Revealing Bipolar Photoresponse in Multiheterostructured WTe2-GaTe/ReSe2-WTe2 P-N Diode by Hybrid 2D Contact Engineering.

The van der Waals (vdW) heterostructures based on two-dimensional (2D) semiconducting materials have been thoroughly investigated with regard to practical applications. Recent studies on 2D materials have reignited attraction in the p-n junction, with promising potential for applications in both electronics and optoelectronics. 2D materials provide exceptional band structural diversity in p-n junction devices, which is rare in regular bulk semiconductors. In this article, we demonstrate a p-n diode based on multiheterostructure configuration, WTe2-GaTe-ReSe2-WTe2, where WTe2 acts as heterocontact with GaTe/ReSe2 junction. Our devices with heterocontacts of WTe2 showed excellent performance in electronic and optoelectronic characteristics as compared to contacts with basic metal electrodes. However, the highest rectification ratio was achieved up to ∼2.09 × 106 with the lowest ideality factor of ∼1.23. Moreover, the maximum change in photocurrent (Iph) is measured around 312 nA at Vds = 0.5 V. The device showed a high responsivity (R) of 4.7 × 104 m·AW-1, maximum external quantum efficiency (EQE) of 2.49 × 104 (%), and detectivity (D*) of 2.1 × 1011 Jones at wavelength λ = 220 nm. Further, we revealed the bipolar photoresponse mechanisms in WTe2-GaTe-ReSe2-WTe2 devices due to band alignment at the interface, which can be modified by applying different gate voltages. Hence, our promising results render heterocontact engineering of the GaTe-ReSe2 heterostructured diode as an excellent candidate for next-generation optoelectronic logic and neuromorphic computing.

Innovative aspects and applications of single cell technology for different diseases.

Recent developments in single-cell technologies have provided valuable insights from cancer genomics to complex microbial communities. Single-cell technologies including the RNA-seq, next-generation sequencing (NGS), epigenomics, genomics, and transcriptomics can be used to uncover the single cell nature and molecular characterization of individual cells. These technologies also reveal the cellular transition states, evolutionary relationships between genes, the complex structure of single-cell populations, cell-to-cell interaction leading to biological discoveries and more reliable than traditional bulk technologies. These technologies are becoming the first choice for the early detection of inflammatory biomarkers affecting the proliferation and progression of tumor cells in the tumor microenvironment and improving the clinical efficacy of patients undergoing immunotherapy. These technologies also hold a central position in the detection of checkpoint inhibitors and thus determining the signaling pathways evoked by tumor invasion. This review addressed the emerging approaches of single cell-based technologies in cancer immunotherapies and different human diseases at cellular and molecular levels and the emerging role of sequencing technologies leading to drug discovery. Advancements in these technologies paved for discovering novel diagnostic markers for better understanding the pathological and biochemical mechanisms also for controlling the rate of different diseases.

ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution.

Protein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi.

A robust and simple non-enzymatic electrochemical sensor based on carbon dots-metal oxide composite for the detection of metronidazole traces in food products.

A facile and simple electrochemical composite sensor, CDs-Ag@Cu2O-GA, prepared from carbon dots stabilized silver nanoparticles and copper oxide, was used as an electrocatalyst and signal amplifier for the non-enzymatic detection of antibiotic traces in food products. The prepared composite demonstrated excellent stability, sensitivity, and cost-effectiveness. The sensor was constructed by modifying a glassy carbon electrode (GCE) with CDs-Ag@Cu2O-GA, and the electroanalytical response was determined for the precise determination of metronidazole (MTZ) drug traces in milk. The analytical response signified fast electron transfer and accessibility of several electroactive sites, producing an amplified response for the reduction of MTZ. The quantitative analysis by the sensor revealed a good linear range (10-110 µM), a low limit of detection (7.1 × 10-7 molL-1), and a high sensitivity (1.5 µA µM-1 cm-2). Furthermore, the sensor displayed excellent potential for practical applications, verified by the good recovery of the drug from spiked milk samples.

Abusive supervision and helping behavior among nursing staff: a moderated mediation model.

PURPOSE: This study aims to explore the adverse impacts of abusive supervision on helping behaviors among employees, as mediating by intention to leave and moderating by Islamic work ethics (IWE). DESIGN/METHODOLOGY/APPROACH: A quantitative approach was employed, and the sample consisted of 283 nurses working in various public sector hospitals in Pakistan. The data analysis was conducted using SPSS and AMOS with the PROCESS macro. FINDINGS: The results suggest that abusive supervision diminishes helping behavior among nurses. Additionally, the study reveals that intention to leave mediates the relationship of abusive supervision and nurses' helping behavior. Moreover, the introduction of IWE as a boundary condition reveals that the mediated link is weaker when IWE is higher, and vice versa. PRACTICAL IMPLICATIONS: This study provides valuable insights for hospital authorities to develop intervention strategies and policies aimed at reducing abusive supervision in hospitals. Hospital management should also be aware of the detrimental effects of abusive supervision on nurses' helping behaviors, which can be mitigated by promoting ethical values aligned with IWE. ORIGINALITY/VALUE: This study makes a valuable contribution to the limited research on the link between abusive supervision and helping behaviors in hospital settings. It offers new perspectives by incorporating the Conservation of Resources theory, particularly within the healthcare sector. Furthermore, this research expands the current knowledge by investigating the mediating influence of intention to leave and the moderating effect of IWE in mitigating the adverse impact of abusive supervision on nurses' helping behavior in Pakistan's public sector hospitals.

Investigation of the role of Cremophor RH 40 and Cremophor EL in the inhibition of efflux pump of Pseudomonas aeruginosa.

Background: There is increasing emphasis on restoring the efficacy of existing antibiotics instead of developing new ones. Objectives: This study aimed to determine the role of Cremophor EL and Cremophor RH40 in the inhibition of efflux pumps in MDR Pseudomonas aeruginosa strains. Methods: Efflux pump-active MDR strains of P. aeruginosa were identified and confirmed by flow cytometry. The identified efflux-active strains were further subjected to determination of the MIC of ciprofloxacin and the synergistic role of non-ionic surfactants (Cremophor EL and Cremophor RH40) along with ciprofloxacin. Results: Out of 30 samples, 6 strains displayed high efflux pump activity. Both Cremophor EL and Cremophor RH40 showed efflux pump inhibitory roles. A 4-fold reduction in the MIC values of ciprofloxacin was observed when Cremophor EL was used along with ciprofloxacin, while a 6-fold reduction was observed when Cremophor RH40 was used along with ciprofloxacin. Both compounds showed synergistic effects with ciprofloxacin, ticarcillin and meropenem when used in a 24-well plate efflux pump inhibitory assay. Conclusion: The inhibition of the efflux pump of MDR Pseudomonas aeruginosa by non-ionic surfactants, namely, Cremophor RH40 and Cremophor EL, provided the best strategy to restore the efficacy of ciprofloxacin.

Effect modification of socioeconomic status in the association between contraception methods and couple's education: A secondary analysis of PDHS 2017-18.

Globally, the region of South Asia reports the highest number of women (87 million) with unmet needs of contraception. Amongst the lower-middle-income countries of South Asia, Pakistan has performed poorly in enhancing contraceptive prevalence, as evident by the Contraceptive Prevalence Rate (CPR) of 34%. Factors including restricted access to contraception, a restricted selection of techniques, cultural/religious resistance, gender-based hurdles, and societal factors, such as the couple's education level, are among the most important causes for this gap in desire and usage. Thus, this study aimed to evaluate the association between couple's education level and their influence on their choice of contraception. In addition, the study also assessed the role of socioeconomic status in modifying the association between couple's education and contraception choice. Using PDHS 2017-18 data, couple's education status, preferences of contraceptive use and wealth quintiles were analyzed through multinomial logistic regression after adjusting for other confounding factors. The findings of our study revealed that out of the total sample of 14,368 women, 67.52% (n = 9701) were categorized as non-users, 23.55% (n = 3383) employed modern contraceptive methods, and 8.94% (n = 1284) utilized traditional contraceptive methods. Multivariable analysis showed that educated couples belonging to higher socioeconomic status (SES) had the highest adjusted odds ratio [7.66 (CI: 4.89-11.96)] of using modern contraceptives as opposed to uneducated couples of low socioeconomic statuses. Our analysis also revealed that the odds of using modern contraceptives were higher amongst mothers with five or more children [8.55 (CI:7.09-10.31)] as compared to mothers with less children when adjusted for other covariates. Thus, this study concludes the dynamic interplay between couple's level of education, contraceptive preference, and socioeconomic status This study contributes valuable insights for the policy makers and stakeholders to understand the intricate relationship between these factors.

Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Barriers and facilitators to perinatal care of women with disabilities in lower- and middle-income countries: a study protocol for scoping review of qualitative studies.

BACKGROUND: The Sustainable Development Goals have put emphasis on equitable healthcare access for marginalised groups and communities. The number of women with disabilities (WWD) to marry and have children is rapidly increasing in low- and middle-income countries (LMICs). However, these women experience multifaceted challenges to seeking perinatal care in LMICs. The objective of this scoping review is to document key facilitators and barriers to seeking perinatal care by WWD. We also will propose strategies for inclusive perinatal healthcare services for women with disabilities in LMICs. METHODS: We will conduct a scoping review of peer-reviewed and grey literature (published reports) of qualitative and mixed-methods studies on facilitators and barriers to seeking perinatal care for women with functional disabilities from 2010 to 2023 in LMICs. An electronic search will be conducted on Medline/PubMed, Scopus and Google Scholar databases. Two researchers will independently assess whether studies meet the eligibility criteria for inclusion based on the title, abstract and a full-text review. ETHICS AND DISSEMINATION: This scoping review is based on published literature and does not require ethics approval. Findings will be published in peer-reviewed journals and presented at conferences related to reproductive health, disability and inclusive health forums.

Phytotherapeutic options for the treatment of epilepsy: pharmacology, targets, and mechanism of action.

Epilepsy is one of the most common, severe, chronic, potentially life-shortening neurological disorders, characterized by a persisting predisposition to generate seizures. It affects more than 60 million individuals globally, which is one of the major burdens in seizure-related mortality, comorbidities, disabilities, and cost. Different treatment options have been used for the management of epilepsy. More than 30 drugs have been approved by the US FDA against epilepsy. However, one-quarter of epileptic individuals still show resistance to the current medications. About 90% of individuals in low and middle-income countries do not have access to the current medication. In these countries, plant extracts have been used to treat various diseases, including epilepsy. These medicinal plants have high therapeutic value and contain valuable phytochemicals with diverse biomedical applications. Epilepsy is a multifactorial disease, and therefore, multitarget approaches such as plant extracts or extracted phytochemicals are needed, which can target multiple pathways. Numerous plant extracts and phytochemicals have been shown to treat epilepsy in various animal models by targeting various receptors, enzymes, and metabolic pathways. These extracts and phytochemicals could be used for the treatment of epilepsy in humans in the future; however, further research is needed to study the exact mechanism of action, toxicity, and dosage to reduce their side effects. In this narrative review, we comprehensively summarized the extracts of various plant species and purified phytochemicals isolated from plants, their targets and mechanism of action, and dosage used in various animal models against epilepsy.

Various pharmacological agents in the pipeline against intractable epilepsy.

Epilepsy is a noncommunicable chronic neurological disorder affecting people of all ages, with the highest prevalence in low and middle-income countries. Despite the pharmacological armamentarium, the plethora of drugs in the market, and other treatment options, 30%-35% of individuals still show resistance to the current medication, termed intractable epilepsy/drug resistance epilepsy, which contributes to 50% of the mortalities due to epilepsy. Therefore, the development of new drugs and agents is needed to manage this devastating epilepsy. We reviewed the pipeline of drugs in "ClinicalTrials. gov," which is the federal registry of clinical trials to identify drugs and other treatment options in various phases against intractable epilepsy. A total of 31 clinical trials were found regarding intractable epilepsy. Among them, 48.4% (15) are about pharmacological agents, of which 26.6% are in Phase 1, 60% are in Phase 2, and 13.3% are in Phase 3. The mechanism of action or targets of the majority of these agents are different and are more diversified than those of the approved drugs. In this article, we summarized various pharmacological agents in clinical trials, their backgrounds, targets, and mechanisms of action for the treatment of intractable epilepsy. Treatment options other than pharmacological ones, such as devices for brain stimulation, ketogenic diets, gene therapy, and others, are also summarized.

Lived experiences of COVID-19 patients admitted in isolation wards of healthcare centers in Peshawar, Pakistan: A phenomenological perspective.

The emergence of COVID-19 caused a significant global threat, affecting populations worldwide. Its impact extended beyond just physical health, as it inflicted severe damage and challenges to individuals' well-being, leading to a deterioration in mental health. The lived experiences of patients hold a paramount position to explore and understand their perception of care which can ultimately strengthen the health system's delivery domain. This study explores the lived experiences of patients in the isolation ward, their recovery, and the quality of care being provided in the hospital and its effects on their mental health. Study design: A phenomenological qualitative study using in-depth interviews. Methods: We conducted 11 in-depth interviews of COVID-19 patients admitted to the isolation ward of the public hospitals of Peshawar, Pakistan. Participants who stayed for a minimum of 10 days in an isolation ward were included in this study. Interviews were transcribed and analyzed using NVivo 12 software and generated five themes through inductive analysis. Results: Five themes emerged from the participants' lived experiences: Heading towards the hospital, Health Care Quality, Impact on Mental Health, Recovering from COVID-19 and Back on one's feet. These included all the positive and negative lived experiences. Socio-environmental factors along with their experiences of the disease itself and with the healthcare providers guided their reaction which was important conciliators in their experiences during the pandemic. Conclusion: Based on the findings, the environment of isolation had a major influence on the mental well-being of the individuals involved. Considering the important role of the ward environment in shaping patient experiences and outcomes prompts a reevaluation of healthcare practices and policies. By addressing these factors healthcare systems can strive for greater effectiveness, resilience, and compassion in managing the pandemic's impact on patient care.

CAPTURE: Comprehensive anti-cancer peptide predictor with a unique amino acid sequence encoder.

Anticancer peptides (ACPs) key properties including bioactivity, high efficacy, low toxicity, and lack of drug resistance make them ideal candidates for cancer therapies. To deeply explore the potential of ACPs and accelerate development of cancer therapies, although 53 Artificial Intelligence supported computational predictors have been developed for ACPs and non ACPs classification but only one predictor has been developed for ACPs functional types annotations. Moreover, these predictors extract amino acids distribution patterns to transform peptides sequences into statistical vectors that are further fed to classifiers for discriminating peptides sequences and annotating peptides functional classes. Overall, these predictors remain fail in extracting diverse types of amino acids distribution patterns from peptide sequences. The paper in hand presents a unique CARE encoder that transforms peptides sequences into statistical vectors by extracting 4 different types of distribution patterns including correlation, distribution, composition, and transition. Across public benchmark dataset, proposed encoder potential is explored under two different evaluation settings namely; intrinsic and extrinsic. Extrinsic evaluation indicates that 12 different machine learning classifiers achieve superior performance with the proposed encoder as compared to 55 existing encoders. Furthermore, an intrinsic evaluation reveals that, unlike existing encoders, the proposed encoder generates more discriminative clusters for ACPs and non-ACPs classes. Across 8 public benchmark ACPs and non-ACPs classification datasets, proposed encoder and Adaboost classifier based CAPTURE predictor outperforms existing predictors with an average accuracy, recall and MCC score of 1%, 4%, and 2% respectively. In generalizeability evaluation case study, across 7 benchmark anti-microbial peptides classification datasets, CAPTURE surpasses existing predictors by an average AU-ROC of 2%. CAPTURE predictive pipeline along with label powerset method outperforms state-of-the-art ACPs functional types predictor by 5%, 5%, 5%, 6%, and 3% in terms of average accuracy, subset accuracy, precision, recall, and F1 respectively. CAPTURE web application is available at https://sds_genetic_analysis.opendfki.de/CAPTURE.

Cholecystokinin neurotransmission in the central nervous system: Insights into its role in health and disease.

Cholecystokinin (CCK) plays a key role in various brain functions, including both health and disease states. Despite the extensive research conducted on CCK, there remain several important questions regarding its specific role in the brain. As a result, the existing body of literature on the subject is complex and sometimes conflicting. The primary objective of this review article is to provide a comprehensive overview of recent advancements in understanding the central nervous system role of CCK, with a specific emphasis on elucidating CCK's mechanisms for neuroplasticity, exploring its interactions with other neurotransmitters, and discussing its significant involvement in neurological disorders. Studies demonstrate that CCK mediates both inhibitory long-term potentiation (iLTP) and excitatory long-term potentiation (eLTP) in the brain. Activation of the GPR173 receptor could facilitate iLTP, while the Cholecystokinin B receptor (CCKBR) facilitates eLTP. CCK receptors' expression on different neurons regulates activity, neurotransmitter release, and plasticity, emphasizing CCK's role in modulating brain function. Furthermore, CCK plays a pivotal role in modulating emotional states, Alzheimer's disease, addiction, schizophrenia, and epileptic conditions. Targeting CCK cell types and circuits holds promise as a therapeutic strategy for alleviating these brain disorders.

Long extrachromosomal circular DNA identification by fusing sequence-derived features of physicochemical properties and nucleotide distribution patterns.

Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction . To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.

Shedding light on cholecystokinin's role in hippocampal neuroplasticity and memory formation.

The hippocampus is a crucial brain region involved in the process of forming and consolidating memories. Memories are consolidated in the brain through synaptic plasticity, and a key mechanism underlying this process is called long-term potentiation (LTP). Recent research has shown that cholecystokinin (CCK) plays a role in facilitating the formation of LTP, as well as learning and memory consolidation. However, the specific mechanisms by which CCK is involved in hippocampal neuroplasticity and memory formation are complicated or poorly understood. This literature review aims to explore the role of LTP in memory formation, particularly in relation to hippocampal memory, and to discuss the implications of CCK and its receptors in the formation of hippocampal memories. Additionally, we will examine the circuitry of CCK in the hippocampus and propose potential CCK-dependent mechanisms of synaptic plasticity that contribute to memory formation.

Dynamics of Malaria Incidence in Khyber Pakhtunkhwa, Pakistan: Unveiling Rapid Growth Patterns and Forecasting Future Trends.

BACKGROUND: Malaria remains a formidable worldwide health challenge, with approximately half of the global population at high risk of catching the infection. This research study aimed to address the pressing public health issue of malaria's escalating prevalence in Khyber Pakhtunkhwa (KP) province, Pakistan, and endeavors to estimate the trend for the future growth of the infection. METHODS: The data were collected from the IDSRS of KP, covering a period of 5 years from 2018 to 2022. We proposed a hybrid model that integrated Prophet and TBATS methods, allowing us to efficiently capture the complications of the malaria data and improve forecasting accuracy. To ensure an inclusive assessment, we compared the prediction performance of the proposed hybrid model with other widely used time series models, such as ARIMA, ETS, and ANN. The models were developed through R-statistical software (version 4.2.2). RESULTS: For the prediction of malaria incidence, the suggested hybrid model (Prophet and TBATS) surpassed commonly used time series approaches (ARIMA, ETS, and ANN). Hybrid model assessment metrics portrayed higher accuracy and reliability with lower MAE (8913.9), RMSE (3850.2), and MAPE (0.301) values. According to our forecasts, malaria infections were predicted to spread around 99,301 by December 2023. CONCLUSIONS: We found the hybrid model (Prophet and TBATS) outperformed common time series approaches for forecasting malaria. By December 2023, KP's malaria incidence is expected to be around 99,301, making future incidence forecasts important. Policymakers will be able to use these findings to curb disease and implement efficient policies for malaria control.

Revolutionizing Cancer Care: Advances in Carbon-Based Materials for Diagnosis and Treatment.

Cancer involves intricate pathological mechanisms marked by complexities such as cytotoxicity, drug resistance, stem cell proliferation, and inadequate specificity in current chemotherapy approaches. Cancer therapy has embraced diverse nanomaterials renowned for their unique magnetic, electrical, and optical properties to address these challenges. Despite the expanding corpus of knowledge in this area, there has been less advancement in approving nano drugs for use in clinical settings. Nanotechnology, and more especially the development of intelligent nanomaterials, has had a profound impact on cancer research and treatment in recent years. Due to their large surface area, nanoparticles can adeptly encapsulate diverse compounds. Furthermore, the modification of nanoparticles is achievable through a broad spectrum of bio-based substrates, including DNA, aptamers, RNA, and antibodies. This functionalization substantially enhances their theranostic capabilities. Nanomaterials originating from biological sources outperform their conventionally created counterparts, offering advantages such as reduced toxicity, lower manufacturing costs, and enhanced efficiency. This review uses carbon nanomaterials, including graphene-based materials, carbon nanotubes (CNTs) based nanomaterials, and carbon quantum dots (CQDs), to give a complete overview of various methods used in cancer theranostics. We also discussed their advantages and limitations in cancer diagnosis and treatment settings. Carbon nanomaterials might significantly improve cancer theranostics and pave the way for fresh tumor diagnosis and treatment approaches. More study is needed to determine whether using nano-carriers for targeted medicine delivery may increase material utilization. More insight is required to explore the correlation between heightened cytotoxicity and retention resulting from increased permeability.

Biochemical characterization of bioinspired nanosuspensions from Swertia chirayita extract and their therapeutic effects through nanotechnology approach.

Swertia chirayita is used as a traditional medicinal plant due to its pharmacological activities, including antioxidant, antidiabetic, antimicrobial, and cytotoxic. This study was aimed to evaluate the therapeutic efficacy of newly synthesized nanosuspensions from Swertia chirayita through nanotechnology for enhanced bioactivities. Biochemical characterization was carried out through spectroscopic analyses of HPLC and FTIR. Results revealed that extract contained higher TPCs (569.6 ± 7.8 mg GAE/100 g)) and TFCs (368.5 ± 9.39 mg CE/100 g) than S. chirayita nanosuspension, TPCs (500.6 ± 7.8 500.6 ± 7.8 mg GAE/100 g) and TFCs (229.5± 3.85 mg CE/100 g). Antioxidant activity was evaluated through DPPH scavenging assay, and nanosuspension exhibited a lower DPPH free radical scavenging potential (06 ±3.61) than extract (28.9± 3.85). Anti-dabetic potential was assessed throughα-amylase inhibition and anti-glycation assays. Extract showed higher (41.4%) antiglycation potential than 35.85% nanosuspension and 19.5% α-amylase inhibitory potential than 5% nanosuspension. Biofilm inhibition activity against E. coli was higher in nanosuspension (69.12%) than extract (62.08%). The extract showed high cytotoxicity potential (51.86%) than nanosuspension (33.63%). These nanosuspensions possessed enhanced bioactivities for therapeutic applications could be explored further for the development of new drugs.