Generation of autoantibodies against glycated fibrinogen: Role in diabetic nephropathy and retinopathy.

The process of glycation, characterized by the non-enzymatic reaction between sugars and free amino groups on biomolecules, is a key contributor to the development and progression of both microvascular and macrovascular complications associated with diabetes, particularly due to persistent hyperglycemia. This glycation process gives rise to advanced glycation end products (AGEs), which play a central role in the pathophysiology of diabetes complications, including nephropathy. The d-ribose-mediated glycation of fibrinogen plays a central role in the pathogenesis of diabetes nephropathy (DN) and retinopathy (DR) by the generation and accumulation of advanced glycation end products (AGEs). Glycated fibrinogen with d-ribose (Rb-gly-Fb) induces structural changes that trigger an autoimmune response by generating and exposing neoepitopes on fibrinogen molecules. The present research is designed to investigate the prevalence of autoantibodies against Rb-gly-Fb in individuals with type 2 diabetes mellitus (T2DM), DN & DR. Direct binding ELISA was used to test the binding affinity of autoantibodies from patients' sera against Rb-gly-Fb and competitive ELISA was used to confirm the direct binding findings by checking the bindings of isolated IgG against Rb-gly-Fb and its native conformer. In comparison to healthy subjects, 32% of T2DM, 67% of DN and 57.85% of DR patients' samples demonstrated a strong binding affinity towards Rb-gly-Fb. Both native and Rb-gly-Fb binding by healthy subjects (HS) sera were non-significant (p > 0.05). Furthermore, the early, intermediate, and end products of glycation have been assessed through biochemical and physicochemical analysis. The biochemical markers in the patient groups were also significant (p < 0.05) in comparison to the HS group. This study not only establishes the prevalence of autoantibodies against d-ribose glycated fibrinogen in DN but also highlights the potential of glycated fibrinogen as a biomarker for the detection of DN and/or DR. These insights may open new avenues for research into novel therapeutic strategies and the prevention of diabetes-related nephropathy and retinopathy.

Multivariable prognostic modelling to improve prediction of colorectal cancer recurrence: the PROSPeCT trial.

OBJECTIVE: Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence. MATERIAL AND METHODS: This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals. Follow-up identified cancer recurrence and death. A baseline model for cancer recurrence at 3 years was developed from pre-specified clinicopathological variables (age, sex, tumour-node stage, tumour size, location, extramural venous invasion, and treatment). Then, CT perfusion (blood flow, blood volume, transit time and permeability), genetic (RAS, RAF, and DNA mismatch repair), and immunohistochemical markers of angiogenesis and hypoxia (CD105, vascular endothelial growth factor, glucose transporter protein, and hypoxia-inducible factor) were added to assess whether prediction improved over tumour-node staging alone as the main outcome measure. RESULTS: Three hundred twenty-six of 448 participants formed the final cohort (226 male; mean 66 ± 10 years. 227 (70%) had ≥ T3 stage cancers; 151 (46%) were node-positive; 81 (25%) developed subsequent recurrence. The sensitivity and specificity of staging alone for recurrence were 0.56 [95% CI: 0.44, 0.67] and 0.58 [0.51, 0.64], respectively. The baseline clinicopathologic model improved specificity (0.74 [0.68, 0.79], with equivalent sensitivity of 0.57 [0.45, 0.68] for high vs medium/low-risk participants. The addition of prespecified CT perfusion, genetic, and immunohistochemical markers did not improve prediction over and above the clinicopathologic model (sensitivity, 0.58-0.68; specificity, 0.75-0.76). CONCLUSION: A multivariable clinicopathological model outperformed staging in identifying patients at high risk of recurrence. Promising CT, genetic, and immunohistochemical markers investigated did not further improve prognostication in rigorous prospective evaluation. CLINICAL RELEVANCE STATEMENT: A prognostic model based on clinicopathological variables including age, sex, tumour-node stage, size, location, and extramural venous invasion better identifies colorectal cancer patients at high risk of recurrence for neoadjuvant/adjuvant therapy than stage alone. KEY POINTS: Identification of colorectal cancer patients at high risk of recurrence is an unmet need for treatment personalisation. This model for recurrence, incorporating many patient variables, had higher specificity than staging alone. Continued optimisation of risk stratification schema will help individualise treatment plans and follow-up schedules.

Dietary isothiocyanates inhibit cancer progression by modulation of epigenome.

Cell cycle, growth, survival and metabolism are tightly regulated together and failure in cellular regulation leads to carcinogenesis. Several signaling pathways like the PI3K, WNT, MAPK and NFKb pathway exhibit aberrations in cancer and help achieve hallmark capabilities. Clinical research and in vitro studies have highlighted the role of epigenetic alterations in cancer onset and development. Altered gene expression patterns enabled by changes in DNA methylation, histone modifications and RNA processing have proven roles in cancer hallmark acquisition. The reversible nature of epigenetic processes offers robust therapeutic targets. Dietary bioactive compounds offer a vast compendium of effective therapeutic moieties. Isothiocyanates (ITCs) sourced from cruciferous vegetables demonstrate anti-proliferative, pro-apoptotic, anti-inflammatory, anti-migratory and anti-angiogenic effect against several cancers. ITCs also modulate the redox environment, modulate signaling pathways including PI3K, MAPK, WNT, and NFkB. They also modulate the epigenetic machinery by regulating the expression and activity of DNA methyltransferases, histone modifiers and miRNA. This further enhances their transcriptional modulation of key cellular regulators. In this review, we comprehensively assess the impact of ITCs such as sulforaphane, phenethyl isothiocyanate, benzyl isothiocyanate and allyl isothiocyanate on cancer and document their effect on various molecular targets. Overall, this will facilitate consolidation of the current understanding of the anti-cancer and epigenetic modulatory potential of these compounds and recognize the gaps in literature. Further, we discuss avenues of future research to develop these compounds as potential therapeutic entities.

Microbes in gynecologic cancers: Causes or consequences and therapeutic potential.

Gynecologic cancers, starting in the reproductive organs of females, include cancer of cervix, endometrium, ovary commonly and vagina and vulva rarely. The changes in the composition of microbiome in gut and vagina affect immune and metabolic signaling of the host cells resulting in chronic inflammation, angiogenesis, cellular proliferation, genome instability, epithelial barrier breach and metabolic dysregulation that may lead to the onset or aggravated progression of gynecologic cancers. While microbiome in gynecologic cancers is just at horizon, certain significant microbiome signature associations have been found. Cervical cancer is accompanied with high loads of human papillomavirus, Fusobacteria and Sneathia species; endometrial cancer is reported to have presence of Atopobium vaginae and Porphyromonas species and significantly elevated levels of Proteobacteria and Firmicutes phylum bacteria, with Chlamydia trachomatis, Lactobacillus and Mycobacterium reported in ovarian cancer. Balancing microbiome composition in gynecologic cancers has the potential to be used as a therapeutic target. For example, the Lactobacillus species may play an important role in blocking adhesions of incursive pathogens to vaginal epithelium by lowering the pH, producing bacteriocins and employing competitive exclusions. The optimum or personalized balance of the microbiota can be maintained using pre- and probiotics, and fecal microbiota transplantations loaded with specific bacteria. Current evidence strongly suggest that a healthy microbiome can train and trigger the body's immune response to attack various gynecologic cancers. Furthermore, microbiome modulations can potentially contribute to improvements in immuno-oncology therapies.

Transfer Learning of Full Molecular Weight Distributions via High-Throughput Computer-Controlled Polymerization.

The skew and shape of the molecular weight distribution (MWD) of polymers have a significant impact on polymer physical properties. Standard summary metrics statistically derived from the MWD only provide an incomplete picture of the polymer MWD. Machine learning (ML) methods coupled with high-throughput experimentation (HTE) could potentially allow for the prediction of the entire polymer MWD without information loss. In our work, we demonstrate a computer-controlled HTE platform that is able to run up to 8 unique variable conditions in parallel for the free radical polymerization of styrene. The segmented-flow HTE system was equipped with an inline Raman spectrometer and offline size exclusion chromatography (SEC) to obtain time-dependent conversion and MWD, respectively. Using ML forward models, we first predict monomer conversion, intrinsically learning varying polymerization kinetics that change for each experimental condition. In addition, we predict entire MWDs including the skew and shape as well as SHAP analysis to interpret the dependence on reagent concentrations and reaction time. We then used a transfer learning approach to use the data from our high-throughput flow reactor to predict batch polymerization MWDs with only three additional data points. Overall, we demonstrate that the combination of HTE and ML provides a high level of predictive accuracy in determining polymerization outcomes. Transfer learning can allow exploration outside existing parameter spaces efficiently, providing polymer chemists with the ability to target the synthesis of polymers with desired properties.

Modeling Global Monkeypox Infection Spread Data: A Comparative Study of Time Series Regression and Machine Learning Models.

The global impact of COVID-19 has heightened concerns about emerging viral infections, among which monkeypox (MPOX) has become a significant public health threat. To address this, our study employs a comprehensive approach using three statistical techniques: Distribution fitting, ARIMA modeling, and Random Forest machine learning to analyze and predict the spread of MPOX in the top ten countries with high infection rates. We aim to provide a detailed understanding of the disease dynamics and model theoretical distributions using country-specific datasets to accurately assess and forecast the disease's transmission. The data from the considered countries are fitted into ARIMA models to determine the best time series regression model. Additionally, we employ the random forest machine learning approach to predict the future behavior of the disease. Evaluating the Root Mean Square Errors (RMSE) for both models, we find that the random forest outperforms ARIMA in six countries, while ARIMA performs better in the remaining four countries. Based on these findings, robust policy-making should consider the best fitted model for each country to effectively manage and respond to the ongoing public health threat posed by monkeypox. The integration of multiple modeling techniques enhances our understanding of the disease dynamics and aids in devising more informed strategies for containment and control.

Embedded droplet printing in yield-stress fluids.

Microfluidic tools and techniques for manipulating fluid droplets have become core to many scientific and technological fields. Despite the plethora of existing approaches to fluidic manipulation, non-Newtonian fluid phenomena are rarely taken advantage of. Here we introduce embedded droplet printing-a system and methods for the generation, trapping, and processing of fluid droplets within yield-stress fluids, materials that exhibit extreme shear thinning. This technique allows for the manipulation of droplets under conditions that are simply unattainable with conventional microfluidic methods, namely the elimination of exterior influences including convection and solid boundaries. Because of this, we believe embedded droplet printing approaches an ideal for the experimentation, processing, or observation of many samples in an "absolutely quiescent" state, while also removing some troublesome aspects of microfluidics including the use of surfactants and the complexity of device manufacturing. We characterize a model material system to understand the process of droplet generation inside yield-stress fluids and develop a nascent set of archetypal operations that can be performed with embedded droplet printing. With these principles and tools, we demonstrate the benefits and versatility of our method, applying it toward the diverse applications of pharmaceutical crystallization, microbatch chemical reactions, and biological assays.

A Comprehensive Review of COVID-19-Associated Endocrine Manifestations.

Coronavirus disease 2019 (COVID-19) has played a significant part in systematic damage, affecting lives and leading to significant mortality. The endocrine system is one of the systems affected by this pandemic outbreak. The relationship between them has been identified in previous and ongoing research. The mechanism through which severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) can achieve this is similar to that for organs that express angiotensin-converting enzyme 2 receptors, which is the primary binding site of the virus. Endocrine cells widely express angiotensin-converting enzyme 2 receptors and transmembrane serine protease 2, the primary mediators initiating the acute phase of the disease. This review aimed to identify and discuss the endocrine complications of COVID-19. This primary focus is on presenting thyroid disorders or newly diagnosed diabetes mellitus (DM). Thyroid dysfunction with subacute thyroiditis, Graves' disease, and hypothyroidism caused by primary autoimmune thyroiditis has been reported. Pancreatic damage leads to type 1 DM because of the autoimmune nature of the disease and type 2 DM because of postinflammatory insulin resistance. Because follow-up data on COVID-19 on the endocrine glands are limited, long-term investigations are needed to assess specific effects.

Effect of soil amendments on the sorption behavior of atrazine in sandy loam soil.

The sorption behavior of pesticides applied during cultivation of crops is affected by amendments such as farm yard manure (FYM) and vermicompost (VC) during land preparation. Among pesticides, atrazine, a widely used herbicide in many crops, was analyzed for its kinetics and sorption behavior through the addition of FYM and VC in sandy loam soil. The pseudo-second-order (PSO) model best fit the kinetics results in the recommended dose of FYM and VC mixed soil. More atrazine was sorbed onto VC mixed soil than FYM mixed soil. In comparison to control (no amendment), both FYM and VC (1, 1.5, and 2%) increased atrazine adsorption, but the effect varied with dosage and type of amendment. The Freundlich adsorption isotherm adequately explained atrazine adsorption in soil/soil + (FYM/VC) mixtures, and the adsorption was highly nonlinear. The values of Gibb's free energy change (ΔG) were negative for both adsorption and desorption in soil/soil + (FYM/VC) mixtures, suggesting sorption was exothermic and spontaneous in nature. The results revealed that the application of amendments used by farmers interferes the availability, mobility, and infiltration of atrazine in the soil. Therefore, the findings of this study suggest that amendments such as FYM and VC can be effectively used to minimize the residual toxicity of atrazine-treated ago-ecosystems in tropical and sub-tropical regions.

Increased Levels of Autoantibodies against ROS-Modified Proteins in Depressed Individuals with Decrease in Antibodies against SARS-CoV-2 Antigen (S1-RBD).

Coronavirus 2019 (COVID-19) disease management is highly dependent on the immune status of the infected individual. An increase in the incidence of depression has been observed during the ongoing COVID-19 pandemic. Autoantibodies against in vitro reactive oxygen species (ROS) modified BSA and Lys as well as antibodies against receptor binding domain subunit S1 (S1-RBD) (S1-RBD-Abs) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were estimated using direct binding and competition ELISA. Serum samples were also tested for fasting blood glucose (FBG), malondialdehyde (MDA), carbonyl content (CC), interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Significant structural changes were observed in ROS modified BSA and Lys. Female depressed subjects who were also smokers (F-D-S) showed the highest levels of oxidative stress (MDA and CC levels). Similarly, increased levels of autoantibodies against ROS modified proteins were detected in F-D-S subjects, in males who were depressed and in smokers (M-D-S) compared to the other subjects from the rest of the groups. However, contrary to this observation, levels of S1-RBD-Abs were found to be lowest in the F-D-S and M-D-S groups. During the pandemic, large numbers of individuals have experienced depression, which may induce excessive oxidative stress, causing modifications in circulatory proteins. Thus, the formation of neo-antigens is induced, which lead to the generation of autoantibodies. The concomitant effect of increased autoantibodies with elevated levels of IFN-γ and TNF-α possibly tilt the immune balance toward autoantibody generation rather than the formation of S1-RBD-Abs. Thus, it is important to identify individuals who are at risk of depression to determine immune status and facilitate the better management of COVID-19.

Improving the magnet alignment of undulator systems by laser interferometer.

The issue of intrinsic-type misalignment errors arising from angular offsets between magnets in an undulator is addressed. A random tilt of the magnets or poles generates undesirable magnetic field components in both transverse and longitudinal directions and gives rise to errors in period lengths and amplitudes. These localized errors are carried to the entire undulator segments and are a cause of concern for precision field integral and phase error estimates. A laser interferometer has been designed to read the offsets and to fix the magnets to minimize the offsets.

Microfluidic Particle Engineering of Hydrophobic Drug with Eudragit E100─Bridging the Amorphous and Crystalline Gap.

Co-processing active pharmaceutical ingredients (APIs) with excipients is a promising particle engineering technique to improve the API physical properties, which can lead to more robust downstream drug product manufacturing and improved drug product attributes. Excipients provide control over critical API attributes like particle size and solid-state outcomes. Eudragit E100 is a widely used polymeric excipient to modulate drug release. Being cationic, it is primarily employed as a precipitation inhibitor to stabilize amorphous solid dispersions. In this work, we demonstrate how co-processing of E100 with naproxen (NPX) (a model hydrophobic API) into monodisperse emulsions via droplet microfluidics followed by solidification via solvent evaporation allows the facile fabrication of compact, monodisperse, and spherical particles with an expanded range of solid-state outcomes spanning from amorphous to crystalline forms. Low E100 concentrations (≤26% w/w) yield crystalline microparticles with a stable NPX polymorph distributed uniformly across the matrix at a high drug loading (∼89% w/w). Structurally, E100 incorporation reduces the size of primary particles comprising the co-processed microparticles in comparison to neat API microparticles made using the same technique and the as-received API powder. This reduction in primary particle size translates into an increased internal porosity of the co-processed microparticles, with specific surface area and pore volume ∼9 times higher than the neat API microparticles. These E100-enabled structural modifications result in faster drug release in acidic media compared to neat API microparticles. Additionally, E100-NPX microparticles have a significantly improved flowability compared to neat API microparticles and as-received API powder. Overall, this study demonstrates a facile microfluidics-based co-processing method that broadly expands the range of solid-state outcomes obtainable with E100 as an excipient, with multiscale control over the key attributes and performance of hydrophobic API-laden microparticles.

Corneal nerve loss is related to the severity of painful diabetic neuropathy.

BACKGROUND AND PURPOSE: Previously it has been shown that patients with painful diabetic neuropathy (PDN) have greater corneal nerve loss compared to patients with painless diabetic neuropathy. This study investigated if the severity of corneal nerve loss was related to the severity of PDN. METHODS: Participants with diabetic neuropathy (n = 118) and healthy controls (n = 38) underwent clinical and neurological evaluation, quantitative sensory testing, nerve conduction testing and corneal confocal microscopy and were categorized into those with no (n = 43), mild (n = 34) and moderate-to-severe (n = 41) neuropathic pain. RESULTS: Corneal nerve fibre density (p = 0.003), corneal nerve fibre length (p < 0.0001) and cold perception threshold (p < 0.0001) were lower and warm perception threshold was higher (p = 0.002) in patients with more severe pain, but there was no significant difference in the neuropathy disability score (p = 0.5), vibration perception threshold (p = 0.5), sural nerve conduction velocity (p = 0.3) and amplitude (p = 0.7), corneal nerve branch density (p = 0.06) and deep breathing heart rate variability (p = 0.08) between patients with differing severity of PDN. The visual analogue scale correlated significantly with corneal nerve fibre density (r = -0.3, p = 0.0002), corneal nerve branch density (r = -0.3, p = 0.001) and corneal nerve fibre length (r = -0.4, p < 0.0001). Receiver operating curve analysis showed that corneal nerve fibre density had an area under the curve of 0.78 with a sensitivity of 0.73 and specificity of 0.72 for the diagnosis of PDN. CONCLUSIONS: Corneal confocal microscopy reveals increasing corneal nerve fibre loss with increasing severity of neuropathic pain and a good diagnostic outcome for identifying patients with PDN.

Control of Drug-Excipient Particle Attributes with Droplet Microfluidic-based Extractive Solidification Enables Improved Powder Rheology.

PURPOSE: Industrial implementation of continuous oral solid dosage form manufacturing has been impeded by the poor powder flow properties of many active pharmaceutical ingredients (APIs). Microfluidic droplet-based particle synthesis is an emerging particle engineering technique that enables the production of neat or composite microparticles with precise control over key attributes that affect powder flowability, such as particle size distribution, particle morphology, composition, and the API's polymorphic form. However, the powder properties of these microparticles have not been well-studied due to the limited mass throughputs of available platforms. In this work, we produce spherical API and API-composite microparticles at high mass throughputs, enabling characterization and comparison of the bulk powder flow properties of these materials and greater understanding of how particle-scale attributes correlate with powder rheology. METHODS: A multi-channel emulsification device and an extractive droplet-based method are harnessed to synthesize spherical API and API-excipient particles of artemether. As-received API and API crystallized in the absence of droplet confinement are used as control cases. Particle attributes are characterized for each material and correlated with a comprehensive series of powder rheology tests. RESULTS: The droplet-based processed artemether particles are observed to be more flowable, less cohesive, and less compressible than conventionally synthesized artemether powder. Co-processing the API with polycaprolactone to produce composite microparticles reduces the friction of the powder on stainless steel, a common equipment material. CONCLUSIONS: Droplet-based extractive solidification is an attractive particle engineering technique for improving powder processing and may aid in the implementation of continuous solid dosage form manufacturing.

Nonenzymatic glycosylation of isolated human immunoglobulin-G by D-ribose.

Glycation is vital in terms of its damaging effect on macromolecules resulting in the formation of end products, which are highly reactive and cross-linked irreversible structures, known as advanced glycation end products (AGEs). The continuous accumulation of AGEs is associated with severe diabetes and its associated ailments. Saccharides with their reducing ends can glycate amino acid side chains of proteins, among them glucose is well-known for its potent glycating capability. However, other reducing sugars can be more reactive glycating agents than glucose. The D-ribose is a pentose sugar-containing an active aldehyde group in its open form and is responsible for affecting the biological processes of the cellular system. D-ribose, a key component of many biological molecules, is more reactive than most reducing sugars. Protein glycation by reducing monosaccharides such as D-ribose promotes the accelerated formation of AGEs that could lead to cellular impairments and dysfunctions. Also, under a physiological cellular state, the bioavailability rate of D-ribose is much higher than that of glucose in diabetes, which makes this species much more active in protein glycation as compared with D-glucose. Due to the abnormal level of D-ribose in the biological system, the glycation of proteins with D-ribose needs to be analyzed and addressed carefully. In the present study, human immunoglobulin G (IgG) was isolated and purified via affinity column chromatography. D-ribose at 10 and 100 mM concentrations was used as glycating agent, for 1-12 days of incubation at 37°C. The postglycation changes in IgG molecule were characterized by UV-visible and fluorescence spectroscopy, nitroblue tetrazolium assay, and various other physicochemical analyses for the confirmation of D-ribose mediated IgG glycation.

No evidence of improvement in neuropathy after renal transplantation in patients with end stage kidney disease.

To assess the impact of renal transplantation on peripheral nerve damage in patients with chronic kidney disease (CKD). Fifteen patients with CKD (eGFR <15 mL/min/1.73 m2 ) underwent longitudinal assessment after renal transplantation (age: 56.88 ± 2.53 years, eGFR: 46.82 ± 4.86) and were compared with 15 age-matched controls (age: 58.25 ± 2.18 years, eGFR: 86.0 ± 2.0). The neuropathy symptom profile (NSP), neuropathy disability score (NDS), vibration perception threshold (VPT), cold and warm sensation threshold (CST and WST), cold and heat induced pain (CIP and HIP), deep breathing heart rate variability (DB-HRV), nerve conduction studies and corneal confocal microscopy (CCM) to quantify small nerve fibre pathology, were undertaken within 1-month of renal transplantation (baseline) and at 6, 12 and 24 months of follow up. There was no significant difference in NSP (P = .1), NDS (P = .3), VPT (P = .6), CST (P = .2), CIP (P = .08), HIP (P = .1), DB-HRV (P = .9) and sural (P = .4) and peroneal (P = .1) nerve amplitude between patients with CKD and controls at baseline. However, sural (P = .04), peroneal (P = .002) and tibial (P = .007) nerve conduction velocity and tibial nerve amplitude (P = .03) were significantly lower, WST (P = .02) was significantly higher and corneal nerve fibre density (P = .004) was significantly lower in patients with CKD compared with controls. There was no significant change in NSP, NDS, quantitative sensory testing, DB-HRV, nerve conduction or CCM parameters 24 months after renal transplantation. There is evidence of small and large fibre neuropathy in patients with CKD, but no change up to 24 months after successful renal transplantation.

Insights into Multifunctional Nanoparticle-Based Drug Delivery Systems for Glioblastoma Treatment.

Glioblastoma (GB) is an aggressive cancer with high microvascular proliferation, resulting in accelerated invasion and diffused infiltration into the surrounding brain tissues with very low survival rates. Treatment options are often multimodal, such as surgical resection with concurrent radiotherapy and chemotherapy. The development of resistance of tumor cells to radiation in the areas of hypoxia decreases the efficiency of such treatments. Additionally, the difficulty of ensuring drugs effectively cross the natural blood-brain barrier (BBB) substantially reduces treatment efficiency. These conditions concomitantly limit the efficacy of standard chemotherapeutic agents available for GB. Indeed, there is an urgent need of a multifunctional drug vehicle system that has potential to transport anticancer drugs efficiently to the target and can successfully cross the BBB. In this review, we summarize some nanoparticle (NP)-based therapeutics attached to GB cells with antigens and membrane receptors for site-directed drug targeting. Such multicore drug delivery systems are potentially biodegradable, site-directed, nontoxic to normal cells and offer long-lasting therapeutic effects against brain cancer. These models could have better therapeutic potential for GB as well as efficient drug delivery reaching the tumor milieu. The goal of this article is to provide key considerations and a better understanding of the development of nanotherapeutics with good targetability and better tolerability in the fight against GB.

Recent Advances in Co-processed APIs and Proposals for Enabling Commercialization of These Transformative Technologies.

Optimized physical properties (e.g., bulk, surface/interfacial, and mechanical properties) of active pharmaceutical ingredients (APIs) are key to the successful integration of drug substance and drug product manufacturing, robust drug product manufacturing operations, and ultimately to attaining consistent drug product critical quality attributes. However, an appreciable number of APIs have physical properties that cannot be managed via routes such as form selection, adjustments to the crystallization process parameters, or milling. Approaches to control physical properties in innovative ways offer the possibility of providing additional and unique opportunities to control API physical properties for both batch and continuous drug product manufacturing, ultimately resulting in simplified and more robust pharmaceutical manufacturing processes. Specifically, diverse opportunities to significantly enhance API physical properties are created if allowances are made for generating co-processed APIs by introducing nonactive components (e.g., excipients, additives, carriers) during drug substance manufacturing. The addition of a nonactive coformer during drug substance manufacturing is currently an accepted approach for cocrystals, and it would be beneficial if a similar allowance could be made for other nonactive components with the ability to modify the physical properties of the API. In many cases, co-processed APIs could enable continuous direct compression for small molecules, and longer term, this approach could be leveraged to simplify continuous end-to-end drug substance to drug product manufacturing processes for both small and large molecules. As with any novel technology, the regulatory expectations for co-processed APIs are not yet clearly defined, and this creates challenges for commercial implementation of these technologies by the pharmaceutical industry. The intent of this paper is to highlight the opportunities and growing interest in realizing the benefits of co-processed APIs, exemplified by a body of academic research and industrial examples. This work will highlight reasons why co-processed APIs would best be considered as drug substances from a regulatory perspective and emphasize the areas where regulatory strategies need to be established to allow for commercialization of innovative approaches in this area.

A trial sequential meta-analysis of IFN-γ +874 A>T (rs2430561) gene polymorphism and extrapulmonary tuberculosis risk.

Interferon-γ (IFN-γ) plays a crucial role in immunological responses against Mycobacterium tuberculosis (M.tb) infection. The polymorphism at +874 A > T (rs2430561) influences the levels of IFN-γ, which may further influence the susceptibility to extrapulmonary tuberculosis (EPTB). This polymorphism has been investigated with respect to EPTB occurrence in different populations and provided contradictory and conflicting results. This study was performed to meta-statistically analyze the data and draw a more accurate conclusion regarding the association of IFN-γ +874 A > T gene polymorphism and EPTB susceptibility. A quantitative synthesis was executed for the pertinent studies retrieved from online web-databases viz. Google Scholar, PubMed/Medline and EMBASE. The pooled odds ratios (ORs) and confidence intervals (95% CIs) were estimated for all the genetic models by meta-analysis. A total of eight studies were retrieved which included 762 confirmed EPTB cases and 1341 controls. The meta-analysis results revealed reduced association of EPTB in allelic contrast (T vs. A: p = 0.001; OR = 0.668, 95% CI = 0.524 to 0.850), homozygous (TT vs. AA: p = 0.017; OR = 0.450, 95% CI = 0.234 to 0.868), heterozygous (AT vs. AA: p = 0.004; OR = 0.574, 95% CI = 0.395 to 0.835), dominant (TT + AT vs. AA: p = 0.003; OR = 0.536, 95% CI = 0.354 to 0.810) and recessive (TT vs. AA + AT: p = 0.039; OR = 0.662, 95% CI = 0.448 to 0.980) genetic models. Furthermore, re-sampling statistics also revealed reduced risk of EPTB in overall population and Asian subgroup. This meta-analysis concluded that IFN-γ +874 A > T gene polymorphism is meaningfully related with the reduced EPTB risk in overall and Asian population, and further necessitates larger studies to be conducted on this topic in other races.

Targeting Caspase 8: Using Structural and Ligand-Based Approaches to Identify Potential Leads for the Treatment of Multi-Neurodegenerative Diseases.

Caspase 8 is a central player in the apoptotic cell death pathway and is also essential for cytokine processing. The critical role of this protease in cell death pathways has generated research interest because its activation has also been linked with neural cell death. Thus, blocking the activity of caspase 8 is considered a potential therapy for neurodegenerative diseases. To extend the repertoire of caspase 8 inhibitors, we employed several computational approaches to identify potential caspase 8 inhibitors. Based on the structural information of reported inhibitors, we designed several individual and consensus pharmacophore models and then screened the ZINC database, which contains 105,480 compounds. Screening generated 5332 candidates, but after applying stringent criteria only two candidate compounds, ZINC19370490 and ZINC04534268, were evaluated by molecular dynamics simulations and subjected to Molecular Mechanics/Poisson Boltzmann Surface Area (MM-PBSA) analysis. These compounds were stable throughout simulations and interacted with targeted protein by forming hydrogen and van der Waal bonds. MM-PBSA analysis showed that these compounds were comparable or better than reported caspase 8 inhibitors. Furthermore, their physical properties were found to be acceptable, and they are non-toxic according to the ADMET online server. We suggest that the inhibitory efficacies of ZINC19370490 and ZINC04534268 be subjected to experimental validation.