Development of pH-Sensitive hydrogel for advanced wound Healing: Graft copolymerization of locust bean gum with acrylamide and acrylic acid.

Wounds pose a formidable challenge in healthcare, necessitating the exploration of innovative tissue-healing solutions. Traditional wound dressings exhibit drawbacks, causing tissue damage and impeding natural healing. Using a Microwave (MW)-)-assisted technique, we envisaged a novel hydrogel (Hg) scaffold to address these challenges. This hydrogel scaffold was created by synthesizing a pH-responsive crosslinked material, specifically locust bean gum-grafted-poly(acrylamide-co-acrylic acid) [LBG-g-poly(AAm-co-AAc)], to enable sustained release of c-phycocyanin (C-Pc). Synthesized LBG-g-poly(AAm-co-AAc) was fine-tuned by adjusting various synthetic parameters, including the concentration of monomers, duration of reaction, and MW irradiation intensity, to maximize the yield of crosslinked LBG grafted product and enhance encapsulation efficiency of C-Pc. Following its synthesis, LBG-g-poly(AAm-co-AAc) was thoroughly characterized using advanced techniques, like XRD, TGA, FTIR, NMR, and SEM, to analyze its structural and chemical properties. Moreover, the study examined the in-vitro C-Pc release profile from LBG-g-poly(AAm-co-AAc) based hydrogel (HgCPcLBG). Findings revealed that the maximum release of C-Pc (64.12 ±â€¯2.69 %) was achieved at pH 7.4 over 48 h. Additionally, HgCPcLBG exhibited enhanced antioxidant performance and compatibility with blood. In vivo studies confirmed accelerated wound closure, and ELISA findings revealed reduced inflammatory markers (IL-6, IL-1ß, TNF-α) within treated skin tissue, suggesting a positive impact on injury repair. A low-cost and eco-friendly approach for creating LBG-g-poly(AAm-co-AAc) and HgCPcLBG has been developed. This method achieved sustained release of C-Pc, which could be a significant step forward in wound care technology.

Characterization of structure of peptidyl-tRNA hydrolase from Enterococcus faecium and its inhibition by a pyrrolinone compound.

In bacteria, peptidyl-tRNA hydrolase (Pth, E.C. 3.1.1.29) is a ubiquitous and essential enzyme for preventing the accumulation of peptidyl-tRNA and sequestration of tRNA. Pth is an esterase that cleaves the ester bond between peptide and tRNA. Here, we present the crystal structure of Pth from Enterococcus faecium (EfPth) at a resolution of 1.92 Å. The two molecules in the asymmetric unit differ in the orientation of sidechain of N66, a conserved residue of the catalytic site. Enzymatic hydrolysis of substrate α-N-BODIPY-lysyl-tRNALys (BLT) by EfPth was characterized by Michaelis-Menten parameters KM 163.5 nM and Vmax 1.9 nM/s. Compounds having pyrrolinone scaffold were tested for inhibition of Pth and one compound, 1040-C, was found to have IC50 of 180 nM. Antimicrobial activity profiling was done for 1040-C. It exhibited equipotent activity against drug-susceptible and resistant S. aureus (MRSA and VRSA) and Enterococcus (VSE and VRE) with MICs 2-8 µg/mL. 1040-C synergized with gentamicin and the combination was effective against the gentamicin resistant S. aureus strain NRS-119. 1040-C was found to reduce biofilm mass of S. aureus to an extent similar to Vancomycin. In a murine model of infection, 1040-C was able to reduce bacterial load to an extent comparable to Vancomycin.

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

BACKGROUND AND OBJECTIVE: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. METHODS: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm against marked DN detection, while box plots and Bland-Altman plots were used to compare its performance with both marked DN detection and an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy Python package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. RESULTS: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87,343) =0.99, p<.001) and PPG (R2(86,764) =0.98, p<.001) waveforms. The algorithm had a lower mean error of DN detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high rate of detectability of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. CONCLUSION: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

The apo-acyl coenzyme A binding protein of Leishmania major forms a unique 'AXXA' motif mediated dimer.

Acyl-Coenzyme A binding domain containing proteins (ACBDs) are ubiquitous in nearly all eukaryotes. They can exist as a free protein, or a domain of a large, multidomain, multifunctional protein. Besides modularity, ACBDs also display multiplicity. The same organism may have multiple ACBDs, differing in sequence and organization. By virtue of this diversity, ACBDs perform functions ranging from transport, synthesis, trafficking, signal transduction, transcription, and gene regulation. In plants and some microorganisms, these ACBDs are designated ACBPs (acyl-CoA binding proteins). The simplest ACBD/ACBP is a small, ∼10 kDa, soluble protein, comprising the acyl-CoA binding (ACB) domain. Most of these small ACBDs exist as monomers, while a few show a tendency to oligomerize. In sync with those studies, we report the crystal structure of two ACBDs from Leishmania major, named ACBP103, and ACBP96 based on the number of residues present. Interestingly, ACBP103 crystallized as a monomer and a dimer under different crystallization conditions. Careful examination of the dimer disclosed an exposed 'AXXA' motif in the helix I of the two ACBP103 monomers, aligned in a head-to-tail arrangement in the dimer. Glutaraldehyde cross-linking studies confirm that apo-ACBP103 can self-associate in solution. Isothermal titration calorimetry studies further show that ACBP103 can bind ligands ranging from C8 - to C20-CoA, and the data could be best fit to a 'two sets of sites'/sequential binding site model. Taken together, our studies show that Leishmania major ACBP103 can self-associate in the apo-form through a unique dimerization motif, an interaction that may play an important role in its function.

An algorithm to detect dicrotic notch in arterial blood pressure and photoplethysmography waveforms using the iterative envelope mean method.

Background and Objective: Detection of the dicrotic notch (DN) within a cardiac cycle is essential for assessment of cardiac output, calculation of pulse wave velocity, estimation of left ventricular ejection time, and supporting feature-based machine learning models for noninvasive blood pressure estimation, and hypotension, or hypertension prediction. In this study, we present a new algorithm based on the iterative envelope mean (IEM) method to detect automatically the DN in arterial blood pressure (ABP) and photoplethysmography (PPG) waveforms. Methods: The algorithm was evaluated on both ABP and PPG waveforms from a large perioperative dataset (MLORD dataset) comprising 17,327 patients. The analysis involved a total of 1,171,288 cardiac cycles for ABP waveforms and 3,424,975 cardiac cycles for PPG waveforms. To evaluate the algorithm's performance, the systolic phase duration (SPD) was employed, which represents the duration from the onset of the systolic phase to the DN in the cardiac cycle. Correlation plots and regression analysis were used to compare the algorithm with an established DN detection technique (second derivative). The marking of the DN temporal location was carried out by an experienced researcher using the help of the 'find_peaks' function from the scipy PYTHON package, serving as a reference for the evaluation. The marking was visually validated by both an engineer and an anesthesiologist. The robustness of the algorithm was evaluated as the DN was made less visually distinct across signal-to-noise ratios (SNRs) ranging from -30 dB to -5 dB in both ABP and PPG waveforms. Results: The correlation between SPD estimated by the algorithm and that marked by the researcher is strong for both ABP (R2(87343) =.99, p<.001) and PPG (R2(86764) =.98, p<.001) waveforms. The algorithm had a lower mean error of dicrotic notch detection (s): 0.0047 (0.0029) for ABP waveforms and 0.0046 (0.0029) for PPG waveforms, compared to 0.0693 (0.0770) for ABP and 0.0968 (0.0909) for PPG waveforms for the established 2nd derivative method. The algorithm has high accuracy of DN detection for SNR of >= -9 dB for ABP waveforms and >= -12 dB for PPG waveforms indicating robust performance in detecting the DN when it is less visibly distinct. Conclusion: Our proposed IEM- based algorithm can detect DN in both ABP and PPG waveforms with low computational cost, even in cases where it is not distinctly defined within a cardiac cycle of the waveform ('DN-less signals'). The algorithm can potentially serve as a valuable, fast, and reliable tool for extracting features from ABP and PPG waveforms. It can be especially beneficial in medical applications where DN-based features, such as SPD, diastolic phase duration, and DN amplitude, play a significant role.

Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.

BACKGROUND: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. METHODS: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. RESULTS: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p < 0.001) and VfSAD (ß of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. CONCLUSIONS: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Structural and biophysical characterization of PadR family protein Rv1176c of Mycobacterium tuberculosis H37Rv.

Rv1176c of Mycobacterium tuberculosis H37Rv belongs to the PadR-s1 subfamily of the PadR family of protein. Rv1176c forms a stable dimer in solution. Its stability is characterized by a thermal melting transition temperature (Tm) of 39.4 °C. The crystal structure of Rv1176c was determined at a resolution of 2.94 Å, with two monomers in the asymmetric unit. Each monomer has a characteristic N-terminal winged-helix-turn-helix DNA-binding domain. Rv1176c C-terminal is a coiled-coil dimerization domain formed of α-helices α5 to α7. In the Rv1176c dimer, there is domain-swapping of the C-terminal domain in comparison to other PadR homologs. In the dimer, there is a long inter-subunit tunnel in which different ligands can bind. Rv1176c was found to bind to the promoter region of its own gene with high specificity. M. smegmatis MC2 155 genome lacks homolog of Rv1176c. Therefore, it was used as a surrogate to characterize the functional role of Rv1176c. Expression of Rv1176c in M. smegmatis MC2 155 cells imparted enhanced tolerance towards oxidative stress. Rv1176c expressing M. smegmatis MC2 155 cells exhibited enhanced intracellular survival in J774A.1 murine macrophage cells. Overall, our studies demonstrate Rv1176c to be a PadR-s1 subfamily transcription factor that can moderate the effect of oxidative stress.

Transporter targeted-carnitine modified pectin-chitosan nanoparticles for inositol hexaphosphate delivery to the colon: An in silico and in vitro approach.

Orally targeted delivery systems have attracted ample interest in colorectal cancer management. In this investigation, we developed Inositol hexaphosphate (IHP) loaded Tripolyphosphate (Tr) crosslinked Pectin (Pe) Chitosan (Ch) nanoparticles (IHP@Tr*Pe-Ch-NPs) and modified them with l-Carnitine (CE) (CE-IHP@Tr*Pe-Ch-NPs) to improve uptake in colon cells. The formulated CE-IHP@Tr*Pe-Ch-NPs displayed a monodisperse distribution with 219.3 ± 5.5 nm diameter and 30.17 mV surface charge. Cell-line studies revealed that CE-IHP@Tr*Pe-Ch-NPs exhibited excellent biocompatibility in J774.2 and decreased cell viability in DLD-1, HT-29, and MCF7 cell lines. More cell internalization was seen in HT-29 and MCF7 due to overexpression of the OCTN2 and ATB0,+ transporter (CE transporters) compared to DLD-1. The cell cycle profile, reactive oxygen species, apoptosis, and mitochondrial membrane potential assays were performed to explore the chemo-preventive mechanism of CE-IHP@Tr*Pe-Ch-NPs. Moreover, the in-silico docking studies revealed enhanced interactive behavior of CE-IHP@Tr*Pe-Ch-NPs, thereby proving their targeting ability. All the findings suggested that CE-IHP@Tr*Pe-Ch-NPs could be a promising drug delivery approach for colon cancer targeting.

Computed tomography-based machine learning for donor lung screening before transplantation.

BACKGROUND: Assessment and selection of donor lungs remain largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo computed tomography (CT) images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs before transplantation. METHODS: Clinical measures and ex situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner before transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning, which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. RESULTS: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) before CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the intensive care unit and were at 19 times higher risk of developing chronic lung allograft dysfunction within 2 years posttransplant. CONCLUSIONS: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of posttransplant complications.

Synthesis of flaxseed gum/melanin-based scaffold: A novel approach for nano-encapsulation of doxorubicin with enhanced anticancer activity.

Doxorubicin is a powerful chemotherapy medicine that is frequently used to treat cancer, but because of its extremely destructive side effects on other healthy cells, its applications have been severely constrained. With the aim of using lower therapeutic doses of doxorubicin while maintaining the same anti-cancerous activity as those of higher doses, the present study designs nano-encapsulation of doxorubicin by acrylamide grafted melanin as core and acrylic acid grafted flax seed gum as shell (DOX@AAM-g-ML/AA-g-FSG-NPs) for studies in-vivo and in-vitro anticancer activity. For biological studies, the cytotoxicity of DOX@AAM-g-ML/AA-g-FSG-NPs was examined on a cancerous human cell line (HCT-15) and it was observed that DOX@AAM-g-ML/AA-g-FSG-NPs exhibited very high toxicity towards HCT-15. In-vivo investigation in colon cancer-inflicted rat model also showed that DOX@AAM-g-ML/AA-g-FSG-NPs showed better anticancer activity against cancerous cells as compared to free doxorubicin. The drug release behavior of DOX@GML-GFS-NPs was studied at several pH and maximum drug release (95 %) was recorded at pH -7.2, and kinetic data of drug release was follows the Higuchi (R2 = 0.9706) kinetic model. Our study is focussed on reducing the side effects of doxorubicin by its nano-encapsulation in acrylamide grafted melanin as core and acrylic acid grafted flax seed gum that will also enhance its efficiency.

The PPE2 protein of Mycobacterium tuberculosis is secreted during infection and facilitates mycobacterial survival inside the host.

Mycobacterium tuberculosis secrets various effector proteins to evade host immune responses for facilitating its intracellular survival. The bacterial genome encodes several unique PE/PPE family proteins, which have been implicated to play important role in mycobacterial pathogenesis. A member of this family, PPE2 have been shown to contain a monopartite nuclear localization signal (NLS) and a DNA binding domain. In this study, we demonstrate that PPE2 protein is present in the sera of mice infected with either M. smegmatis expressing PPE2 or a clinical strain of M. tuberculosis (CDC1551). It was found that exogenously added PPE2 can permeate through the macrophage cell membrane and eventually translocate into the nucleus which requires the presence of NLS which showed considerable homology to HIV-tat like cell permeable peptides. Exogenously added PPE2 could inhibit NO production and decreased mycobacterial survival in macrophages. PPE2-null mutant of M. tuberculosis failed to inhibit NO production and had poor survival in macrophages which could be rescued by complementation with full-length PPE2. PPE2-null mutants also had poor survival in the lungs of infected mice indicating that PPE2 even when present in the bloodstream can confer a survival advantage to mycobacteria.

Machine Learning of Physiologic Waveforms and Electronic Health Record Data: A Large Perioperative Data Set of High-Fidelity Physiologic Waveforms.

Perioperative morbidity and mortality are significantly associated with both static and dynamic perioperative factors. The studies investigating static perioperative factors have been reported; however, there are a limited number of previous studies and data sets analyzing dynamic perioperative factors, including physiologic waveforms, despite its clinical importance. To fill the gap, the authors introduce a novel large size perioperative data set: Machine Learning Of physiologic waveforms and electronic health Record Data (MLORD) data set. They also provide a concise tutorial on machine learning to illustrate predictive models trained on complex and diverse structures in the MLORD data set.

Polymeric Gel Scaffolds and Biomimetic Environments for Wound Healing.

Infected wounds that do not heal are a worldwide problem that is worsening, with more people dying and more money being spent on care. For any disease to be managed effectively, its root cause must be addressed. Effective wound care becomes a bigger problem when various traditional wound healing methods and products may not only fail to promote good healing. Still, it may also hinder the healing process, causing wounds to stay open longer. Progress in tissue regeneration has led to developing three-dimensional scaffolds (3D) or constructs that can be leveraged to facilitate cell growth and regeneration while preventing infection and accelerating wound healing. Tissue regeneration uses natural and fabricated biomaterials that encourage the growth of tissues or organs. Even though the clinical need is urgent, the demand for polymer-based therapeutic techniques for skin tissue abnormalities has grown quickly. Hydrogel scaffolds have become one of the most imperative 3D cross-linked scaffolds for tissue regeneration because they can hold water perfectly and are porous, biocompatible, biodegradable, and biomimetic. For damaged organs or tissues to heal well, the porosity topography of the natural extracellular matrix (ECM) should be imitated. This review details the scaffolds that heal wounds and helps skin tissue to develop. After a brief overview of the bioactive and drug-loaded polymeric hydrogels, the discussion moves on to how the scaffolds are made and what they are made of. It highlights the present uses of in vitro and in-vivo employed biomimetic scaffolds. The prospects of how well bioactiveloaded hydrogels heal wounds and how nanotechnology assists in healing and regeneration have been discussed.

Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression.

Objectives: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. Materials and Methods: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n=8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. Results: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p<0.001) and VfSAD (ß of 0.065, p=0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. Conclusions: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Lung cancer lesion detection in histopathology images using graph-based sparse PCA network.

Early detection of lung cancer is critical for improvement of patient survival. To address the clinical need for efficacious treatments, genetically engineered mouse models (GEMM) have become integral in identifying and evaluating the molecular underpinnings of this complex disease that may be exploited as therapeutic targets. Assessment of GEMM tumor burden on histopathological sections performed by manual inspection is both time consuming and prone to subjective bias. Therefore, an interplay of needs and challenges exists for computer-aided diagnostic tools, for accurate and efficient analysis of these histopathology images. In this paper, we propose a simple machine learning approach called the graph-based sparse principal component analysis (GS-PCA) network, for automated detection of cancerous lesions on histological lung slides stained by hematoxylin and eosin (H&E). Our method comprises four steps: 1) cascaded graph-based sparse PCA, 2) PCA binary hashing, 3) block-wise histograms, and 4) support vector machine (SVM) classification. In our proposed architecture, graph-based sparse PCA is employed to learn the filter banks of the multiple stages of a convolutional network. This is followed by PCA hashing and block histograms for indexing and pooling. The meaningful features extracted from this GS-PCA are then fed to an SVM classifier. We evaluate the performance of the proposed algorithm on H&E slides obtained from an inducible K-rasG12D lung cancer mouse model using precision/recall rates, Fß-score, Tanimoto coefficient, and area under the curve (AUC) of the receiver operator characteristic (ROC) and show that our algorithm is efficient and provides improved detection accuracy compared to existing algorithms.

CT-based Machine Learning for Donor Lung Screening Prior to Transplantation.

Background: Assessment and selection of donor lungs remains largely subjective and experience based. Criteria to accept or decline lungs are poorly standardized and are not compliant with the current donor pool. Using ex vivo CT images, we investigated the use of a CT-based machine learning algorithm for screening donor lungs prior to transplantation. Methods: Clinical measures and ex-situ CT scans were collected from 100 cases as part of a prospective clinical trial. Following procurement, donor lungs were inflated, placed on ice according to routine clinical practice, and imaged using a clinical CT scanner prior to transplantation while stored in the icebox. We trained and tested a supervised machine learning method called dictionary learning , which uses CT scans and learns specific image patterns and features pertaining to each class for a classification task. The results were evaluated with donor and recipient clinical measures. Results: Of the 100 lung pairs donated, 70 were considered acceptable for transplantation (based on standard clinical assessment) prior to CT screening and were consequently implanted. The remaining 30 pairs were screened but not transplanted. Our machine learning algorithm was able to detect pulmonary abnormalities on the CT scans. Among the patients who received donor lungs, our algorithm identified recipients who had extended stays in the ICU and were at 19 times higher risk of developing CLAD within 2 years post-transplant. Conclusions: We have created a strategy to ex vivo screen donor lungs using a CT-based machine learning algorithm. As the use of suboptimal donor lungs rises, it is important to have in place objective techniques that will assist physicians in accurately screening donor lungs to identify recipients most at risk of post-transplant complications.

Crosslinked and PEGylated Pectin Chitosan nanoparticles for delivery of Phytic acid to colon.

Polysaccharide-based nanoparticles (NPs) such as pectin/ chitosan (PN/CN) had always been of greatest interest because of their excellent solubility, biocompatibility, and higher suitability for oral drug delivery. This study employed blending-crosslinking of polymers (PN&CN) followed by emulsification-solvent evaporation to prepare and compare two sets of PEGylated NPs to deliver phytic acid (IP6) to colon orally as it has potential to manage colon cancer but fails to reach colon when ingested in pure form. The first set was crosslinked with Glutaraldehyde (GE) (GE*PN-CN-NPs) while the second set was crosslinked with sodium tripolyphosphate (TPP) (TPP*PN-CN-NPs). IP6-loaded-GE/TPP*PN-CN-NPs were optimized using a central composite design. Developed TPP*PN-CN-NPs had a smaller size (210.6 ± 7.93 nm) than GE*PN-CN-NPs (557.2 ± 5.027 nm). Prepared NPs showed <12% IP6 release at pH 1.2 whereas >80% release was observed at pH 7.4. Further, NPs were explored for cytocompatibility in J774.2 cell lines, cytotoxicity, and cellular uptake in HT-29 and DLD-1 cell lines. While exhibiting substantial cytotoxicity and cellular uptake in HT-29 and DLD-1, the NPs were deemedsafe in J774.2. The PEGylated-TPP*PN-CN-NPs showed time-dependent uptake in J774.2 cell lines. Conclusively, the employed NP development method successfully delivered IP6 to colon and may also open avenues for the oral delivery of other drugs to colon.

Assessment of anti-arthritic activity of lipid matrix encased berberine in rheumatic animal model.

The purpose of this study was to evaluate the drug delivery and therapeutic potential of berberine (Br) loaded nanoformulation in rheumatoid arthritis (RA)-induced animal model. The Br-loaded NLCs (nanostructured lipid carriers) were prepared employing melt-emulsification process, and optimised through Box-Behnken design. The prepared NLCs were assessed for in-vitro and in-vivo evaluations. The optimised NLCs exhibited a mean diameter of 180.2 ± 0.31 nm with 88.32 ± 2.43% entrapment efficiency. An enhanced anti-arthritic activity with reduced arthritic scores to 0.66 ± 0.51, reduction in ankle diameter to 5.80 ± 0.27 mm, decline in paw withdrawal timing, and improvements in walking behaviour were observed in the Br-NLCs treated group. The radiographic images revealed a reduction in bone and cartilage deformation. The Br-NLCs showed promising results in the management of RA disease, can be developed as an efficient delivery system at commercial levels, and may be explored for clinical application after suitable experiments in the future.

Downregulation of pro-inflammatory markers IL-6 and TNF-α in rheumatoid arthritis using nano-lipidic carriers of a quinone-based phenolic: an in vitro and in vivo study.

Rheumatoid arthritis (RA) is a joint ailment with multi-factorial immune-mediated degenerative pathogenesis, including genetic and environmental defects. Resistance to disease-modifying anti-rheumatic drugs (DMARDs) happens due to excessive drug efflux over time, rendering the concentration insufficient to elicit a response. Thymoquinone (TQ) is a quinone-based phenolic compound with antioxidant and anti-inflammatory activities that downregulate numerous pro-inflammatory cytokines. However, its pharmaceutical importance and therapeutic utility are underexplored due to intrinsic physicochemical characteristics such as inadequate biological stability, short half-life, low hydrophilicity, and less systemic availability. Tamanu oil-stabilised nanostructured lipid carriers (TQ-NLCs) were prepared and optimised using Box-Behnken design (BBD) with the size of 153.9 ± 0.52 nm and surface charge of -30.71 mV. The % entrapment efficiency and drug content were found to be 84.6 ± 0.50% and 14.75 ± 0.52%, respectively. Furthermore, the TQ-loaded NLCs (TQ-NLCs) assayed for skin permeation for transdermal delivery which significantly (p < 0.05) increased skin enhancement ratio 14.6 times compared to the aqueous solution of TQ. Tamanu oil displayed the synergistic anti-inflammatory potential with TQ in comparison to pure TQ, as evidenced against carrageenan (CRG)-induced paw oedema model and Freund's adjuvant-induced arthritic model. The arthritic and X-ray scores significantly (p < 0.05) reduced in TQ-NLCs and standard formulation-treated groups. Moreover, serum pro-inflammatory marker TNF-α and IL-6 levels were also significantly (p < 0.05) reduced in TQ-NLCs gel-treated group compared to the arthritic control group.

QbD-assisted development of lipidic nanocapsules for antiestrogenic activity of exemestane in breast cancer.

Some breast cancers are caused by hormonal imbalances, such as estrogen and progesterone. These hormones play a function in directing the growth of cancer cells. The hormone receptors in hormone receptor-positive breast cancer lead breast cells to proliferate out of control. Cancer therapy such as hormonal, targeted, radiation is still unsatisfactory because of these challenges namely multiple drug resistance (MDR), off-targeting, severe adverse effects. A novel aromatase inhibitor exemestane (Exe) exhibits promising therapy in breast cancer. This study aims to develop and optimize Exe-loaded lipid nanocapsules (LNCs) by using DSPC, PF68 and olive oil as lipid, surfactant and oil phase, respectively and to characterize the same. The prepared nanocapsules were investigated via in vitro cell culture and in vivo animal models. The LNCs exhibited cytotoxicity in MCF-7 cell lines and enhanced anti-cancer activity and reduced cardiotoxicity in DMBA-induced animal model when compared to the drug. Additionally, in vivo pharmacokinetics revealed a 4.2-fold increased oral bioavailability when compared with Exe suspension. This study demonstrated that oral administration of Exe-loaded LNCs holds promise for the antiestrogenic activity of exemestane in breast cancer.