Hybrid motility mechanism of sperm at viscoelastic-solid interface.

To fertilize eggs, sperm must pass through narrow, complex channels filled with viscoelastic fluids in the female reproductive tract. While it is known that the topography of the surfaces plays a role in guiding sperm movement, sperm have been thought of as swimmers, i.e., their motility comes solely from sperm interaction with the surrounding fluid, and therefore, the surfaces have no direct role in the motility mechanism itself. Here, we examined the role of solid surfaces in the movement of sperm in a highly viscoelastic medium. By visualizing the flagellum interaction with surfaces in a microfluidic device, we found that the flagellum stays close to the surface while the kinetic friction between the flagellum and the surface is in the direction of sperm movement, providing thrust. Additionally, the flow field generated by sperm suggests slippage between the viscoelastic fluid and the solid surface, deviating from the no-slip boundary typically used in standard fluid dynamics models. These observations point to hybrid motility mechanisms in sperm involving direct flagellum-surface interaction in addition to flagellum pushing the fluid. This finding signifies an evolutionary strategy of mammalian sperm crucial for their efficient migration through narrow, mucus-filled passages of the female reproductive tract.

After-meal blood glucose level prediction for type-2 diabetic patients.

Type 2 Diabetes, a metabolic disorder disease, is becoming a fast growing health crisis worldwide. It reduces the quality of life, and increases mortality and health care costs unless managed well. After-meal blood glucose level measure is considered as one of the most fundamental and well-recognized steps in managing Type 2 diabetes as it guides a user to make better plans of their diet and thus control the diabetes well. In this paper, we propose a data-driven approach to predict the 2 h after meal blood glucose level from the previous discrete blood glucose readings, meal, exercise, medication, & profile information of Type 2 diabetes patients. To the best of our knowledge, this is the first attempt to use discrete blood glucose readings for 2 h after meal blood glucose level prediction using data-driven models. In this study, we have collected data from five prediabetic and diabetic patients in free living conditions for six months. We have presented comparative experimental study using different popular machine learning models including support vector regression, random forest, and extreme gradient boosting, and two deep layer techniques: multilayer perceptron, and convolutional neural network. We present also the impact of different features in blood glucose level prediction, where we observe that meal has some modest and medication has a good influence on blood glucose level.

Phage resistance in Klebsiella pneumoniae and bidirectional effects impacting antibiotic susceptibility.

OBJECTIVES: Bacteriophage (phage) therapy is a promising anti-infective option to combat antimicrobial resistance. However, the clinical utilization of phage therapy has been severely compromised by the potential emergence of phage resistance. Although certain phage resistance mechanisms can restore bacterial susceptibility to certain antibiotics, a lack of knowledge of phage resistance mechanisms hinders optimal use of phages and their combination with antibiotics. METHODS: Genome-wide transposon screening was performed with a mutant library of Klebsiella pneumoniae MKP103 to identify phage pKMKP103_1-resistant mutants. Phage-resistant phenotypes were evaluated by time-kill kinetics and efficiency of plating assays. Phage resistance mechanisms were investigated with adsorption, one-step growth, and mutation frequency assays. Antibiotic susceptibility was determined with broth microdilution and population analysis profiles. RESULTS: We observed a repertoire of phage resistance mechanisms in K pneumoniae, such as disruption of phage binding (fhuA::Tn and tonB::Tn), extension of the phage latent period (mnmE::Tn and rpoN::Tn), and increased mutation frequency (mutS::Tn and mutL::Tn). Notably, in contrast to the prevailing view that phage resistance re-sensitizes antibiotic-resistant bacteria, we observed a bidirectional steering effect on bacterial antibiotic susceptibility. Specifically, rpoN::Tn increased susceptibility to colistin while mutS::Tn and mutL::Tn increased resistance to rifampicin and colistin. DISCUSSION: Our findings demonstrate that K pneumoniae employs multiple strategies to overcome phage infection, which may result in enhanced or reduced antibiotic susceptibility. Mechanism-guided phage steering should be incorporated into phage therapy to better inform clinical decisions on phage-antibiotic combinations.

Transcriptomic interplay between Acinetobacter baumannii , human macrophage and polymyxin.

Optimization of antibiotic therapy has been hindered by our dearth of understanding on the mechanism of the host-pathogen-drug interactions. Here, we employed dual RNA-sequencing to examine transcriptomic perturbations in response to polymyxin B in a co-culture infection model of Acinetobacter baumannii and human macrophages. Our findings revealed that polymyxin B treatment induced significant transcriptomic response in macrophage-interacting A. baumannii , exacerbating bacterial oxidative stress, disrupting metal homeostasis, affecting osmoadaptation, triggering stringent stress response, and influencing pathogenic factors. Moreover, infected macrophages adapt heme catabolism, coagulation cascade, and hypoxia-inducible signaling to confront bacterial invasion. Disrupting rcnB , ompW , and traR/dksA genes in A. baumannii impairs metal homeostasis, osmotic stress defense and stringent responses, thereby enhancing antibacterial killing by polymyxin. These findings shed light on the global stress adaptations at the network level during host-pathogen-drug interactions, revealing promising therapeutic targets for further investigation. IMPORTANCE: In the context of the development of bacterial resistance during the course of antibiotic therapy, the role of macrophages in shaping bacterial response to antibiotic killing remains enigmatic. Herein we employed dual RNA-sequencing and an in vitro tripartite model to delve into the unexplored transcriptional networks of the Acinetobacter baumannii -macrophage-polymyxin axis. Our findings uncovered the potential synergy between macrophages and polymyxin B which appear to act in co-operation to disrupt multiple stress tolerance mechanisms in A. baumannii . Notably, we discovered the critical roles of bacterial nickel/cobalt homeostasis ( rcnB family), osmotic stress defense ( ompW family), and stringent response regulator ( traR/dksA C4-type zinc finger) in tolerating the last-line antibiotic polymyxin B. Our findings may lead to potential targets for the development of novel therapeutics against the problematic pathogen A. baumannii .

Transmission of high arterial pressure into renal microvessels during venous-clamping augments ischaemia/reperfusion-induced acute kidney injury in anaesthetized rats.

AIM: In two recent studies, we observed that a 30-min renal vein clamping caused formation of interstitial haemorrhagic congestion in ischaemic and ischaemic/reperfused kidney along with the development of severer acute kidney injury (AKI) than renal artery or pedicle clamping. It was suggested that the transmission of high arterial pressure into renal microvessels during vein occlusion probably causes the occurrence of interstitial haemorrhagic congestion that augments AKI. The present investigation aimed to evaluate this suggestion by reducing renal perfusion pressure (RPP) during renal venous occlusion. METHODS: Anaesthetized male Sprague-Dawley rats were divided into three groups (n = 8), which underwent a 2-h reperfusion period following 30-min bilateral renal venous clamping along with reduced RPP (VIR-rRPP group) or without reduced RPP (VIR group) and an equivalent period after sham-operation (Sham group). RESULTS: The VIR-rRPP group compared with VIR group had lower levels of kidney malondialdehyde and tissue damages as epithelial injuries of proximal tubule and thick ascending limb, vascular congestion, intratubular cast and oedema, along with the less reductions in renal blood flow, creatinine clearance, Na+ -reabsorption, K+ and urea excretion, urine osmolality and free-water reabsorption. Importantly, the formation of intensive interstitial haemorrhagic congestion in the VIR group was not observed in the VIR-rRPP group. CONCLUSION: These results indicate that the transmission of high arterial pressure into renal microvessels during venous occlusion leads to rupturing of their walls and the formation of interstitial haemorrhagic congestion, which has an augmenting impact on ischaemia/reperfusion-induced renal structural damages and haemodynamic, excretory and urine-concentrating dysfunctions.

Effects of nebulized hypertonic saline on inflammatory mediators in patients with severe COVID-19 pneumonia: A double-blinded randomized controlled trial.

INTRODUCTION: An exaggerated immune response is considered the most important aspect of COVID-19 pathogenesis. Hypertonic saline (HS) has shown promise in combating inflammation in several respiratory diseases. We investigated the effects of nebulized HS on clinical symptoms and inflammatory status in patients with severe novel coronavirus infection (COVID-19) pneumonia. MATERIALS AND METHODS: We randomly assigned 60 adults admitted to the intensive care unit (ICU) due to severe COVID-19 pneumonia to the experimental (received nebulized 5% saline) and control (received nebulized distilled water) groups. All interventions were applied 4 times daily for 5 days. The levels of tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6), and other clinical factors from venous blood were evaluated before and after intervention application. Mortality rate, intubation rate, and durations of ICU and hospital stay were also compared between groups. RESULTS: The levels of TNF-α (MD: -21.35 [-32.29, -10.40], P = 0.000) and IL-6 (-9.94 [-18.86, -1.02], P = 0.003) were lower in the experimental group compared to the control group after applying the interventions. The levels of white blood cell count, PO2, and serum sodium were also statistically significant differences between groups. However, we did not observe significant differences in terms of hospitalization durations and mortality rates. CONCLUSION: Nebulization of HS in patients with severe COVID-19 pneumonia appears to be effective in reducing inflammation, but does not appear to affect intubation rates, mortality, hospitalization, or length of stay in ICU.

Applications of Depth Minimization of Decision Trees Containing Hypotheses for Multiple-Value Decision Tables.

In this research, we consider decision trees that incorporate standard queries with one feature per query as well as hypotheses consisting of all features' values. These decision trees are used to represent knowledge and are comparable to those investigated in exact learning, in which membership queries and equivalence queries are used. As an application, we look into the issue of creating decision trees for two cases: the sorting of a sequence that contains equal elements and multiple-value decision tables which are modified from UCI Machine Learning Repository. We contrast the efficiency of several forms of optimal (considering the parameter depth) decision trees with hypotheses for the aforementioned applications. We also investigate the efficiency of decision trees built by dynamic programming and by an entropy-based greedy method. We discovered that the greedy algorithm produces very similar results compared to the results of dynamic programming algorithms. Therefore, since the dynamic programming algorithms take a long time, we may readily apply the greedy algorithms.

Transcriptomic Responses to Polymyxin B and Analogues in Human Kidney Tubular Cells.

Polymyxins are last-line antibiotics for the treatment of Gram-negative 'superbugs'. However, nephrotoxicity can occur in up to 60% of patients administered intravenous polymyxins. The mechanisms underpinning nephrotoxicity remain unclear. To understand polymyxin-induced nephrotoxicity, human renal proximal tubule cells were treated for 24 h with 0.1 mM polymyxin B or two new analogues, FADDI-251 or FADDI-287. Transcriptomic analysis was performed, and differentially expressed genes (DEGs) were identified using ANOVA (FDR < 0.2). Cell viability following treatment with polymyxin B, FADDI-251 or FADDI-287 was 66.0 ± 5.33%, 89.3 ± 3.96% and 90.4 ± 1.18%, respectively. Transcriptomics identified 430, 193 and 150 DEGs with polymyxin B, FADDI-251 and FADDI-287, respectively. Genes involved with metallothioneins and Toll-like receptor pathways were significantly perturbed by all polymyxins. Only polymyxin B induced perturbations in signal transduction, including FGFR2 and MAPK signaling. SIGNOR network analysis showed all treatments affected essential regulators in the immune system, autophagy, cell cycle, oxidative stress and apoptosis. All polymyxins caused significant perturbations of metal homeostasis and TLR signaling, while polymyxin B caused the most dramatic perturbations of the transcriptome. This study reveals the impact of polymyxin structure modifications on transcriptomic responses in human renal tubular cells and provides important information for designing safer new-generation polymyxins.

Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis.

Histopathology is the most accurate way to diagnose cancer and identify prognostic and therapeutic targets. The likelihood of survival is significantly increased by early cancer detection. With deep networks' enormous success, significant attempts have been made to analyze cancer disorders, particularly colon and lung cancers. In order to do this, this paper examines how well deep networks can diagnose various cancers using histopathology image processing. This work intends to increase the performance of deep learning architecture in processing histopathology images by constructing a novel fine-tuning deep network for colon and lung cancers. Such adjustments are performed using regularization, batch normalization, and hyperparameters optimization. The suggested fine-tuned model was evaluated using the LC2500 dataset. Our proposed model's average precision, recall, F1-score, specificity, and accuracy were 99.84%, 99.85%, 99.84%, 99.96%, and 99.94%, respectively. The experimental findings reveal that the suggested fine-tuned learning model based on the pre-trained ResNet101 network achieves higher results against recent state-of-the-art approaches and other current powerful CNN models.

Preparation of Free-Flowing Spray-Dried Amorphous Composites Using Neusilin®.

A highly porous additive, Neusilin®, with high adsorption capability is investigated to improve bulk properties, hence processability of spray-dried amorphous solid dispersions (ASDs). Griseofulvin (GF) is applied as a model BCS class 2 drug in ASDs. Two grades of Neusilin®, US2 (coarser) and UFL2 (finer), were used as additives to produce spray-dried amorphous composite (AC) powders, and their performance was compared with the resulting ASDs without added Neusilin®. The resulting AC powders that included Neusilin® had greatly enhanced flowability (flow function coefficient (FFC) > 10) comparable to larger particles (100 µm) yet had finer particle size (< 50 µm), hence retaining the advantage of fast dissolution rate of finer sizes. Dissolution results demonstrated that achieved GF supersaturation for AC powders with Neusilin® was as high as 3 times that of crystalline GF concentration and was achieved within 30 min. In addition, 80% of drug was released within 4 min. The flowability improvement for AC powders with Neusilin® was more significant as compared to spray-dried ASDs without Neusilin®. Thus, the role of Neusilin® in flowability improvement was evident, considering that spray-dried AC with Neusilin® UFL2 has higher FFC than ASDs having a similar size. Lastly, the AC powders retained a fully amorphous state of GF after 3-month ambient storage. The overall results conveyed that the improved flowability and dissolution rate could outweigh the loss of drug loading resulted by addition of Neusilin®.

The Effects of an Empathic Communication Workshop on Internal Medicine Residents' Self-Perceived Empathy and Their Patients' Perception of Physician's Empathy: A Single-Group Experimental Study.

Background: Empathetic communication improves the physician-patient relationship and enhances patient and physician satisfaction. This study aims to evaluate the impact of empathic communication skills training on physicians' self-perceived performance and patient satisfaction regarding the empathetic quality of their relationship with their physicians. Methods: In this single-group before-after experimental study, we recruited 50 internal medicine residents at a large teaching hospital. We assessed the residents' empathy using the Jefferson Scale of Empathy before and 3 weeks after an 8-hour workshop on empathic communication skills. We also recruited 50 of their patients before and another 50 patients 3 weeks after the training to assess the patient's perceptions of their physician's empathy using the Consultation and Relational Empathy scale. Physicians' and patients' mean scores on empathetic care at the beginning of the study were then compared using paired t-tests with their scores after the workshop. Results: The residents' mean score on Jefferson Empathy Scale increased from 81.1(95%CI:78.8-83.3) at baseline to 96.8(95%CI:93.6-100) following the workshop (p < 0.001). Before the empathetic communication skills training, patients assessed their doctors' empathy at 68.3(95%CI:63.5-73.2). After the intervention, this improved to 84.9(95%CI:82.2-87.5) (p < 0.001). Conclusion: In this study, both the residents and their patients stated that the residents' empathy skills had significantly improved after an empathetic communication workshop for internal medicine residents.

An Enhanced Machine Learning Approach for Brain MRI Classification.

Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space's dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today's era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40-60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

AdipoRon induces AMPK activation and ameliorates Alzheimer's like pathologies and associated cognitive impairment in APP/PS1 mice.

Alzheimer's disease (AD) is a progressive devastating neurodegenerative disorder characterized by extracellular amyloid beta (Aß42) plaque formation, hyperphosphorylation of tau protein leading to intracellular neurofibrillary tangle formation. Recently discovered hallmark features responsible for AD pathogenesis are neuronal insulin resistance, dysregulation in adiponectin and AMPK signaling. The presence of adiponectin and its receptor in the brain with its unique anti-diabetic effects and association with neurodegenerative diseases has raised our interest in exploring orally active small molecule adiponectin receptor agonist, AdipoRon. To date, all the available drugs for the treatment of AD provides symptomatic relief and do not stall the progression of the disease. Indeed, it is becoming increasingly apparent to find appropriate targets. Here, we attempt to shed lights on adiponectin receptor agonist, AdipoRon and its downstream molecular targets in reducing disease pathogenesis and insulin resistance. In brain, AdipoRon induced AMPK activation, increased insulin sensitivity, reduced amyloid beta plaque deposition and improved cognitive impairment. Levels of BACE were also downregulated while LDLR, APOE and neprilysin were upregulated promoting amyloid beta clearance from brain. AdipoRon further reduced the chronic inflammatory marker, GFAP and improved synaptic markers PSD-95 and synaptophysin in APP/PS1 mice. Our in-vitro studies further confirmed the potential role of AdipoRon in improving insulin sensitivity by increasing GLUT 4 translocation, glucose uptake and insulin signaling under hyperinsulinemic condition. Our findings suggest that AdipoRon could be a promising lead in the future treatment strategies in the development of effective AD treatment.

Improved Aerosolization Stability of Inhalable Tobramycin Powder Formulation by Co-Spray Drying with Colistin.

PURPOSE: Tobramycin shows synergistic antibacterial activity with colistin and can reduce the toxic effects of colistin. The purpose of this study is to prepare pulmonary powder formulations containing both colistin and tobramycin and to assess their in vitro aerosol performance and storage stability. METHODS: The dry powder formulations were manufactured using a lab-scale spray dryer. In vitro aerosol performance was measured using a Next Generation Impactor. The storage stability of the dry powder formulations was measured at 22°C and two relative humidity levels - 20 and 55%. Colistin composition on the particle surface was measured using X-ray photoelectron spectroscopy. RESULTS: Two combination formulations, with 1:1 and 1:5 molar ratios of colistin and tobramycin, showed fine particle fractions (FPF) of 85%, which was significantly higher than that of the spray dried tobramycin (45%). FPF of the tobramycin formulation increased significantly when stored for four weeks at both 20% and 55% RH. In contrast, FPF values of both combination formulations and spray dried colistin remained stable at both humidity levels. Particle surface of each combination was significantly enriched in colistin molecules; 1:5 combination showed 77% by wt. colistin. CONCLUSIONS: The superior aerosol performance and aerosolization stability of 1:1 and 1:5 combination formulations of colistin and tobramycin could be attributed to enrichment of colistin on the co-spray dried particle surface. The observed powder properties may be the result of a surfactant-like assembly of these colistin molecules during spray drying, thus forming a hydrophobic particle surface.

Inwardly rectifying potassium channels mediate polymyxin-induced nephrotoxicity.

Polymyxin antibiotics are often used as a last-line defense to treat life-threatening Gram-negative pathogens. However, polymyxin-induced kidney toxicity is a dose-limiting factor of paramount importance and can lead to suboptimal treatment. To elucidate the mechanism and develop effective strategies to overcome polymyxin toxicity, we employed a whole-genome CRISPR screen in human kidney tubular HK-2 cells and identified 86 significant genes that upon knock-out rescued polymyxin-induced toxicity. Specifically, we discovered that knockout of the inwardly rectifying potassium channels Kir4.2 and Kir5.1 (encoded by KCNJ15 and KCNJ16, respectively) rescued polymyxin-induced toxicity in HK-2 cells. Furthermore, we found that polymyxins induced cell depolarization via Kir4.2 and Kir5.1 and a significant cellular uptake of polymyxins was evident. All-atom molecular dynamics simulations revealed that polymyxin B1 spontaneously bound to Kir4.2, thereby increasing opening of the channel, resulting in a potassium influx, and changes of the membrane potential. Consistent with these findings, small molecule inhibitors (BaCl2 and VU0134992) of Kir potassium channels reduced polymyxin-induced toxicity in cell culture and mouse explant kidney tissue. Our findings provide critical mechanistic information that will help attenuate polymyxin-induced nephrotoxicity in patients and facilitate the design of novel, safer polymyxins.

A Precise Medical Imaging Approach for Brain MRI Image Classification.

Magnetic resonance imaging (MRI) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging. Most of the existing systems showed significant performances on small MRI datasets, while their performances decrease against large MRI datasets. Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large MRI dataset. Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from MRI image. The proposed algorithm selects the best features from the MRI images of various diseases. Further, this approach discriminates various classes based on recursive values such as partial Z-value. The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models. The most interrelated features are nominated in the forward regression model that depends on the values of partial Z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model. In both cases, the values of Z-test are estimated through the defined labels of the diseases. The proposed model is efficiently looking the localized features, which is one of the benefits of this method. After extracting and selecting the best features, the model is trained by utilizing support vector machine (SVM) to provide the predicted labels to the corresponding MRI images. To show the significance of the proposed model, we utilized a publicly available standard dataset such as Harvard Medical School and Open Access Series of Imaging Studies (OASIS), which contains 24 various brain diseases including normal. The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

An Efficient Approach for the Design and Synthesis of Antimicrobial Peptide-Peptide Nucleic Acid Conjugates.

Peptide-Peptide Nucleic Acid (PNA) conjugates targeting essential bacterial genes have shown significant potential in developing novel antisense antimicrobials. The majority of efforts in this area are focused on identifying different PNA targets and the selection of peptides to deliver the peptide-PNA conjugates to Gram-negative bacteria. Notably, the selection of a linkage strategy to form peptide-PNA conjugate plays an important role in the effective delivery of PNAs. Recently, a unique Cysteine- 2-Cyanoisonicotinamide (Cys-CINA) click chemistry has been employed for the synthesis of cyclic peptides. Considering the high selectivity of this chemistry, we investigated the efficiency of Cys-CINA conjugation to synthesize novel antimicrobial peptide-PNA conjugates. The PNA targeting acyl carrier protein gene (acpP), when conjugated to the membrane-active antimicrobial peptides (polymyxin), showed improvement in antimicrobial activity against multidrug-resistant Gram-negative Acinetobacter baumannii. Thus, indicating that the Cys-CINA conjugation is an effective strategy to link the antisense oligonucleotides with antimicrobial peptides. Therefore, the Cys-CINA conjugation opens an exciting prospect for antimicrobial drug development.

Multicomponent Droplet Drying Modeling Based on Conservation and Population Balance Equations.

The aim of this work is to present a modeling tool to describe drying kinetics and delineate evolving physical and chemical behavior of multicomponent droplets during drying. Conservation equations coupled with population balance equations (PBE) are used to achieve this goal. Modeling results are gauged with single salt-water droplet drying from literature and show congruent trends. This model is then extended to a more complex system: various droplet sizes containing methanol (solvent), Felodipine (active ingredient), and PVP (polyvinylpyrrolidone as excipient). The FIB-SEM (Focused-Ion Beam Scanning Electron Microscopy) imaging results from spray-dried particles produced with similar formulation and processing conditions are consistent with phase behavior predicted by the model. The results show competing impacts of transport phenomena on the intermittent shell formation process and final particle structure and chemical heterogeneity. Solute diffusion, solvent efflux, and intra-drop flow impact the model system. It is found that shell formation follows a fluctuating profile where the initial precipitation of the dissolved species on the droplet surface is dampened, and nucleated particles become dispersed periodically until the shell becomes strong enough to withstand internal circulations. These internal effects are dependent on droplet size and are pronounced for larger droplets. That is, the particle phase behavior and physical nature are functions of the atomized droplet size. Stemming understating from this study would inform an optimized unit, operating in target design space. This would provide better product quality control and minimize discrepancies observed in process development during the early phase vs. commercial scale.

Correlative proteomics identify the key roles of stress tolerance strategies in Acinetobacter baumannii in response to polymyxin and human macrophages.

The opportunistic pathogen Acinetobacter baumannii possesses stress tolerance strategies against host innate immunity and antibiotic killing. However, how the host-pathogen-antibiotic interaction affects the overall molecular regulation of bacterial pathogenesis and host response remains unexplored. Here, we simultaneously investigate proteomic changes in A. baumannii and macrophages following infection in the absence or presence of the polymyxins. We discover that macrophages and polymyxins exhibit complementary effects to disarm several stress tolerance and survival strategies in A. baumannii, including oxidative stress resistance, copper tolerance, bacterial iron acquisition and stringent response regulation systems. Using the spoT mutant strains, we demonstrate that bacterial cells with defects in stringent response exhibit enhanced susceptibility to polymyxin killing and reduced survival in infected mice, compared to the wild-type strain. Together, our findings highlight that better understanding of host-pathogen-antibiotic interplay is critical for optimization of antibiotic use in patients and the discovery of new antimicrobial strategy to tackle multidrug-resistant bacterial infections.