The immunological and pharmacokinetic evaluation of Lipid-PLGA hybrid nanoparticle-based oxycodone vaccines.

The current opioid epidemic is one of the most profound public health crises facing the United States. Despite that it has been under the spotlight for years, available treatments for opioid use disorder (OUD) and overdose are limited to opioid receptor ligands such as the agonist methadone and the overdose reversing drugs such as naloxone. Vaccines are emerging as an alternative strategy to combat OUD and prevent relapse and overdose. Most vaccine candidates consist of a conjugate structure containing the target opioid attached to an immunogenic carrier protein. However, conjugate vaccines have demonstrated some intrinsic shortfalls, such as fast degradation and poor recognition by immune cells. To overcome these challenges, we proposed a lipid-PLGA hybrid nanoparticle (hNP)-based vaccine against oxycodone (OXY), which is one of the most frequently misused opioid analgesics. The hNP-based OXY vaccine exhibited superior immunogenicity and pharmacokinetic efficacy in comparison to its conjugate vaccine counterpart. Specifically, the hNP-based OXY vaccine formulated with subunit keyhole limpet hemocyanin (sKLH) as the carrier protein and aluminum hydroxide (Alum) as the adjuvant (OXY-sKLH-hNP(Alum)) elicited the most potent OXY-specific antibody response in mice. The induced antibodies efficiently bound with OXY molecules in blood and suppressed their entry into the brain. In a following dose-response study, OXY-sKLH-hNP(Alum) equivalent to 60 µg of sKLH was determined to be the most promising OXY vaccine candidate moving forward. This study provides evidence that hybrid nanoparticle-based vaccines may be superior vaccine candidates than conjugate vaccines and will be beneficial in treating those suffering from OUD.

Yeast Two-Hybrid Assay for Investigating Antiviral Innate Immunity.

Yeast two-hybrid (YTH) technology is a powerful tool for studying protein interactions and has been widely used in various fields of molecular biology, including the study of antiviral innate immunity. This chapter presents detailed information and experimental procedures for identifying virus-host protein interactions involved in immune regulation using yeast two-hybrid technology.

"All in one" lipid-polymer nanodelivery system for gene therapy of ischemic diseases.

Gene therapy offers a promising avenue for treating ischemic diseases, yet its clinical efficacy is hindered by the limitations of single gene therapy and the high oxidative stress microenvironment characteristic of such conditions. Lipid-polymer hybrid vectors represent a novel approach to enhance the effectiveness of gene therapy by harnessing the combined advantages of lipids and polymers. In this study, we engineered lipid-polymer hybrid nanocarriers with tailored structural modifications to create a versatile membrane fusion lipid-nuclear targeted polymer nanodelivery system (FLNPs) optimized for gene delivery. Our results demonstrate that FLNPs facilitate efficient cellular uptake and gene transfection via membrane fusion, lysosome avoidance, and nuclear targeting mechanisms. Upon encapsulating Hepatocyte Growth Factor plasmid (pHGF) and Catalase plasmid (pCAT), HGF/CAT-FLNPs were prepared, which significantly enhanced the resistance of C2C12 cells to H2O2-induced injury in vitro. In vivo studies further revealed that HGF/CAT-FLNPs effectively alleviated hindlimb ischemia-induced gangrene, restored motor function, and promoted blood perfusion recovery in mice. Metabolomics analysis indicated that FLNPs didn't induce metabolic disturbances during gene transfection. In conclusion, FLNPs represent a versatile platform for multi-dimensional assisted gene delivery, significantly improving the efficiency of gene delivery and holding promise for effective synergistic treatment of lower limb ischemia using pHGF and pCAT.

Quantitative analysis of soil potassium by near-infrared (NIR) spectroscopy combined with a three-step progressive hybrid variable selection strategy.

Soil potassium is a crucial nutrient element necessary for crop growth, and its efficient measurement has become essential for developing rational fertilization plans and optimizing crop growth benefits. At present, data mining technology based on near-infrared (NIR) spectroscopy analysis has proven to be a powerful tool for real-time monitoring of soil potassium content. However, as technology and instruments improve, the curse of the dimensionality problem also increases accordingly. Therefore, it is urgent to develop efficient variable selection methods suitable for NIR spectroscopy analysis techniques. In this study, we proposed a three-step progressive hybrid variable selection strategy, which fully leveraged the respective strengths of several high-performance variable selection methods. By sequentially equipping synergy interval partial least squares (SiPLS), the random forest variable importance measurement (RF(VIM)), and the improved mean impact value algorithm (IMIV) into a fusion framework, a soil important potassium variable selection method was proposed, termed as SiPLS-RF(VIM)-IMIV. Finally, the optimized variables were fitted into a partial least squares (PLS) model. Experimental results demonstrated that the PLS model embedded with the hybrid strategy effectively improved the prediction performance while reducing the model complexity. The RMSET and RT on the test set were 0.01181% and 0.88246, respectively, better than the RMSET and RT of the full spectrum PLS, SiPLS, and SiPLS-RF(VIM) methods. This study demonstrated that the hybrid strategy established based on the combination of NIR spectroscopy data and the SiPLS-RF(VIM)-IMIV method could quantitatively analyze soil potassium content levels and potentially solve other issues of data-driven soil dynamic monitoring.

Letter to the editor: The use of XperGuide® needle guidance software for CT guided thoracic sympathetic block.

With the introduction of modern cone beam computed tomography in the operating room, the benefits of imaging modalities in daily practice are recognized by an increasing number of clinicians. Newer generation imaging modalities include CT needle guidance software, which can aid the operator place the needle correctly during percutaneous intervention. This technique has several advantages over traditional percutaneous interventions, especially for high risk procedures like thoracic sympathectomy. We describe and discuss outcomes and possible advantages of applying CT guided needle placement using needle guidance software (XperGuide®) for percutaneous thoracic sympathetic blockade in 8 patients. Based on our findings, we conclude that the use of high quality imaging and needle guidance software such as XperGuide® may improve patient outcomes, and reduce the risk of adverse effects, providing a relatively easy, safe, and valuable alternative treatment strategy for thoracic sympathectomies.

Sliding mode control with self-adaptive parameters of a 5-DOF hybrid robot.

Due to the advantages of high stiffness, high precision, high load capacity and large workspace, hybrid robots are applicable to drilling and milling of complicated components with large sizes, for instance car panels. However, the difficulty in establishing an exact dynamic model and external disturbances affect the high accuracy control directly, which will decrease the machining accuracy and thereby affect the machining quality and efficiency of the system. Sliding mode control is an effective approach for high-order nonlinear dynamic systems since that it is very insensitive to disturbances and parameter variations. However, chattering may exist in traditional sliding mode control with fixed parameters, which results from a constant approaching speed. Besides, the approaching speed will affect the chattering strength directly. To solve these problems, a modified sliding mode controller with self-adaptive parameters is proposed to enhance the trajectory-tracking performance of a 5-degree-of-freedom hybrid robot. Firstly, the kinematic model of the robot is established. Then adopting the principle of virtual work, a rigid dynamic model of the robot is built. Based on the built dynamic model, a modified sliding mode control method is developed, of which the approaching speed is dependent on the system state. Finally, the sliding mode controller with self-adaptive parameters is created for a hybrid robot. The proposed sliding mode controller can achieve a rapid approaching speed and suppress chattering simultaneously. Simulation results demonstrate that the proposed modified sliding mode controller can achieve a comparatively accurate and smooth trajectory, which owns good robustness to external disturbances.

A Hybrid Concept Analysis of Frailty Among Older Adults Living in the Community.

AIM: To analyse the concept of frailty through a literature review and in-depth interviews. DESIGN: A hybrid model of concept analysis. METHODS: The theoretical phase identified 43 articles for reviewing the definition and measurement of frailty. Seven frail older adults were invited in the fieldwork phase for in-depth interviews. In the final analysis phase, results from the fieldwork and theoretical phases were integrated to obtain a final definition of frailty. RESULTS: Attributes of frailty were heterogeneous, involving dynamic/bidirectional, multidimensional and multiple systems. The antecedents of the concept were exposure to various stimuli and challenges in responding to these stimuli. Consequences included losing autonomy and adverse health outcomes. Four themes of frailty were identified based on the fieldwork data: 'accumulation of functional decline', 'powerlessness of coping with', 'vicissitudes of lived experience' and 'loss of autonomy and positivity'. CONCLUSIONS: The final definition of frailty was 'a dynamic and fluctuating process of powerlessness to manage biopsychosocial and environmental stimuli, involving functional decline and vicissitudes of life, which results in losing autonomy and positivity or adverse health outcomes'. IMPLICATIONS FOR THE PROFESSION AND/OR PATIENT CARE: Characterising the definition of frailty is essential for nurses to address the lived experiences of older adults when providing person-centred care and for developing interventions that meet the needs of frail older adults. IMPACT: Since some discrepancies existed in the definition of frailty from individual perception of older adults, combined in-depth interviews with a theoretical literature review were used to provide comprehensive insight. This concept analysis provides guidelines of training for nurses and opportunities to improving quality of life for community dwelling older adults. REPORTING METHOD: N/A. PATIENT OR PUBLIC CONTRIBUTION: No Patient or Public Contribution.

Inorganic-Enriched Solid Electrolyte Interphases: A Key to Enhance Sodium-Ion Battery Cycle Stability?

The characteristics of solid electrolyte interphase (SEI) at both the cathode and anode interfaces are crucial for the performance of sodium-ion batteries (SIBs). The research demonstrates the merits of a balanced organic component, specifically the organic sodium alkyl sulfonate (ROSO2Na) featured in this work, in conjunction with the inorganic sodium fluoride (NaF), to enhance the interfacial stability. Using a customized electrolyte, it has optimized the interphase, curbing excess NaF production, and created a thin and uniform NaF/ROSO2Na-rich SEI layer. It offers exceptional protection against interface deterioration, transition metal dissolution, and concurrently ensures a consistent reduction in interfacial impedance. This creative approach results in a substantial improvement in the performance of both the Na0.9Ni0.4Fe0.2Mn0.4O2 cathode and the hard carbon anode. The cathode demonstrates remarkable average Coulombic efficiency exceeding 99.9% and a capacity retention of 81% after 500 cycles. Furthermore, the Ah-level pouch cell has shown outstanding performance with an 87% capacity retention after 400 cycles. Moving beyond the prevailing focus on inorganic-rich SEI, these results highlight the effectiveness of the customized organic-inorganic hybrid SEI formulation in improving SIB technology, offering an adaptable solution that ensures superior interfacial stability.

Suppressing Hole Accumulation Through Sub-Nanometer Dipole Interfaces in Hybrid Perovskite/Organic Solar Cells for Boosting Near-Infrared Photon Harvesting.

The potential of hybrid perovskite/organic solar cells (HSCs) is increasingly recognized owing to their advantageous characteristics, including straightforward fabrication, broad-spectrum photon absorption, and minimal open-circuit voltage (VOC) loss. Nonetheless, a key bottleneck for efficiency improvement is the energy level mismatch at the perovskite/bulk-heterojunction (BHJ) interface, leading to charge accumulation. In this study, it is demonstrated that introducing a uniform sub-nanometer dipole layer formed of B3PyMPM onto the perovskite surface effectively reduces the 0.24 eV energy band offset between the perovskite and the donor of BHJ. This strategic modification suppresses the charge recombination loss, resulting in a noticeable 30 mV increase in the VOC and a balanced carrier transport, accompanied by a 5.0% increase in the fill factor. Consequently, HSCs that achieve power conversion efficiency of 24.0% is developed, a new record for Pb-based HSCs with a remarkable increase in the short-circuit current of 4.9 mA cm-2, attributed to enhanced near-infrared photon harvesting.

Chalcone derived bis-organosilane and its magnetic nanoparticles: Unveiling precision in selective Cu(II) ion detection and elucidating biocompatibility.

The escalating concern regarding the adverse effects of metal ion toxicity on both human health and environmental ecosystems necessitates the development of efficient detection methodologies. This study presents a focused investigation on the selective and sensitive detection of Cu(II) ions employing hybrid magnetic nanoparticles derived from chalcone-based bis-organosilane. These nanoparticles exhibit a notably low detection limit in the nano-scale range, rendering the sensor highly sensitive to Copper(II) ion detection while maintaining robust anti-interference capabilities, even in the presence of diverse metal ions. Real sample analysis confirms the sensor's efficacy in detecting Cu(II) ions below WHO-prescribed levels. Computational analyses reveal molecular interactions and biological activities, including potent antibacterial and antioxidant properties, suggesting promising applications. Furthermore, the biological effectiveness of chalcone-derived bis-organosilane is investigated, unveiling notable antibacterial efficacy and also exhibiting potential as a scavenger of free radicals, indicating promising applications in both antibacterial and antioxidant domains.

Hybrid Biatrial Ablation Versus Catheter Ablation for Patients With Nonparoxysmal Atrial Fibrillation and Enlarged Left Atrium.

OBJECTIVE: There is no consensus on the optimal ablation strategy for nonparoxysmal atrial fibrillation (NPAF) with enlarged left atrium. We aimed to explore whether hybrid ablation (HA) of combined thoracoscopic surgical ablation with catheter ablation (CA) was superior to CA alone in these patients. METHODS: Patients with NPAF and left atrial diameter (LAD) ≥45 mm who underwent hybrid biatrial ablation or CA procedure from June 2014 to July 2021 were included in this study. Propensity score matching was applied to select patients in each group. The primary endpoint was freedom from atrial tachyarrhythmias after procedures. RESULTS: After propensity score matching, 52 patients with enlarged left atrium (median LAD = 51 mm) were enrolled in each group. The median follow-up was 36 months. The probability of freedom from atrial tachyarrhythmias at 12, 24, and 36 months on antiarrhythmic drugs (AADs) was 70.1%, 65.4%, and 62.6% in the HA group and 34.3%, 29.4%, and 22.0% in the CA group, respectively (P < 0.001); off AADs was 57.1%, 52.7%, and 50.0% in the HA group and 25.0%, 16.2%, and 11.5% in the CA group (P < 0.001); on AADs after redo CA was 76.2%, 73.7%, and 73.7% in the HA group and 43.6%, 43.6%, and 38.2% in the CA group, respectively (P < 0.001); off AADs after redo CA was 62.5%, 60.1%, and 60.1% in the HA group and 30.4%, 25.1%, and 20.9% in the CA group, respectively (P < 0.001). CONCLUSIONS: For patients with NPAF and enlarged left atrium, hybrid biatrial ablation was superior to CA in sinus rhythm maintenance even if redo CA was performed.

pH-Sensitive Tacrolimus loaded nanostructured lipid carriers for the treatment of inflammatory bowel disease.

Inflammatory Bowel Disease is the chronic tissue inflammation of the lower part of the Gastrointestinal tract (GIT). Conventional therapeutic approaches face numerous challenges, often making the delivery system inadequate for treating the disease. This study aimed to integrate a pH-sensitive polymer and nanostructured lipid carriers (NLCs) to develop a hybrid nanocarrier system. Tacrolimus-loaded NLCs coated with Eudragit® FS100 (TAC-NLCs/E FS100) nanoparticles were prepared via double emulsion technique followed by an aqueous enteric coating technique. Various parameters, such as particle size, entrapment efficiency, and zeta potential were optimized using Design Expert software®. Cetyltrimethyl ammonium bromide (CTAB) was used as a cationic surfactant which induces a positive charge on the nanoparticles. These cationic NLCs can adhere to the mucosal surface, thereby enabling prolonged retention. In vitro drug release was assessed, and the results demonstrated that drug release was retarded at pH 1.2 corresponding to upper GIT pH and maximum drug was released at pH 7.4 (colonic pH). Moreover, we evaluated TAC-NLCs/E FS100 nanoparticles in murine colitis models to gauge the efficacy of both coated and uncoated NLCs formulation. The TAC-NLCs/E FS100 showed a pronounced reduction in induced colitis, as evident from the restoration of morphological features, improved histopathological scores, antioxidant levels, and decreased the levels of proinflammatory cytokines. Thus, pH-sensitive TAC-NLCs/EFS 100 are attributed to the enhanced localization and targeted delivery at the specific site.

Classification of Infant Cry Based on Hybrid Audio Features and ResLSTM.

Crying is one of the primary means by which infants communicate with their environment in the early stages of life. These cries can be triggered by physiological factors such as hunger or sleepiness, or by pathological factors such as illness or discomfort. Therefore, analyzing infant cries can assist inexperienced parents in better caring for their babies. Most studies have predominantly utilized a single-speech feature, such as Mel Frequency Cepstral Coefficients (MFCC), for classifying infant cries, while other speech features, such as Mel Spectrogram and Tonnetz, are often overlooked. In this study, we manually designed a hybrid feature set, MMT (including MFCC, Mel Spectrogram, and Tonnetz), and explored its application in infant cry classification. Additionally, we proposed a convolutional neural network based on residual connections and long short-term memory (LSTM) networks, termed ResLSTM. We compared the performance of different deep learning models using the hybrid feature set MMT and the single MFCC feature. This study utilized the Baby Crying, Dunstan Baby Language, and Donate a Cry datasets. The results indicate that the hybrid feature set MMT outperforms the single MFCC feature. The MMT combined with the ResLSTM method achieved the best performance, obtaining accuracy rates of 94.15%, 92.92%, and 95.98% on the three datasets, respectively.

A hybrid features fusion-based framework for classification of breast micronodules using ultrasonography.

BACKGROUND: Breast cancer is one of the leading diseases worldwide. According to estimates by the National Breast Cancer Foundation, over 42,000 women are expected to die from this disease in 2024. OBJECTIVE: The prognosis of breast cancer depends on the early detection of breast micronodules and the ability to distinguish benign from malignant lesions. Ultrasonography is a crucial radiological imaging technique for diagnosing the illness because it allows for biopsy and lesion characterization. The user's level of experience and knowledge is vital since ultrasonographic diagnosis relies on the practitioner's expertise. Furthermore, computer-aided technologies significantly contribute by potentially reducing the workload of radiologists and enhancing their expertise, especially when combined with a large patient volume in a hospital setting. METHOD: This work describes the development of a hybrid CNN system for diagnosing benign and malignant breast cancer lesions. The models InceptionV3 and MobileNetV2 serve as the foundation for the hybrid framework. Features from these models are extracted and concatenated individually, resulting in a larger feature set. Finally, various classifiers are applied for the classification task. RESULTS: The model achieved the best results using the softmax classifier, with an accuracy of over 95%. CONCLUSION: Computer-aided diagnosis greatly assists radiologists and reduces their workload. Therefore, this research can serve as a foundation for other researchers to build clinical solutions.

RECOMED: A comprehensive pharmaceutical recommendation system.

OBJECTIVES: To build datasets containing useful information from drug databases and recommend a list of drugs to physicians and patients with high accuracy by considering a wide range of features of people, diseases, and chemicals. METHODS: A comprehensive pharmaceutical recommendation system was designed based on the features of people, diseases, and medicines extracted from two major drug databases and the created datasets of patients and drug information. Then, the recommendation was given based on recommender system algorithms using patient and caregiver ratings and the knowledge obtained from drug specifications and interactions. Sentiment analysis was employed by natural language processing approaches in pre-processing, along with neural network-based methods and recommender system algorithms for modelling the system. Patient conditions and medicine features were used to make two models based on matrix factorization. Then, we used drug interaction criteria to filter drugs with severe or mild interactions with other drugs. We developed a deep learning model for recommending drugs using data from 2304 patients as a training set and 660 patients as our validation set. We used knowledge from drug information and combined the model's outcome into a knowledge-based system with the rules obtained from constraints on taking medicine. RESULTS: Our recommendation system can recommend an acceptable combination of medicines similar to the existing prescriptions available in real life. Compared with conventional matrix factorization, our proposed model improves the accuracy, sensitivity, and hit rate by 26 %, 34 %, and 40 %, respectively. In addition, it improves the accuracy, sensitivity, and hit rate by an average of 31 %, 29 %, and 28 % compared to other machine learning methods. We have open-sourced our implementation in Python. CONCLUSION: Compared to conventional machine learning approaches, we obtained average accuracy, sensitivity, and hit rates of 31 %, 29 %, and 28 %, respectively. Compared to conventional matrix factorisation our proposed method improved the accuracy, sensitivity, and hit rate by 26 %, 34 %, and 40 %, respectively. However, it is acknowledged that this is not the same as clinical accuracy or sensitivity, and more accurate results can be obtained by gathering larger datasets.

Multitarget Ligands that Comprise Opioid/Nonopioid Pharmacophores for Pain Management: Current State of the Science.

Chronic pain, which affects more than one-third of the world's population, represents one of the greatest medical challenges of the 21st century, yet its effective management remains sub-optimal. The 'gold standard' for the treatment of moderate to severe pain consists of opioid ligands, such as morphine and fentanyl, that target the µ-opioid receptor (MOP). Paradoxically, these opioids also cause serious side effects, including constipation, respiratory depression, tolerance, and addiction. In addition, the development of opioid-use disorders, such as opioid diversion, misuse, and abuse, has led to the current opioid crisis, with dramatic increases in addiction, overdoses, and ultimately deaths. As pain is a complex, multidimensional experience involving a variety of pathways and mediators, dual or multitarget ligands that can bind to more than one receptor and exert complementary analgesic effects, represent a promising avenue for pain relief. Indeed, unlike monomodal therapeutic approaches, the modulation of several endogenous nociceptive systems can often result in an additive or even synergistic effect, thereby improving the analgesic-to-side-effect ratio. Here, we provide a comprehensive overview of research efforts towards the development of dual- or multi-targeting opioid/nonopioid hybrid ligands for effective and safer pain management. We reflect on the underpinning discovery rationale by discussing the design, medicinal chemistry, and in vivo pharmacological effects of multitarget antinociceptive compounds.

Weakly Solvating Ether-Based Electrolyte Constructing Anion-Derived Solid Electrolyte Interface in Graphite Anode toward High-Stable Potassium-Ion Batteries.

Low-cost graphite has emerged as the most promising anode material for potassium-ion batteries (PIBs). Constructing the inorganic-rich solid electrolyte interface (SEI) on the surface of graphite anode is crucial for achieving superior electrochemical performance of PIBs. However, the compositions of SEI formed by conventional strongly solvating electrolytes are mainly organic, leading to the SEI structure being thick and causing the co-intercalation behavior of ions with the solvent. Herein, a weakly solvating electrolyte is applied to weaken the cation-solvent interaction and alter the cation solvation sheath structures, conducing to the inorganic composition derived from anions also participating in the formation of SEI, together with forming a uniformly shaped SEI with superior mechanical properties, and thus improving the overall performance of PIBs. The electrolyte solvation structure rich in aggregated ion pairs (AGGs) (69%) enables remarkable potassium-ion intercalation behavior at the graphite anode (reversible capacity of 269 mAh g-1) and highly stable plating/stripping of potassium metal anode (96.5%). As a practical device application, the assembled potassium-ion full-battery (PTCDA//Graphite) displays superior cycle stability. The optimizing strategy of cation solvation sheath structures offers a promising approach for developing high-performance electrolytes and beyond.

Complete genome sequence of Serratia marcescens D1_6, isolated from peat soil.

We present a complete genome of Serratia marcescens D1_6 isolated from peat swamp forest. The complete genome for the isolate D1_6 was constructed using data from Oxford Nanopore Technologies and Illumina. The genome of D1_6 has a total length of 4,996,151 bp, comprising a chromosome and a plasmid.

Efficient diagnostic classification of diverse pathologies through contextual eye movement data analysis with a novel hybrid architecture.

The analysis of eye movements has proven valuable for understanding brain function and the neuropathology of various disorders. This research aims to utilize eye movement data analysis as a screening tool for differentiation between eight different groups of pathologies, including scholar, neurologic, and postural disorders. Leveraging a dataset from 20 clinical centers, all employing AIDEAL and REMOBI eye movement technologies this study extends prior research by considering a multi-annotation setting, incorporating information from recordings from saccade and vergence eye movement tests, and using contextual information (e.g. target signals and latency of the eye movement relative to the target and confidence level of the quality of eye movement recording) to improve accuracy while reducing noise interference. Additionally, we introduce a novel hybrid architecture that combines the weight-sharing feature of convolution layers with the long-range capabilities of the transformer architecture to improve model efficiency and reduce the computation cost by a factor of 3.36, while still being competitive in terms of macro F1 score. Evaluated on two diverse datasets, our method demonstrates promising results, the most powerful discrimination being Attention & Neurologic; with a macro F1 score of up to 78.8%; disorder. The results indicate the effectiveness of our approach in classifying eye movement data from different pathologies and different clinical centers accurately, thus enabling the creation of an assistant tool in the future.

Silver secnidazole nano-hybrid emulsion-based probiotics as a novel antifungal formula against multidrug-resistant vaginal pathogens.

This study presents a novel approach to manage vaginal infections due to Candidiasis, utilizing a novel silver secnidazole nano-hybrid emulsion (Ag-Secn-NHE)-based probiotics and free Ag-Secn-NHE. Ag-Secn-NHE was prepared by simple homogenization‒ultrasonication technique and validated by using a ultraviolet‒visible scan, dynamic light scattering, transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy, and zeta potential. Saccharomyces cerevisiae (RCMB 002Y001) is the most effective probiotic-producing organism that demonstrates significant effects when combined with Ag-Secn-NHE. Ag-Secn-NHE-based probiotics showed significant antifungal effect compared to free Ag-Secn-NHE, silver nitrate, silver nanoparticles, secnidazole, secnidazole nanoemulsion, and commercial vaginal wash against multidrug-resistant vaginal pathogens. The highest inhibitory effect was achieved with Ag-Secn-NHE-based probiotic against Candida auris, Candida albicans, and Cryptococcus neoformans with minimal inhibitory concentration (MIC) 0.625 ± 0.002, 0.00625:1.25 ± 0.012 and 0.00625:1.25 ± 0.032 mg/mL, respectively, in comparison with Ag-Secn-NHE that show MIC at 0.00625:1.25 ± 0.612, 0.0125:2.5 ± 0.812, and 0.0125:2.5 ± 0.112 mg/mL (Ag:Secn). Ag-Secn-NHE-based- probiotic show minimum fungicidal concentration (MFC) at range from 2.5 to 20 mg/mL, wherever free Ag-Secn-NHE show MFC range from 5 to >20 mg/mL. Additionally, Ag-Secn-NHE-based probiotics have 75% inhibition of biofilm formation against C. auris and 60% inhibition of biofilm formation against both Cryptococcus neoformans and C. albicans in comparison with free Ag-Secn-NHE. Time-kill curves showed that the antifungal effect of Ag-Secn-NHE-based probiotics was fungistatic at 2MIC value after 4 h and after 16 h for Ag-Secn-NHE. TEM photographs showed that C. auris cells treated with Ag-Secn-NHE-based probiotic formula revealed severe deformations and distored ultrastructural changes. furthermore, results indicated that the Gamma radiation up to 15 kGy increases production of Ag-Secn-NHE in comparison with non-irradiated one.