Peptide-conjugated Nanoparticle Platforms for Targeted Delivery, Imaging, and Biosensing Applications.

Peptides have become an indispensable tool in engineering of multifunctional nanostructure platforms for biomedical applications such as targeted drug and gene delivery, imaging and biosensing. They can be covalently incorporated into a variety of nanoparticles (NPs) including polymers, metallic nanoparticles, and others. Using different bioconjugation techniques, multifunctional peptide-modified NPs can be formulated to produce therapeutical and diagnostic platforms offering high specificity, lower toxicity, biocompatibility, and stimuli responsive behavior. Targeting peptides can direct the nanoparticles into specific tissues for targeted drug and gene delivery and imaging applications due to their specificity towards certain receptors. Furthermore, due to their stimuli-responsive features, they can offer controlled release of therapeutics into desired sites of disease. In addition, peptide-based biosensors and imaging agents can provide non-invasive detection and monitoring of diseases including cancer, infectious diseases, and neurological disorders. In this review, we covered the design and formulation of recent peptide-based NP platforms, as well as their utilization in in vitro and in vivo applications such as targeted drug and gene delivery, targeting, sensing, and imaging applications. In the end, we provided the future outlook to design new peptide conjugated nanomaterials for biomedical applications.

pH-sensitive release of nitric oxide gas using peptide-graphene co-assembled hybrid nanosheets.

Nitric oxide (NO) donating drugs such as organic nitrates have been used to treat cardiovascular diseases for more than a century. These donors primarily produce NO systemically. It is however sometimes desirable to control the amount, location, and time of NO delivery. We present the design of a novel pH-sensitive NO release system that is achieved by the synthesis of dipeptide diphenylalanine (FF) and graphene oxide (GO) co-assembled hybrid nanosheets (termed as FF@GO) through weak molecular interactions. These hybrid nanosheets were characterised by using X-ray diffraction, Raman spectroscopy, Fourier transform infrared spectroscopy, zeta potential measurements, X-ray photoelectron spectroscopy, scanning and transmission electron microscopies. The weak molecular interactions, which include electrostatic, hydrogen bonding and π-π stacking, are pH sensitive due to the presence of carboxylic acid and amine functionalities on GO and the dipeptide building blocks. Herein, we demonstrate that this formulation can be loaded with NO gas with the dipeptide acting as an arresting agent to inhibit NO burst release at neutral pH; however, at acidic pH it is capable of releasing NO at the rate of up to 0.6 µM per minute, comparable to the amount of NO produced by healthy endothelium. In conclusion, the innovative conjugation of dipeptide with graphene can store and release NO gas under physiologically relevant concentrations in a pH-responsive manner. pH responsive NO-releasing organic-inorganic nanohybrids may prove useful for the treatment of cardiovascular diseases and other pathologies.

Advances in pH Sensing: From Traditional Approaches to Next-Generation Sensors in Biological Contexts.

pH has been considered one of the paramount factors in bodily functions because most cellular tasks exclusively rely on precise pH values. In this context, the current techniques for pH sensing provide us with the futuristic insight to further design therapeutic and diagnostic tools. Thus, pH-sensing (electrochemically and optically) is rapidly evolving toward exciting new applications and expanding researchers' interests in many chemical contexts, especially in biomedical applications. The adaptation of cutting-edge technology is subsequently producing the modest form of these biosensors as wearable devices, which are providing us the opportunity to target the real-time collection of vital parameters, including pH for improved healthcare systems. The motif of this review is to provide insight into trending tech-based systems employed in real-time or in-vivo pH-responsive monitoring. Herein, we briefly go through the pH regulation in the human body to help the beginners and scientific community with quick background knowledge, recent advances in the field, and pH detection in real-time biological applications. In the end, we summarize our review by providing an outlook; challenges that need to be addressed, and prospective integration of various pH in vivo platforms with modern electronics that can open new avenues of cutting-edge techniques for disease diagnostics and prevention.

Predictors of clinical outcomes in patients with sepsis: A retrospective study from a tertiary care hospital in Pakistan.

OBJECTIVE: To assess associations between various clinic-demographic factors and clinical outcomes among patients treated for sepsis. METHODS: The retrospective study was conducted at the Aga Khan University Hospital, Karachi, and comprised data of all patients aged >18 years diagnosed with sepsis from January to December 2019. Multivariable logistic regression was used to evaluate independent associations between predictors and outcomes. Data was analysed using R packages. RESULTS: Of the 1,136 patients, 621(54.6%) were male and 515(45.3%) were female. The overall mean age was 59.05±16.91 years. Female gender (odds ratio: 1.029; 95% confidence interval: 1.03-1.64) was found to be an independent predictor of septic shock, while hypertension (odds ratio0.75; 95% confidence interval: 0.59-0.95) emerged as a protective factor. Chronic kidney disease (odds ratio: 1.539; 95% confidence interval: 1.14-2.07) was an independent predictor of prolonged length of stay, while older age appeared to be protective (odds ratio: 0.98; 95% confidence interval: 0.98-0.99). Mortality was associated with a significantly lower odds of Escherichia coli on culture (odds ratio: 0.26; 95% confidence interval: 0.12-0.54). CONCLUSIONS: Independent associations were found between specific patient characteristics and adverse clinical outcomes.

Microbes-mediated synthesis strategies of metal nanoparticles and their potential role in cancer therapeutics.

Past few years have seen a paradigm shift towards ecofriendly, green and biological fabrication of metal nanoparticles (MNPs) for diverse nanomedicinal applications especially in cancer nanotheranostics. Besides, the well-known green synthesis methods of plant materials, the potential of the microbial world (bacteria, fungi, alga, etc.) in biofabrication is equally realized. Biomolecules and enzymes in the microbial cells are capable of catalyzing the biosynthesis process. These microbial derived inorganic nanoparticles have been frequently evaluated as potential agents in cancer therapies revealing exciting results. Through, cellular and molecular pathways, these microbial derived nanoparticles are capable of killing the cancer cells. Considering the recent developments in the anticancer applications of microbial derived inorganic MNPs, a dire need was felt to bring the available information to a single document. This manuscript reviews not only the mechanistic aspects of the microbial derived MNPs but also include the diverse mechanisms that governs their anticancer potential. Besides, an updated literature review is presented that includes studies of 2019-onwards.

Fibrils Emerging from Droplets: Molecular Guiding Principles behind Phase Transitions of a Short Peptide-Based Condensate Studied by Solid-State NMR.

Biochemical reactions occurring in highly crowded cellular environments require different means of control to ensure productivity and specificity. Compartmentalization of reagents by liquid-liquid phase separation is one of these means. However, extremely high local protein concentrations of up to 400 mg/ml can result in pathological aggregation into fibrillar amyloid structures, a phenomenon that has been linked to various neurodegenerative diseases. Despite its relevance, the process of liquid-to-solid transition inside condensates is still not well understood at the molecular level. We thus herein use small peptide derivatives that can undergo both liquid-liquid and subsequent liquid-to-solid phase transition as model systems to study both processes. Using solid-state nuclear magnetic resonance (NMR) and transmission electron microscopy (TEM), we compare the structure of condensed states of leucine, tryptophan and phenylalanine containing derivatives, distinguishing between liquid-like condensates, amorphous aggregates and fibrils, respectively. A structural model for the fibrils formed by the phenylalanine derivative was obtained by an NMR-based structure calculation. The fibrils are stabilised by hydrogen bonds and side-chain π-π interactions, which are likely much less pronounced or absent in the liquid and amorphous state. Such noncovalent interactions are equally important for the liquid-to-solid transition of proteins, particularly those related to neurodegenerative diseases.

Recent Progress in Azobenzene-Based Supramolecular Materials and Applications.

Azobenzene-containing small molecules and polymers are functional photoswitchable molecules to form supramolecular nanomaterials for various applications. Recently, supramolecular nanomaterials have received enormous attention in material science because of their simple bottom-up synthesis approach, understandable mechanisms and structural features, and batch-to-batch reproducibility. Azobenzene is a light-responsive functional moiety in the molecular design of small molecules and polymers and is used to switch the photophysical properties of supramolecular nanomaterials. Herein, we review the latest literature on supramolecular nano- and micro-materials formed from azobenzene-containing small molecules and polymers through the combinatorial effect of weak molecular interactions. Different classes including complex coacervates, host-guest systems, co-assembled, and self-assembled supramolecular materials, where azobenzene is an essential moiety in small molecules, and photophysical properties are discussed. Afterward, azobenzene-containing polymers-based supramolecular photoresponsive materials formed through the host-guest approach, polymerization-induced self-assembly, and post-polymerization assembly techniques are highlighted. In addition to this, the applications of photoswitchable supramolecular materials in pH sensing, and CO2 capture are presented. In the end, the conclusion and future perspective of azobenzene-based supramolecular materials for molecular assembly design, and applications are given.

Genome-wide identification and expression analysis of metal tolerance protein (MTP) gene family in soybean (Glycine max) under heavy metal stress.

AIM: Plant metal tolerance proteins (MTPs) are plant membrane divalent cation transporters that specifically contribute to heavy metal stress resistance and mineral uptake. However, little is known about this family's molecular behaviors and biological activities in soybean. METHODS AND RESULTS: A total of 20 potential MTP candidate genes were identified and studied in the soybean genome for phylogenetic relationships, chromosomal distributions, gene structures, gene ontology, cis-elements, and previous gene expression. Furthermore, the expression of MTPs has been investigated under different heavy metals treatments. All identified soybean MTPs (GmaMTPs) contain a cation efflux domain or a ZT dimer and are further divided into three primary cation diffusion facilitator (CDF) groups: Mn-CDFs, Zn-CDFs, and Fe/Zn-CDFs. The developmental analysis reveals that segmental duplication contributes to the GmaMTP family's expansion. Tissue-specific expression profiling revealed comparative expression profiling in similar groups, although gene expression differed between groups. GmaMTP genes displayed biased responses in either plant leaves or roots when treated with heavy metal. In the leaves and roots, nine and ten GmaMTPs responded to at least one metal ion treatment. Furthermore, in most heavy metal treatments, GmaMTP1.1, GmaMTP1.2, GmaMTP3.1, GmaMTP3.2, GmaMTP4.1, and GmaMTP4.3 exhibited significant expression responses. CONCLUSION: Our findings provided insight into the evolution of MTPs in soybean. Overall, our findings shed light on the evolution of the MTP gene family in soybean and pave the path for further functional characterization of this gene family.

Evaluation of data analytics workshop using RStudio amongst medical students in Pakistan.

A quasi-experimental study was conducted at the Aga Khan University, Karachi, Pakistan, to evaluate the outcomes of a series of workshops on 25 medical students' statistical knowledge and acceptance of RStudio. The knowledge in each of the five sessions was assessed using pre- and post- knowledge-based quizzes. In addition, the Student's Attitude Towards Statistics (SATS-36) and the Technology Acceptance Model were used. Data analysis on RStudio revealed a statistically significant improvement in knowledge in all five sessions (p<0.05). SATS-36 showed statistically significant improvement in Cognitive Competence (p<0.001). RStudio had commendable acceptance with relatively high scores of Attitudes (behavioural intention, median = 6.00 [5.20-7.00]) and Utility (perceived usefulness, median = 5.20 [4.10-6.20]). In conclusion, medical students had improved statistical knowledge and acceptance towards the novel statistical tool. Hence, further studies must evaluate the effectiveness of RStudio when integrated as part of the medical curriculum.

Engineering carbon nanotubes for sensitive viral detection.

Viral infections have been proven a severe threat to human beings, and the pandemic of Coronavirus Disease 2019 (COVID-19) has become a societal health concern, including mental distress and morbidity. Therefore, the early diagnosis and differentiation of viral infections are the prerequisite for curbing the local and global spread of viruses. To this end, carbon nanotubes (CNTs) based virus detection strategies are developed that provide feasible alternatives to conventional diagnostic techniques. Here in this review, an overview of the design and engineering of CNTs-based sensors for virus detection is summarized, followed by the nano-bio interactions used in developing biosensors. Then, we classify the viral sensors into covalently engineered CNTs, non-covalently engineered CNTs, and size-tunable CNTs arrays for viral detection, based on the type of CNTs-based nano-bio interfaces. Finally, the current challenges and prospects of CNTs-based sensors for virus detection are discussed.

Integrating advancements in root phenotyping and genome-wide association studies to open the root genetics gateway.

Plant adaptation to challenging environmental conditions around the world has made root growth and development an important research area for plant breeders and scientists. Targeted manipulation of root system architecture (RSA) to increase water and nutrient use efficiency can minimize the adverse effects of climate change on crop production. However, phenotyping of RSA is a major bottleneck since the roots are hidden in the soil. Recently the development of 2- and 3D root imaging techniques combined with the genome-wide association studies (GWASs) have opened up new research tools to identify the genetic basis of RSA. These approaches provide a comprehensive understanding of the RSA, by accelerating the identification and characterization of genes involved in root growth and development. This review summarizes the latest developments in phenotyping techniques and GWAS for RSA, which are used to map important genes regulating various aspects of RSA under varying environmental conditions. Furthermore, we discussed about the state-of-the-art image analysis tools integrated with various phenotyping platforms for investigating and quantifying root traits with the highest phenotypic plasticity in both artificial and natural environments which were used for large scale association mapping studies, leading to the identification of RSA phenotypes and their underlying genetics with the greatest potential for RSA improvement. In addition, challenges in root phenotyping and GWAS are also highlighted, along with future research directions employing machine learning and pan-genomics approaches.

Factors Influencing the Intention to Pursue Surgery among Female Pre-Medical Students: A Cross-Sectional Study in Pakistan.

BACKGROUND: While gender disparities in surgery are documented worldwide, it is unclear to what extent women consider surgery as a career before embarking on their medical school journey. This study aimed to report the percentage of pre-medical women in Pakistan who intend to eventually specialize in surgery and assess the factors motivating and deterring this decision. METHODS: An online survey was conducted among female pre-medical (high school) students across Pakistan. Multivariable logistic regression was performed to determine motivating and deterring factors associated with the intention to pursue surgery. RESULTS: Out of 1219 female high-school students, 764 (62.7) intended to join medical school. Among these 764, only 9.8% reported an exclusive intent to pursue surgery, while just 20.3% reported considering other specialties in addition to surgery. Significant motivators to pursue surgery exclusively were the intellectual satisfaction of pursuing surgery (adjusted odds ratio: 2.302), having opportunities to travel internationally for work (2.300) and use cutting-edge technology (2.203), interest in the specialty of surgery (2.031), the social prestige of becoming a surgeon (1.910), and considering one's personality well-suited to surgery (1.888). Major deterrents included the lack of interest in surgery (adjusted odds ratio: 3.812), surgical education and training being too difficult (2.440) and lengthy (1.404), and the risk of aggressive behavior from patients (2.239). CONCLUSION: Even before entering medical school, most female pre-medical students have already decided against considering a future surgical career. Deterrents likely stem from women being pressured to conform to deep-seated societal expectations to dedicate their time and energy to domestic responsibilities.

Peptide-based coacervates as biomimetic protocells.

Coacervates are condensed liquid-like droplets formed by liquid-liquid phase separation of molecules through multiple weak associative interactions. In recent years it has emerged that not only long polymers, but also short peptides are capable of forming simple and complex coacervates. The coacervate droplets they form act as compartments that sequester and concentrate a wide range of solutes, and their spontaneous formation make coacervates attractive protocell models. The main advantage of peptides as building blocks lies in the functional diversity of the amino acid residues, which allows for tailoring of the peptide's phase separation propensity, their selectivity in guest molecule uptake and the physicochemical and catalytic properties of the compartments. The aim of this tutorial review is to illustrate the recent developments in the field of peptide-based coacervates in a systematic way and to deduce the basic requirements for both simple and complex coacervation of peptides. We review a selection of peptide coacervates that illustrates the essentials of phase separation, the limitations, and the properties that make peptide coacervates biomimetic protocells. Finally, we provide some perspectives of this novel research field in the direction of active droplets, moving away from thermodynamic equilibrium.

Biological Photothermal Nanodots Based on Self-Assembly of Peptide-Porphyrin Conjugates for Antitumor Therapy.

Photothermal agents can harvest light energy and convert it into heat, offering a targeted and remote-controlled way to destroy carcinomatous cells and tissues. Inspired by the biological organization of polypeptides and porphyrins in living systems, here we have developed a supramolecular strategy to fabricate photothermal nanodots through peptide-modulated self-assembly of photoactive porphyrins. The self-assembling nature of porphyrins induces the formation of J-aggregates as substructures of the nanodots, and thus enables the fabrication of nanodots with totally inhibited fluorescence emission and singlet oxygen production, leading to a high light-to-heat conversion efficiency of the nanodots. The peptide moieties not only provide aqueous stability for the nanodots through hydrophilic interactions, but also provide a spatial barrier between porphyrin groups to inhibit the further growth of nanodots through the strong π-stacking interactions. Thermographic imaging reveals that the conversion of light to heat based on the nanodots is efficient in vitro and in vivo, enabling the nanodots to be applied for photothermal acoustic imaging and antitumor therapy. Antitumor therapy results show that these nanodots are highly biocompatible photothermal agents for tumor ablation, demonstrating the feasibility of using bioinspired nanostructures of self-assembling biomaterials for biomedical photoactive applications.

One-Step Nanoengineering of Hydrophobic Photosensitive Drugs for the Photodynamic Therapy.

Nanoengineering of anticancer therapeutic drugs including photosensitizers is highly desired and extremely required for improved therapeutic efficacy. It remains a formidable challenge to achieve nanostructured colloidal particles directly starting from hydrophobic drugs due to their hydrophobic nature and ready aggregation in aqueous ambient. In this work, we report a facile method for a one-pot preparation of hydrophobic photosensitizer nanoparticles by coating with different types of polyelectrolyte as stabilizing agents. Regardless of negatively or positively charged polyelectrolyte used, including Poly-L-lysine (PLL, MW = 15 k-30 k), PLL (MW = 30 k-70 k), heparin, and hyaluronic acid (HA), the hydrophobic photosensitizer BDEA (2,5-Bis(4-(diethylamino)benzylidene)cyclopentanone) as a model drug can be readily manipulated into stable and well-dispersed nanoparticles with size of average 120 nm. Stabilization presumably contributes to electrostatic repulsion of the adsorbed polyelectrolyte layer onto nanoparticles. Their anticancer activity against the HeLa cell line shows that the endocytic internalization of these nanosystems is associated with antiproliferative effects after irradiation with visible light. The one-step preparation strategy may be an alternative approach for the design of nano-formulations of hydrophobic photosensitive drugs, presenting a potential for photodynamic antitumor therapy.

FBXO8 is a novel prognostic biomarker in different molecular subtypes of breast cancer and suppresses breast cancer progression by targeting c-MYC.

F-box only protein 8 (FBXO8) is a recently identified member of the F-box proteins, showcasing its novelty in this protein family. Extensive research has established FBXO8's role as a tumor suppressor in various cancers, including hepatocellular carcinoma, and colorectal cancer, Nevertheless, its functional, mechanistic, and prognostic roles in primary and metastatic breast cancer, particularly in different molecular subtypes of breast cancer, various stages, as well as its potential implications in immunotherapy, tumor microenvironment, and prognostic survival among breast cancer patients, remain unexplored. In this article, we employed a multi-dimensional investigation leveraging TCGA, TIMER, TISIDB, STRING, MEXPRESS, UALCAN, and cBioPortal databases to explore the underlying suppression mechanism of FBXO8 in breast cancer. FBXO8 negatively correlates with MYC, NOTCH, WNT and inflammatory signaling pathways in breast tumor microenvironment. Furthermore we conducted RT-PCR, western blot, cell proliferation, cell migration, and mRNA target gene RT-PCR analyses to elucidate the role of FBXO8 in breast cancer progression. Mechanistically, PTEN and FBXW7 expression were down-regulated and MYC, IL10, IL6, NOTCH1, WNT6 mRNA expressions were up-regulated in FBXO8 knockdown cell lines. c-MYC silenced cells showed an increase in FBXO8 protein level, which suggests a negative feedback loop between FBXO8 and c-MYC to control breast cancer metastasis. These findings illuminate the novel role of FBXO8 as a prognostic and therapeutic target across different molecular subtypes of breast cancer. Finally, through the utilization of virtual screening and Molecular Dynamics simulations, we successfully identified two FDA-approved medications, Ledipasvir and Paritaprevir, that demonstrated robust binding capabilities and interactions with FBXO8.

Modified Frailty Index as a Predictor of Adverse Outcomes in Elective Primary Hip and Knee Replacement Surgery Patients at a Tertiary Care Hospital in Pakistan: A Cross-Sectional Study.

Objective The objective was to evaluate the modified frailty index as a predictor of early (within 30 days) postoperative complications in total joint arthroplasty patients, in a low middle-income country. Material and methods A cross-sectional study was carried out which included patients with ages ranging from 23 to 86 years, who underwent elective primary Total Hip or Knee Arthroplasties (TKA or THA) between December 2021 and February 2023. Modified frailty index (mFI-5) was calculated and 30-day morbidity and mortality were recorded. Post-operative complications were categorized as either surgical or medical and recorded. Results A total of 175 patients were included, amongst whom the majority were females (68.6%, n=120) and the mean age was 60.5 ± 13.2 years. 85 patients (48.6%) had a mFI-5 score of one while 48 patients (27.4%) had a score of two. Superficial surgical site infection was the most common complication overall in 6 patients (3.4%); however, no case of prosthetic joint infection was noted. Deep vein thrombosis (DVT) was the most common medical complication (1.7%, n=3). 5 patients (2.9%) required re-admission and two mortalities were recorded within the 30-day interval. A significant association was noted between post-operative surgical complications and mFI-5 score (p-value = < 0.001), with the risk of complications increasing with a higher mFI-5 score. Smoking was noted to be a risk factor for post-operative medical complications as well as 30-day mortality. Conclusion The current study shows that the mFI-5 index can effectively be used as a predictor of postoperative complications in the South Asian region such as Pakistan. This should be calculated routinely and can be used as a tool for pre-operative assessment and counseling.

Rate and Predictors of Patient Satisfaction After Total Joint Arthroplasty: A Cross-Sectional Study in a Low-to-Middle-Income Country.

Objective This study aimed to assess the rate of patient satisfaction after primary total joint arthroplasty (TJA) using a validated satisfaction measure. Materials and methods A cross-sectional study was conducted, including all patients who underwent primary TJA between December 2021 and February 2023. The age of the study population was found to range from 23 to 86 years. Patient satisfaction was assessed using a validated tool comprising four questions and a quality of life (QoL) question. Results A total of 197 patients were included, with a mean age of 60.9 ± 12.7 years. Total knee replacement (TKR) was performed in 124 patients (62.9%), and total hip replacement (THR) in 73 patients (37.1%). The mean patient satisfaction score was 86.6 ± 14.4 out of a maximum of 100. A significant negative correlation was observed between the Charlson Comorbidity Index (CCI) and the overall satisfaction score (p-value = 0.029). The majority of the patients (52.3%, n = 103) answered that their QoL had greatly improved, and a similar level of improvement was noted in elderly vs. adult patients (p-value = 0.17). A significantly higher proportion of male patients reported improvement more than they ever expected compared to female patients, the majority of whom reported their QoL was greatly improved (p-value = 0.025). Conclusion Total joint arthroplasty has been shown to achieve good patient satisfaction and an improvement in QoL. However, an increased comorbidity index and female gender were identified as factors for reduced satisfaction. Hence, it is recommended to consider these factors and counsel patients accordingly based on local patient data.

Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants.

Biotic and abiotic stresses significantly affect plant fitness, resulting in a serious loss in food production. Biotic and abiotic stresses predominantly affect metabolite biosynthesis, gene and protein expression, and genome variations. However, light doses of stress result in the production of positive attributes in crops, like tolerance to stress and biosynthesis of metabolites, called hormesis. Advancement in artificial intelligence (AI) has enabled the development of high-throughput gadgets such as high-resolution imagery sensors and robotic aerial vehicles, i.e., satellites and unmanned aerial vehicles (UAV), to overcome biotic and abiotic stresses. These High throughput (HTP) gadgets produce accurate but big amounts of data. Significant datasets such as transportable array for remotely sensed agriculture and phenotyping reference platform (TERRA-REF) have been developed to forecast abiotic stresses and early detection of biotic stresses. For accurately measuring the model plant stress, tools like Deep Learning (DL) and Machine Learning (ML) have enabled early detection of desirable traits in a large population of breeding material and mitigate plant stresses. In this review, advanced applications of ML and DL in plant biotic and abiotic stress management have been summarized.