoric laboratory waste by using biodegradable carbon nanoparticles of bacterial biofilm origin.

Research laboratories generate a broad range of hazardous pharmacophoric chemical contaminants, from drugs to dyes used during various experimental procedures. In the recent past, biological methods have demonstrated great potential in the remediation of such contaminants. However, the presence of pharmacophoric chemicals containing antibiotics, xenobiotics, and heavy metals suppresses the growth and survivability of used microbial agents, thus decreasing the overall efficiency of biological remediation processes. Bacterial biofilm is a natural arrangement to counter some of these inhibitions but its use in a systemic manner, portable devices, and pollutant remediation plants post serious challenges. This could be countered by synthesizing a biodegradable carbon nanoparticle from bacterial biofilm. In this study, extracellular polymeric substance-based carbon nanoparticles (Bio-EPS-CNPs) were synthesized from bacterial biofilm derived from Bacillus subtilis NCIB 3610, as a model bacterial system. The produced Bio-EPS-CNPs were investigated for physiochemical properties by dynamic light scattering, optical, Fourier-transformed infrared, and Raman spectroscopy techniques, whereas X-ray diffraction study, scanning electron microscopy, and transmission electron microscopy were used to investigate structural and morphological features. The Bio-EPS-CNPs exhibited negative surface charge with spherical morphology having a uniform size of sub-100 nm. The maximum remediation of some laboratory-produced pharmacophoric chemicals was achieved through a five-round scavenging process and confirmed by UV/Vis spectroscopic analysis with respect to the used pharmacophore. This bioinspired remediation of used pharmacophoric chemicals was achieved through the mechanism of surface adsorption via hydrogen bonding and electrostatic interactions, as revealed by different characterizations. Further experiments were performed to investigate the effects of pH, temperature, stirring, and the protocol of scavenging to establish Bio-EPS-CNP as a possible alternative to be used in research laboratories for efficient removal of pharmacophoric chemicals by incorporating it in a portable, filter-based device.

APASL clinical practice guidelines on the management of acute kidney injury in acute-on-chronic liver failure.

Acute-on-chronic liver failure (ACLF) is a syndrome that is characterized by the rapid development of organ failures predisposing these patients to a high risk of short-term early death. The main causes of organ failure in these patients are bacterial infections and systemic inflammation, both of which can be severe. For the majority of these patients, a prompt liver transplant is still the only effective course of treatment. Kidneys are one of the most frequent extrahepatic organs that are affected in patients with ACLF, since acute kidney injury (AKI) is reported in 22.8-34% of patients with ACLF. Approach and management of kidney injury could improve overall outcomes in these patients. Importantly, patients with ACLF more frequently have stage 3 AKI with a low rate of response to the current treatment modalities. The objective of the present position paper is to critically review and analyze the published data on AKI in ACLF, evolve a consensus, and provide recommendations for early diagnosis, pathophysiology, prevention, and management of AKI in patients with ACLF. In the absence of direct evidence, we propose expert opinions for guidance in managing AKI in this very challenging group of patients and focus on areas of future research. This consensus will be of major importance to all hepatologists, liver transplant surgeons, and intensivists across the globe.

A comprehensive youth diabetes epidemiological dataset and web portal: Resource Development and Case Studies.

BACKGROUND: The prevalence of Type 2 diabetes (DM) and prediabetes (preDM) has been increasing among youth in recent decades in the United States, prompting an urgent need for understanding and identifying their associated risk factors. Such efforts, however, have been hindered by the lack of easily accessible youth preDM/DM data. OBJECTIVE: We aimed to first build a high quality, comprehensive epidemiological dataset focused on youth preDM/DM. Subsequently, we aimed to make this data accessible by creating a user-friendly web portal to share it and corresponding codes. Through this, we hope to address this significant gap and facilitate youth preDM/DM research. METHODS: Building on data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018, we cleaned and harmonized hundreds of variables relevant to preDM/DM (fasting plasma glucose level ≥100 mg/dL and/or HbA1C ≥5.7%) for youth aged 12-19 years (n=15,149). We identified individual factors associated with preDM/DM risk using bivariate statistical analyses and predicted preDM/DM status using our Ensemble Integration (EI) framework for multi-domain machine learning. We then developed a user-friendly web portal named Prediabetes/diabetes in youth ONline Dashboard (POND) to share the data and codes. RESULTS: We extracted 95 variables potentially relevant to preDM/DM risk organized into 4 domains (sociodemographic, health status, diet, and other lifestyle behaviors). The bivariate analyses identified 27 significant correlates of preDM/DM (P ≤0.0005, Bonferroni adjusted), including race/ethnicity, health insurance, BMI, added sugar intake, and screen time. Sixteen of these factors were also identified based on the EI methodology (Fisher's P of overlap=7.06x10^-6). In addition to those, the EI approach identified 11 additional predictive variables, including some known (e.g., meat and fruit intake and family income) and less recognized factors (e.g., number of rooms in homes). The factors identified in both analyses spanned over all 4 of the domains mentioned. These data and results, as well as other exploratory tools, can be accessed on POND (https://rstudio-connect.hpc.mssm.edu/POND/). CONCLUSIONS: Using NHANES data, we built one of the largest public epidemiological datasets for studying youth preDM/DM and identified potential risk factors using complementary analytical approaches. Our results align with the multifactorial nature of preDM/DM with correlates across several domains. Also, our data-sharing platform, POND, facilitates a wide range of applications to inform future youth preDM/DM studies.

Coherent multi-band MIMO radar: robustness analysis to SSMF-based RF signal delivery.

A numerical evaluation is conducted to assess the impact of distributing radio frequency (RF) signals through optical fiber links on the performance of a coherent multi-band multiple-input multiple-output (MIMO) radar system. The analysis focuses on scenarios where the antennas are widely separated in comparison to the employed signal wavelengths. The development of a model to quantify the phase noise (PN) induced on each RF band due to the signal transmission through optical fiber links between the centralized base station and each radar peripheral is described. Monte Carlo simulation results are collected to estimate the key performance indicators (KPIs) for varying standard single-mode fiber (SSMF) length and different PN contributions. The main contributors to the PN are revealed to be chromatic dispersion (CD), double Rayleigh scattering (DRS), and mechanical vibrations. In a shipborne scenario, a significant performance degradation occurs only when the length of the fiber links reaches approximately 20 km. Further, the PN impact has also been studied in a shipborne scenario to analyze the robustness of the system for worse phase noise level assumptions. The results reveal excellent robustness of the proposed centralized acquisition and processing approach in the presence of both very long fiber links and economically employed RF oscillators.

Combination of EZH2 and ATM inhibition in BAP1-deficient mesothelioma.

BACKGROUND: More than half of mesothelioma tumours show alterations in the tumour suppressor gene BAP1. BAP1-deficient mesothelioma is shown to be sensitive to EZH2 inhibition in preclinical settings but only showed modest efficacy in clinical trial. Adding a second inhibitor could potentially elevate EZH2i treatment efficacy while preventing acquired resistance at the same time. METHODS: A focused drug synergy screen consisting of 20 drugs was performed by combining EZH2 inhibition with a panel of anti-cancer compounds in mesothelioma cell lines. The compounds used are under preclinical investigation or already used in the clinic. The synergistic potential of the combinations was assessed by using the Bliss model. To validate our findings, in vivo xenograft experiments were performed. RESULTS: Combining EZH2i with ATMi was found to have synergistic potential against BAP1-deficient mesothelioma in our drug screen, which was validated in clonogenicity assays. Tumour growth inhibition potential was significantly increased in BAP1-deficient xenografts. In addition, we observe lower ATM levels upon depletion of BAP1 and hypothesise that this might be mediated by E2F1. CONCLUSIONS: We demonstrated the efficacy of the combination of ATM and EZH2 inhibition against BAP1-deficient mesothelioma in preclinical models, indicating the potential of this combination as a novel treatment modality using BAP1 as a biomarker.

Relationship Between Cognitive Impairment and Depression Among Middle Aged and Older Adults in Primary Care.

Objectives: To determine rates of previously undetected cognitive impairment among patients with depression in primary care. Methods: Patients ages 55 and older with no documented history of dementia or mild cognitive impairment were recruited from primary care practices in New York City, NY and Chicago, IL (n = 855). Cognitive function was assessed with the Montreal Cognitive Assessment (MoCA) and depression with the Patient Health Questionnaire-8. Results: The mean age was 66.8 (8.0) years, 45.3% were male, 32.7% Black, and 29.2% Latinx. Cognitive impairment increased with severity of depression: 22.9% in persons with mild depression, 27.4% in moderate depression and 41.8% in severe depression (p = .0002). Severe depression was significantly associated with cognitive impairment in multivariable analysis (standardized ß = -.11, SE = 0.33, p < .0001). Discussion: Depression was strongly associated with previously undetected cognitive impairment. Primary care clinicians should consider screening, or expand their screening, for both conditions.

Chimeric antigen receptor macrophages target and resorb amyloid plaques.

Substantial evidence suggests a role for immunotherapy in treating Alzheimer's disease (AD). While the precise pathophysiology of AD is incompletely understood, clinical trials of antibodies targeting aggregated forms of ß amyloid (Aß) have shown that reducing amyloid plaques can mitigate cognitive decline in patients with early-stage AD. Here, we describe what we believe to be a novel approach to target and degrade amyloid plaques by genetically engineering macrophages to express an Aß-targeting chimeric antigen receptor (CAR-Ms). When injected intrahippocampally, first-generation CAR-Ms have limited persistence and fail to significantly reduce plaque load, which led us to engineer next-generation CAR-Ms that secrete M-CSF and self-maintain without exogenous cytokines. Cytokine secreting "reinforced CAR-Ms" have greater survival in the brain niche and significantly reduce plaque load locally in vivo. These findings support CAR-Ms as a platform to rationally target, resorb, and degrade pathogenic material that accumulates with age, as exemplified by targeting Aß in AD.

Machine learning-driven identification of air toxic combinations associated with asthma symptoms among elementary school children in Spokane, Washington, USA.

Air toxics are atmospheric pollutants with hazardous effects on health and the environment. Although methodological constraints have limited the number of air toxics assessed for associations with health and disease, advances in machine learning (ML) enable the assessment of a much larger set of environmental exposures. We used ML methods to conduct a retrospective study to identify combinations of 109 air toxics associated with asthma symptoms among 269 elementary school students in Spokane, Washington. Data on the frequency of asthma symptoms for these children were obtained from Spokane Public Schools. Their exposure to air toxics was estimated by using the Environmental Protection Agency's Air Toxics Screening Assessment and National Air Toxics Assessment. We defined three exposure periods: the most recent year (2019), the last three years (2017-2019), and the last five years (2014-2019). We analyzed the data using the ML-based Data-driven ExposurE Profile (DEEP) extraction method. DEEP identified 25 air toxic combinations associated with asthma symptoms in at least one exposure period. Three combinations (1,1,1-trichloroethane, 2-nitropropane, and 2,4,6-trichlorophenol) were significantly associated with asthma symptoms in all three exposure periods. Four air toxics (1,1,1-trichloroethane, 1,1,2,2-tetrachloroethane, BIS (2-ethylhexyl) phthalate (DEHP), and 2,4-dinitrophenol) were associated only in combination with other toxics, and would not have been identified by traditional statistical methods. The application of DEEP also identified a vulnerable subpopulation of children who were exposed to 13 of the 25 significant combinations in at least one exposure period. On average, these children experienced the largest number of asthma symptoms in our sample. By providing evidence on air toxic combinations associated with childhood asthma, our findings may contribute to the regulation of these toxics to improve children's respiratory health.

Microwave Doping of Sulfur and Iron in ß12 Borophene.

Borophene, a 2D material exhibiting unique crystallographic phases like the anisotropic atomic lattices of ß12 and X3 phases, has attracted considerable attention due to its intriguing Dirac nature and metallic attributes. Despite surpassing graphene in electronic mobility, borophene's potential in energy storage and catalysis remains untapped due to its inherent electrochemical and catalytic limitations. Elemental doping emerges as a promising strategy to introduce charge carriers, enabling localized electrochemical and catalytic functionalities. However, effective doping of borophene has been a complex and underexplored challenge. Here, an innovative, one-pot microwave-assisted doping method, tailored for the ß12 phase of borophene is introduced. By subjecting dispersed ß12 borophene in dimethylformamide to controlled microwave exposure with sulfur powder and FeCl3 as doping precursors, S- and Fe doping in borophene can be controlled. Employing advanced techniques including high-resolution transmission electron microscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy, confirm successful sulfur and iron dopant incorporation onto ß12 borophene is confirmed, achieving doping levels of up to 11 % and 13 %, respectively. Remarkably, S- and Fe-doped borophene exhibit exceptional supercapacitive behavior, with specific capacitances of 202 and 120 F g-1 , respectively, at a moderate current density of 0.25 A g-1 .

A primer on the use of machine learning to distil knowledge from data in biological psychiatry.

Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in psychiatry. The review covers definitions and expositions of commonly used machine learning methods, and historical trends of their use in psychiatry. We also provide a set of standards, namely Guidelines for REporting Machine Learning Investigations in Neuropsychiatry (GREMLIN), for designing and reporting studies that use machine learning as a primary data-analysis approach. Lastly, we propose the establishment of the Machine Learning in Psychiatry (MLPsych) Consortium, enumerate its objectives, and identify areas of opportunity for future applications of machine learning in biological psychiatry. This review serves as a cautiously optimistic primer on machine learning for those on the precipice as they prepare to dive into the field, either as methodological practitioners or well-informed consumers.

Combined Inhibition of EZH2 and FGFR is Synergistic in BAP1-deficient Malignant Mesothelioma.

Malignant mesothelioma is a highly aggressive tumor with a survival of only 4-18 months after diagnosis. Treatment options for this disease are limited. Immune checkpoint blockade using ipilimumab and nivolumab has recently been approved as a frontline therapy, but this led to only a small improvement in overall patient survival. As more than half of patients with mesothelioma have alterations in the gene encoding for BAP1 this could be a potential marker for targeted therapies. In this study, we investigated the synergistic potential of combining EZH2 inhibition together with FGFR inhibition for treatment of BAP1-deficient malignancies. The efficacy of the combination was evaluated using human and murine preclinical models of mesothelioma and uveal melanoma in vitro. The efficacy of the combination was further validated in vivo by using BAP1-deficient mesothelioma xenografts and autochthonous mouse models. In vitro data showed sensitivity to the combined inhibition in BAP1-deficient mesothelioma and uveal melanoma tumor cell lines but not for BAP1-proficient subtypes. In vivo data showed susceptibility to the combination of BAP1-deficient xenografts and demonstrated an increase of survival in autochthonous models of mesothelioma. These results highlight the potential of this novel drug combination for the treatment of mesothelioma using BAP1 as a biomarker. Given these encouraging preclinical results, it will be important to clinically explore dual EZH2/FGFR inhibition in patients with BAP1-deficient malignant mesothelioma and justify further exploration in other BAP1 loss-associated tumors. SIGNIFICANCE: Despite the recent approval of immunotherapy, malignant mesothelioma has limited treatment options and poor prognosis. Here, we observe that EZH2 inhibitors dramatically enhance the efficacy of FGFR inhibition, sensitising BAP1-mutant mesothelioma and uveal melanoma cells. The striking synergy of EZH2 and FGFR inhibition supports clinical investigations for BAP1-mutant tumors.

Improved Stability and Manufacturability of Nucleocapsid Antigens for SARS-CoV2 Diagnostics through Protein Engineering.

The COVID-19 pandemic has had a significant impact on human health management. A rapid diagnosis of SARS-CoV2 at the point-of-care (POC) is critical to prevent disease spread. As a POC device for remote settings, a LFIA should not require cold-chain maintenance and should be kept at normal temperatures. Antigen stability can be enhanced by addressing instability issues when dealing with fragile components, such as proteinaceous capture antigens. This study used immunologically guided protein engineering to enhance the capture nucleocapsid (NP) antigen stability of SARS-CoV2. A search of the IEDB database revealed that antibodies detecting epitopes are almost uniformly distributed over NP1-419. In contrast, N-terminal stretches of NP1-419 are theoretically more unstable than C-terminal stretches. We identified NP250-365 as a NP stretch with a low instability index and B-cell epitopes. Apart from NP1-419, two other variants (NP121-419 and NP250-365) were cloned, expressed, and purified. The degradation pattern of the proteins was observed on SDS-PAGE after three days of stability studies at -20 °C, 4 °C, and 37 °C. NP1-419 was the most degraded while NP250-365 exhibited the least degradation. Also, NP1-419, NP250-365, and NP121-419 reacted with purified antibodies from COVID-19 patient serum. Our results suggest that NP250-365 may be used as a stable capture antigen in LFIA devices to detect COVID-19.

Strong Laser Field-Driven Coupled Electron-Nuclear Dynamics: Quantum vs Classical Description.

We have performed a coupled electron-nuclear dynamics study of H2+ molecular ions under the influence of an intense few-cycle 4.5 fs laser pulse with an intensity of 4 × 1014 W/cm2 and a central wavelength of 750 nm. Both quantum and classical dynamical methods are employed in the exact similar initial conditions with the aim of head-to-head comparison of two methodologies. A competition between ionization and dissociation channel is explained under the framework of quantum and classical dynamics. The origin of the electron localization phenomena is elucidated by observing the molecular and electronic wave packet evolution pattern. By probing with different carrier envelope phase (CEP) values of the ultrashort pulse, the possibility of electron localization on either of the two nuclei is investigated. The effects of initial vibrational states on final dissociation and ionization probabilities for several CEP values are studied in detail. Finally, asymmetries in the dissociation probabilities are calculated and mutually compared for both quantum and classical dynamical methodologies, whereas Franck-Condon averaging over the initial vibrational states is carried out in order to mimic the existing experimental conditions.

BAP1 promotes osteoclast function by metabolic reprogramming.

Treatment of osteoporosis commonly diminishes osteoclast number which suppresses bone formation thus compromising fracture prevention. Bone formation is not suppressed, however, when bone degradation is reduced by retarding osteoclast functional resorptive capacity, rather than differentiation. We find deletion of deubiquitinase, BRCA1-associated protein 1 (Bap1), in myeloid cells (Bap1∆LysM), arrests osteoclast function but not formation. Bap1∆LysM osteoclasts fail to organize their cytoskeleton which is essential for bone degradation consequently increasing bone mass in both male and female mice. The deubiquitinase activity of BAP1 modifies osteoclast function by metabolic reprogramming. Bap1 deficient osteoclast upregulate the cystine transporter, Slc7a11, by enhanced H2Aub occupancy of its promoter. SLC7A11 controls cellular reactive oxygen species levels and redirects the mitochondrial metabolites away from the tricarboxylic acid cycle, both being necessary for osteoclast function. Thus, in osteoclasts BAP1 appears to regulate the epigenetic-metabolic axis and is a potential target to reduce bone degradation while maintaining osteogenesis in osteoporotic patients.

Exploring the Druggable Conformational Space of Protein Kinases Using AI-Generated Structures.

Protein kinase function and interactions with drugs are controlled in part by the movement of the DFG and ɑC-Helix motifs, which enable kinases to adopt various conformational states. Small molecule ligands elicit therapeutic effects with distinct selectivity profiles and residence times that often depend on the kinase conformation(s) they bind. However, the limited availability of experimentally determined structural data for kinases in inactive states restricts drug discovery efforts for this major protein family. Modern AI-based structural modeling methods hold potential for exploring the previously experimentally uncharted druggable conformational space for kinases. Here, we first evaluated the currently explored conformational space of kinases in the PDB and models generated by AlphaFold2 (AF2) (1) and ESMFold (2), two prominent AI-based structure prediction methods. We then investigated AF2's ability to predict kinase structures in different conformations at various multiple sequence alignment (MSA) depths, based on this parameter's ability to explore conformational diversity. Our results showed a bias within the PDB and predicted structural models generated by AF2 and ESMFold toward structures of kinases in the active state over alternative conformations, particularly those conformations controlled by the DFG motif. Finally, we demonstrate that predicting kinase structures using AF2 at lower MSA depths allows the exploration of the space of these alternative conformations, including identifying previously unobserved conformations for 398 kinases. The results of our analysis of structural modeling by AF2 create a new avenue for the pursuit of new therapeutic agents against a notoriously difficult-to-target family of proteins. Significance Statement: Greater abundance of kinase structural data in inactive conformations, currently lacking in structural databases, would improve our understanding of how protein kinases function and expand drug discovery and development for this family of therapeutic targets. Modern approaches utilizing artificial intelligence and machine learning have potential for efficiently capturing novel protein conformations. We provide evidence for a bias within AlphaFold2 and ESMFold to predict structures of kinases in their active states, similar to their overrepresentation in the PDB. We show that lowering the AlphaFold2 algorithm's multiple sequence alignment depth can help explore kinase conformational space more broadly. It can also enable the prediction of hundreds of kinase structures in novel conformations, many of whose models are likely viable for drug discovery.

Facilitating youth diabetes studies with the most comprehensive epidemiological dataset available through a public web portal.

The prevalence of type 2 diabetes mellitus (DM) and prediabetes (preDM) is rapidly increasing among youth, posing significant health and economic consequences. To address this growing concern, we created the most comprehensive youth-focused diabetes dataset to date derived from National Health and Nutrition Examination Survey (NHANES) data from 1999 to 2018. The dataset, consisting of 15,149 youth aged 12 to 19 years, encompasses preDM/DM relevant variables from sociodemographic, health status, diet, and other lifestyle behavior domains. An interactive web portal, POND (Prediabetes/diabetes in youth ONline Dashboard), was developed to provide public access to the dataset, allowing users to explore variables potentially associated with youth preDM/DM. Leveraging statistical and machine learning methods, we conducted two case studies, revealing established and lesser-known variables linked to youth preDM/DM. This dataset and portal can facilitate future studies to inform prevention and management strategies for youth prediabetes and diabetes.

Machine learning using institution-specific multi-modal electronic health records improves mortality risk prediction for cardiac surgery patients.

Background: The Society of Thoracic Surgeons risk scores are widely used to assess risk of morbidity and mortality in specific cardiac surgeries but may not perform optimally in all patients. In a cohort of patients undergoing cardiac surgery, we developed a data-driven, institution-specific machine learning-based model inferred from multi-modal electronic health records and compared the performance with the Society of Thoracic Surgeons models. Methods: All adult patients undergoing cardiac surgery between 2011 and 2016 were included. Routine electronic health record administrative, demographic, clinical, hemodynamic, laboratory, pharmacological, and procedural data features were extracted. The outcome was postoperative mortality. The database was randomly split into training (development) and test (evaluation) cohorts. Models developed using 4 classification algorithms were compared using 6 evaluation metrics. The performance of the final model was compared with the Society of Thoracic Surgeons models for 7 index surgical procedures. Results: A total of 6392 patients were included and described by 4016 features. Overall mortality was 3.0% (n = 193). The XGBoost algorithm using only features with no missing data (336 features) yielded the best-performing predictor. When applied to the test set, the predictor performed well (F-measure = 0.775; precision = 0.756; recall = 0.795; accuracy = 0.986; area under the receiver operating characteristic curve = 0.978; area under the precision-recall curve = 0.804). eXtreme Gradient Boosting consistently demonstrated improved performance over the Society of Thoracic Surgeons models when evaluated on index procedures within the test set. Conclusions: Machine learning models using institution-specific multi-modal electronic health records may improve performance in predicting mortality for individual patients undergoing cardiac surgery compared with the standard-of-care, population-derived Society of Thoracic Surgeons models. Institution-specific models may provide insights complementary to population-derived risk predictions to aid patient-level decision making.

Isolated pulmonary hemorrhage after electric shock: a rare phenomenon.

Any type of contact with electricity of low or high voltage can cause injury to the human body, with a variable effect on the body. Low-voltage injury is quite common worldwide, but there is very little information present in the available literature. The degree of organ damage depends on many factors, which include the duration of electric current exposure, current type, and nature of the affected tissue. The most common presentations are muscle injury, hyperkalemia, pulmonary edema, and rarely isolated diffuse pulmonary hemorrhage. We present a case of bilateral pulmonary hemorrhage due to electric shock with no visible signs of damage to the chest wall when exposed to a 220 V shock. The diagnosis was confirmed by fresh hemoptysis, chest imaging that showed bilateral perihilar ground glass opacities, and bronchoscopy findings. Given a life-threatening condition, a timely diagnosis is required, as massive hemoptysis can occlude the airways, leading to hypoxia and mortality.

Chimeric Antigen Receptor Macrophages Target and Resorb Amyloid Plaques in a Mouse Model of Alzheimer's Disease.

Substantial evidence suggests a role for immunotherapy in treating Alzheimer's disease (AD). Several monoclonal antibodies targeting aggregated forms of beta amyloid (Aß), have been shown to reduce amyloid plaques and in some cases, mitigate cognitive decline in early-stage AD patients. We sought to determine if genetically engineered macrophages could improve the targeting and degradation of amyloid plaques. Chimeric antigen receptor macrophages (CAR-Ms), which show promise as a cancer treatment, are an appealing strategy to enhance target recognition and phagocytosis of amyloid plaques in AD. We genetically engineered macrophages to express a CAR containing the anti-amyloid antibody aducanumab as the external domain and the Fc receptor signaling domain internally. CAR-Ms recognize and degrade Aß in vitro and on APP/PS1 brain slices ex vivo; however, when injected intrahippocampally, these first-generation CAR-Ms have limited persistence and fail to reduce plaque load. We overcame this limitation by creating CAR-Ms that secrete M-CSF and self-maintain without exogenous cytokines. These CAR-Ms have greater survival in the brain niche, and significantly reduce plaque load locally in vivo. These proof-of-principle studies demonstrate that CAR-Ms, previously only applied to cancer, may be utilized to target and degrade unwanted materials, such as amyloid plaques in the brains of AD mice.

Bioactive molecules from fungal endophytes and their applications in pharmaceutical industries: challenges and future scope.

Medicinal plants are an important source of bioactive compounds and have been used to isolate various bioactive compounds having industrial applications. The demand for plants derived bioactive molecules is increasing gradually. However, the extensive use of these plants to extract bioactive molecules has threatened many plant species. Moreover, extracting bioactive molecules from these plants is laborious, costly, and time-consuming. So, some alternative sources and strategies are urgently needed to produce these bioactive molecules similar to that of plant origin. However, the interest in new bioactive molecules has recently shifted from plants to endophytic fungi because many fungi produce bioactive molecules similar to their host plant. Endophytic fungi live in mutualistic association within the healthy plant tissue without causing disease symptoms to the host plant. These fungi are a treasure house of novel bioactive molecules having broad pharmaceutical, industrial, and agricultural applications. The rapid increase in publications in this domain over the last three decades proves that natural product biologists and chemists are paying great attention to the natural bioactive products from endophytic fungi. Though endophytes are source of novel bioactive molecules but there is need of advanced technologies like clustered regularly interspaced short palindromic repeats and CRISPR-associated protein 9 (CRISPR-Cas9) and epigenetic modifiers to enhance the production of compounds having industrial applications. This review provides an overview of the various industrial applications of bioactive molecules produced by endophytic fungi and the rationale behind selecting specific plants for fungal endophyte isolation. Overall, this study presents the current state of knowledge and highlights the potential of endophytic fungi for developing alternative therapies for drug-resistant infections.