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Studies on Optical, Thermal and Mechanical Properties of Glasses for Automotive Applications
No full textThe research problem in the present thesis is related to improve the properties of automobile
windshield glasses and to explore the recyclability of these glasses to reduce the carbon
footprint. To achieve these objectives, first the study has been conducted on the commercial
available automobile windshield glasses of different cars. Based on the initial findings,
a series of silicate-based samples are synthesised using the melt-quench technique. The
physical, structural, optical, thermal and mechanical properties of the prepared glasses are
studied using various experimental techniques to determine their suitability as automobile
windshield glasses. Based on the above properties, the windshields with better optical and
mechanical properties are used for recycling and reusability by adding a small quantity of
glass formers and fining agents, such as B2O3, P2O5, NaCl, and KCl. The present thesis
contains five chapters followed by the references.
Chapter 1 deals with the basics of oxide glass, followed by the required properties for
windshield glass in vehicles and the methods with which these properties are adapted for
automotive applications. Furthermore, this chapter describes the types of glass used in automotive
applications, their required properties, and the role of different structural units,
their amounts and constituents in silicate-based glasses used in automobiles.
Chapter 2 is associated with the comprehensive review of the literature on silicate-containing
alkali and alkaline earth oxide glasses for automotive applications, particularly windshield
glasses, which highlights their critical role in enhancing the mechanical, thermal, and optical
properties of automotive glasses. Alkali, alkaline, and intermediate oxides influence the
optical properties of the glasses, such as transparency and refractive index, along with other
properties also. The presence of these oxides usually reduces the melting temperatures of the
glass composition and allows for high optical clarity, which is critical for vehicle windshield
glass. Based on this literature review, the motivation of the present study, along with the
objectives of the thesis are outlined at the end of this chapter.
Chapter 3 gives the details of the glass synthesis, their characterisation and testing using
various experimental techniques. The melt-quench method is employed to prepare the samples using conventional chemicals in powder form. X-ray diffraction (XRD) is used
to identify the nature of the as-prepared glasses, while Fourier transform infrared (FTIR)
and Raman spectroscopy are used to study the local structural units with respect to wave
numbers and compositions. The softening and characteristic temperatures are analysed using
a dilatometer and differential scanning calorimetry (DSC), respectively, to confirm the
thermal expansion and glassy nature of the synthesised samples. The elements present in
commercially available windshield glasses are analysed using energy-dispersive spectroscopy
(EDS). The elemental composition and oxidation states of the ions in the samples are also
examined through X-ray photoelectron spectroscopy (XPS) on selected glasses. Optical and
mechanical properties are investigated using diffuse reflectance spectroscopy (DRS) and a
Vickers microhardness tester, respectively.
Chapter 4 describes the results and discussion of the prepared samples. In this chapter,
interpretations of the data obtained from various characterisation techniques have been discussed
in detail. This chapter is further divided into five sub-sections.
The first section represents the properties of commercially available windshield glasses. XRD
confirms the amorphous nature of the windshield glass of various cars. The chemical composition
of available windshield glasses exhibits some variations in their glass composition.
Audi A6 Sedan (A6) shows the highest optical band gap (Eg) (3.66 eV), whereas BMW 7
Sedan (B7) shows the lowest Eg (3.40 eV). The highest transparency, hardness, and fracture
toughness are observed for the BMW 7 Sedan compared to other windshield glasses
due to the moderate contents of Al2O3 in its composition. The Al2O3 and K2O plays a
very important role in polymerisation and influence the optical and mechanical properties
of windshield glasses. Thus, the small variations in Al2O3 and K2O in glass compositions
lead to an increase in the transparency and mechanical properties as well as prevent devitrification
of these glasses.
In the second section, the properties of 64SiO2−16Na2O−12CaO−2Al2O3−(6−x)MgO−
(x)Li2O; (x = 0, 2, 4, and 6 mol%) system have been explained. XRD patterns confirm the
amorphous nature of the as-prepared glasses. FTIR and Raman spectra indicate that the
addition of Li2O instead of MgO changes the NBOs formation in asymmetric ways, such
as enrichment in Q3 and Q1 silicate structural units in place of Q2. The Eg decreases from
4.03 to 3.88 eV while the Urbach energy (Eu) increases (0.31-0.43 eV) with the addition of
Li2O content in place of MgO. The optical and mechanical properties decrease with the concentration of Li2O in the glasses due to the decrease in the structural unit connectivity.
The third section describes the properties of 64SiO2 − 16Na2O − 12CaO − 2Al2O3 − (6 −
x)MgO−(x)K2O; (x = 2, 4, and 6 mol%) systems. The density and oxygen packing density
decrease with the addition of K2O in place of MgO in the glasses. XRD patterns confirm the
shift in the broad halo between 20◦ to 30◦ with the increase in K2O content. The FTIR and
Raman spectra confirm the presence of Q1, Q2, and Q3 units of silicate in the glasses. K2O
addition increases the number of NBOs and the Urbach energy, as well as the transparency
of the glasses. On the other hand, the Eg decreases with increasing K2O concentration
because of the increase in ionicity in the glasses. The microhardness and fracture toughness
decreased due to the variation of the field strength of K+ and Mg2+ cations.
The fourth section describes the properties of (64−x)SiO2−(x)B2O3−16Na2O−12CaO−
2Al2O3 −6MgO; (x = 2, 4, and 6 mol%) systems. Adding B2O3 in place of SiO2 in glasses
shows a strong effect on optical and mechanical properties. The B2O3 content decreases the
density as well as the glass network volume. FTIR spectra show that, with the addition of
B2O3, the Q3 silicate structural units are dominating. The B2O3 content increases the hardness
of the glasses. The maximum hardness is observed for 6 mol% of B2O3 content glass,
i.e., 6.93 GPa. The 62S-2B sample has higher values of optical basicity, band gap, and oxide
ion polarisability, which is due to the presence of a higher number of NBOs in this glass.
The 52S-6B glass has the highest hardness and fracture toughness, which can be attributed
to the high bond strength of the B-O than the Si-O bond. The highest transparency is 88%
for 62S-2B glass.
The fifth section deals with the recycling of the BMW 7 Series Sedan windshield to study
their feasibility for reuse in automobiles. All the recycled glasses are formed without any
tendency of phase separation in glasses. With the addition of glass formers (B2O3 and P2O5)
and fining agents (NaCl and KCl), the tendency to diffuse alumina from a recrystallised
alumina crucible increases. The RB7 glass has the highest Eg, i.e., 3.54 eV instead of the
RB7B sample (3.43 eV). The RB7B and RB7P have the highest (2.25) refractive index,
which suggests that the maximum number of NBOs are formed in these glasses as compared
to other samples. The additives (B2O3, NaCl and KCl) decrease the hardness from 5.27 to
4.29 GPa while the fracture toughness increases from 0.54 to 0.63 MPa m1/2 of the recycled
glass. Interestingly, the RB7P shows better properties than the other recycled glasses. The
presence of Al2O3 and P2O5 was found to significantly influence the polymerisation process and thereby impact the optical as well as mechanical properties of the recycled glasses.
Chapter 5 describes the overall conclusion drawn from the physical, structural, optical,
thermal, and mechanical properties of the prepared samples. To enrich this work, the future
scope of the present study has also been given at the end of this chapter. Overall, 20 glasses
are prepared and characterised. The highest transparency, along with mechanical properties
and other properties, is observed in the SML-0, SML-6, 58S-6B and RB7 glasses. The recycled
glasses RB7 is usable without compromising the transparency and other properties.
This approach not only paves the way to decrease the carbon footprint but also increases
the circular economy. At last, the future scope of the work is proposed to explore more
recycling of the windshield glasses with different additives, such as TiO2/Cr2O3
Study of calcium silicate glasses derived from agro-food wastes and minerals for biomedical applications
No full textThe rapid expansion of the agricultural sector has led to a significant increase
in agricultural waste. Crop residues such as corn, rice, sugarcane, and wheat
are often openly burned, despite the detrimental environmental consequences.
In addition to agricultural waste, a substantial portion of the food produced
is also wasted and left to decompose without proper disposal mechanisms.
From both environmental and energy perspectives, the most effective waste
management strategy involves utilizing these wastes to synthesize value-added
products. Rice husk ash (RHA) and eggshells (ES) are among the most abundant
agro-food wastes and represent promising sources of SiO2 and CaCO3,
respectively. These agro-food wastes/ashes/powders also contain magnesium,
potassium, and other trace elements typically required for the production of
bioglass and bioglass-ceramics. Furthermore, agro-food wastes offer sustainable
and cost-effective alternatives to conventional precursors. This study focuses on
the synthesis of bioglasses with a base composition of 43SiO2-25CaO-25Na2O-
7P2O5 (wt%) using biowaste-derived silica and calcium, supplemented with
conventional precursors for P2O5, Na2O, and MgO. The effects of systematically
substituting CaO with MgO and Na2O with K2O were investigated. The
resulting glasses and glass-ceramics were characterized to determine their physical,
structural, thermal, and mechanical properties to assess their suitability
as biomaterials. Bioactive properties were evaluated in-vitro using simulated
body fluid (SBF). Furthermore, biocompatibility was assessed using the 3-(4,5-
dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay in human
peripheral blood mononuclear cells (PBMC)
Study of Performance and Combustion Parameters of a dual fuel engine runs on Blends of Diesel and Producer Gas using Forest Residue (Pine Cone and Coconut shell)
No full textIndia's economy is mostly agricultural-based and India's forest cover covers approximately 24% of land area.
Subsequently, tons and tons of biomass in the form of wastage are generated every year in the nation.In the
global arena, the three key fossil fuels, namely oil, coal, and natural gas, presently supply nearly 81.4% of
the primary energy requirements in the world. The next largest share, i.e., approximately 9.7%, comes from
biomass (mostly ethanol and biodiesel) and waste. Installed power generation capacity in India is 456747
MW as on Dec 2024. Total power generated capacity through Renewable energy is 46.3% of the total capacity
out of which Approximately 32% of India's total primary energy demand comes from biomass. Biomass is
produced in different forms like agriculture residue, forest residue, sewage sludge, herb residue, landfill gas
and biogas, and alcohol fuels .Biomass gasification is a prevalent utilized thermochemical procedure for the
transformation of biomass to useful products which are more useful and have more usesthan the raw material.
Gasification transforms biomass feedstocks into burnable gas (producer gas), which may be utilized in order
to generate mechanical and electrical energy, synthetic fuel, and chemicals.
Growing fears of depleting energy resources and adverse environmental conditions have driven the world
towards more green energy production methods. Faced with these new challenges, renewable energy sources
have become the number one priority industry. Amongst these, biomass too has been foremost in the list of
promising energy providers, providing a natural, green, and abundant source of fuel for replacement of
traditional fossil fuels. The work in this research is centered on the production of syngas through gasification
of dry coconut shells and pine cones in a downdraft gasifier and experimentation with thissyngas in a variable
compression ratio dual-fuel engine. Key parameters like gas composition, major fuel parameters (proximate
analysis), elemental chemical content (ultimate analysis), and energy content (calorific value) were measured
to determine the quality of dried coconut shells and pine cones as a fuel. Engine performance and other
performance characteristics were also tested to see how well the syngas derived from these biomass samples
can be utilized. The research shows that the syngas from dried coconut shells and pine cones can improve
engine performance. This places it in a good position to be used as a substitute fuel in dual-fuel systems
Fault Diagnosis and Condition Monitoring of Brushless DC Motor Drive for Electric Vehicle Applications
No full textN/AWith the increase in Electric Vehicles (EVs) adoption, the need for reliable, efficient, and
fault-tolerant motor drive systems also increases. Brushless Direct Current (BLDC) motors are
standard in EVs applications because they exhibit high efficiency, small size, and low
maintenance. However, like any electric machine, BLDC motors can incur faults in various
components that could affect motor behaviour (performance, safety, operating life). Therefore,
this thesis includes an in-depth study of developing and detecting faults and real-time condition
monitoring of BLDC motor drives for EVs. The research starts with comprehensively
classifying BLDC motors based on faults occurring during regular use. Common faults
occurring in BLDC motors include inter-turn, coil-to-coil, phase-to-phase, open-circuit, and
external motor-inverter connection faults. These external faults consist of single-phase, double
phase, and ground faults. Perform scanning of MATLAB/Simulink-based mathematical models
of healthy and faulty BLDC motor operation; the research scan will be successful by simulating
the system input-output and ascertaining the impact on the system's faulty operation. After that,
a Machine learning model is used to classify the healthy and faulty conditions of the motor.
For that, the next step is the significance of diagnostic methods, where signal-based
(monitoring data characterizations), model-based (simulated motor faults), and data-based
(using machine learning algorithms) diagnostic methods fundamentally apply features to
separate data characterizations of faults. Once this was done, the researcher trained and tested
the BLDC dataset through multiple machine learning algorithms: support vector machine
(SVM), k-nearest neighbors (KNN), decision trees, and neural networks, primarily to
demonstrate clarity to the BLDC dataset with faults, specifically the labelled faults
classification.
Complementary to the above, using the ANSYS Maxwell model captures and models
magnetic flux distribution for perimeter validation of scientific honesty and duplicated
accuracy for further experimentation or distinguishing different coils in experimentation.
Experimentation includes a built-in health monitoring setup across the STM32 NUCLEO
H743ZI2 microcontroller for monitoring BLDC systems health and processing time to
complete tasks of real-time data acquisition from various sensor inputs (voltage, current, speed,
vibration, and temperature). A fusing of the internal and external sensors where real-time data
acquisition acts separately in task order of voltage and current, speed and vibration,
temperature, and provides a processing buffer for separate data task acquisition. At the end,
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Opal-RT platform is utilized to study and analyze the characteristics of the motor, for the known
healthy and faulty conditions, at a high degree of fidelity.
The proposed research work has been implemented using MATLAB/Simulink software,
Ansys, Opal-RT, and further, experimentally validated over a small-scale laboratory set-up of
rating 1 kW, 610 rpm BLDC motor drive.
The main objectives behind the present work have been the following:
1.
2.
3.
4.
To the mathematical modelling of a healthy and faulty BLDC motor drive.
To analyze and diagnose faults of the BLDC motor drive.
To simulate a healthy and faulty BLDC motor drive in the MATLAB/Simulink, Ansys,
and Opal-RT environment.
To validate simulation results through an experimental setup.N/
Pre-Silicon Testchip (IO ring) validation
No full textThe design and verification of Input/Output (IO) circuits play a crucial role in ensuring reliable
communication between integrated circuits and external interfaces. This thesis presents the layout
design and verification of a level shifter, focusing on layout strategies, and verification
methodologies in CMOS technology.
The final layout was verified through Design Rule Check (DRC) and Layout vs. Schemtic (LVS)
validation using Cadence Layout Design Suite and Calibre, ensuring manufacturability and
functional correctness. Additionally, the level shifter is an essential circuit for voltage
translation between different power domains, especially in low-power and mixed-signal
designs. The layout for the level shifter was designed from scratch using Cadence tools,
following strict foundry guidelines. Rigorous DRC and LVS checks were performed using
Calibre to confirm its correctness and adherence to fabrication constraints.
This work highlights the importance of precise layout design and validation in IO circuit,
reducing the chances of post-silicon failures and ensuring first-pass success in silicon fabrication.
The successful completion of level shifter layout, and associated verification steps demonstrates
the effectiveness of the adopted design methodologies.
By presenting a practical implementation of level shifter design, this thesis keeps importance in
validation of IO and bridges the gap between theoretical VLSI concepts and real-world design
constraints, providing valuable insights for future IO circuit development
Synthesis of Heterocyclic Compounds and their Evaluation for Biological Activity
No full textIn this thesis we have designed and developed novel biological active heterocycles hybrid with different pharmacophores. Further we have evaluated the different biological activity of all the synthesized compounds. We investigated the compounds biosensing ability for detection of important biomarkers such as human serum albumin protein and bovine serum albumin protein for early detection of diseases such as liver disease, renal disease etc. through different spectroscopic methods using UV and Fluorescence spectrophotometer. We also investigated the mechanism of interaction of the active compound towards both the analytes. The next biological activities that we explored is antibacterial activity of the synthesized indole/naphthalimide appended benzimidazole and quinoxalinone hybrids. Further we investigated most potent compounds for different bacterial strains. The mechanism of action of most potent compounds were investigated
Study on Vibration Based Rotor System Fault Detection and Diagnosis Using Deep Learning Approaches
No full textAuthor Contact information- [email protected] research demonstrates the implementation of AI-based techniques for rotor machinery's fault detection and diagnosis through a comprehensive experimental and theoretical study applying various Machine Learning (ML), Deep Learning, and Domain Adaptation techniques. The work addresses the significant research gaps, including the domain adaptation task, which is the immediate Industrial requirement. Rotating machines play a vital role in various industries, and the failure of these systems poses a significant threat to asset, production, environment, and human life. The rotor may fail in different ways due to various types of faults; however, in most cases, the fault arises due to common reasons such as manufacturing errors or severe operational conditions. Most failures are due to the severe operational condition for getting higher yield, which is implemented to cope with ever-increasing global market growth and competitive environment. In general, catastrophic failures are observed due to the initiation of a sequence of faults. Initially, the presence of rotor mass imbalance induces other critical faults that eventually lead to catastrophic failures.
As an important measure, industries spend millions of funds on the maintenance of machines. In particular, fault prediction in the rotor system has always been a paramount activity in the industry. Out of different maintenance strategies, predictive maintenance gains importance as it enhances the equipment life and reliability of the rotor system. It includes various techniques of condition monitoring for rotor systems, out of which the vibration-based methods are quite popular, accurate, and relatively inexpensive. It is evident that technological enhancement in the last two decades has helped engineers to apply automation in maintenance, and Artificial intelligence based strategies have gained a lot of attention. In industries, using Machine Learning and Deep Learning in predictive maintenance for rotor systems has gained significant momentum in preventing untimely failures. However, there are challenges in digitization of plant maintenance, and ample efforts are underway globally to achieve a robust methodology for continuous health monitoring of rotor systems. Hence, the literature review has been conducted based on Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol guidelines, and research gaps were identified. Research gaps such as automatic feature extraction, overfitting, hyperparameters tuning, domain shift, biased prediction due to imbalance class size etc. are important and systematically analyzed in the present study. For this purpose, different techniques such as SMOTE, SMOTEBoost, ensembling, 1D-CNN, Genetic Algorithm (GA) based optimization, effective dropout layer positioning, Adversarial Discriminative Domain Adaptation (ADDA) etc. are implemented. An additional case of investigation of noise-based signals using the variational mode decomposition (VMD) approach is also studied to check the performance of the proposed methodology.
An experimental rig was designed and developed in the laboratory to generate faulty signals of a rotor system. Primarily, two types of faults, rotor mass imbalance and crack of different severity levels are considered for the study. Vibration signals are collected using accelerometers and NI data acquisition system. Various case studies have been conducted to benchmark the performance of the proposed methodology against the traditional ML, DL, and transfer learning approaches. Rotor mass imbalance signal is utilized to demonstrate the performance of the proposed methodology and finally tested for the rotor crack fault prediction on the rotor rig.
The results demonstrate that rotor mass imbalance fault prediction using machine learning with balanced class data achieved an accuracy of 95.63%. However, in the presence of noise, the accuracy decreased to 69.45%. Furthermore, the application of VMD to mitigate the effects of noise enhanced the prediction accuracy to 88.89%. However, the available data's class imbalance, which leads to overfitting, has decreased the prediction capabilities to 75.33 percent. After addressing the class imbalance with SMOTEBoost and noise with VMD, the prediction accuracy increased to 81.33%. The overfitting issue is further mitigated by the ensemble technique, and the maximal prediction accuracy of VMD-based Ensembled SMOTEBoost is as high as 86%.
The DL-based method, which uses 1D-CNN, achieved an 84.16% prediction performance with the help of 7 convolutional layers and hyperparameters manually tuned. The hyperparameter optimization problem of CNNs has been resolved with the proposed GA-optimized 1D-CNN. The strategic placement of the dropout layer in 1D-CNN's architecture addresses the inherent overfitting problems of CNNs, while GA optimizes hyperparameters, resulting in enhanced prediction accuracy of up to 97.47% with three convolutional layers. The results of the tests show that while GA optimization decreases the depth of CNN architecture, 1D-CNN based deep learning does away with the need for human intervention for feature extraction. Efficient dropout placement minimizes computational burden and duration by decreasing the learnable parameters of the CNN (network weights) while maintaining optimal prediction accuracy.
Domain adaptation test results show that ML couldn't achieve a better prediction accuracy of more than 41%, while DL could reach 52.3% with 3 convolutional layers and manually tuned hyperparameters. Hence, the domain shift issue has been addressed through ADDA's domain invariant feature learning algorithm. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA's architecture to take advantage of CNN's feature extraction capability. Further, the data class imbalance issue has been addressed through SMOTE. The domain adaptation approach of ADDA without SMOTE and the manually tuned hyperparameter could achieve a prediction performance of 60.1%. Hence, to improve the prediction capabilities of ADDA, the dropout layer, and GA-optimized hyperparameters have been used with 1D CNN to tackle CNN's overfitting and hyperparameter issues within ADDA's architecture. Thus, the proposed methodology, which uses SMOTE and a genetically tuned hyperparameter, could increase the domain adaptation prediction performance to a maximum of 86.87%. Thus, the proposed methodology successfully addresses the deep learning based domain adaptation challenges, and it can be scalable in the industrial environment for online continuous monitoring. Further, the present research boosts the possibility of Industry 4.0 implementation at a faster speed
Simulation and Verification of Steam Sterilization of Medical Device Trays
No full textThe sterilization of surgical instruments is essential in the medical field to prevent the
transmission of germs, bacteria, and viruses. This can be achieved using an autoclave, a device
that sterilizes instruments through the application of steam. In thisthesis, the processes of steam
generation in the steam generator and its distribution within the sterilization chamber have been
analysed numerically. This study included an in-depth review of relevant literature and
standards to identify the worst-case scenario that could be simulated among multiple trays. It
also examined the dynamics of evaporation-condensation, steam distribution within the
autoclave, and the effective elimination of microorganisms from the instruments. This research
leverages existing numerical studies in steam sterilization to evaluate their relevance and
efficacy in real-time tray sterilization applications. These studies offer valuable insights into
the crucial parameters and conditions necessary for achieving optimal sterilization results,
including temperature, pressure, and cycle duration. Thermocouple readings taken at six
locations within the tray closely aligned with the thermal simulation results, showing only a
minimal deviation of 0.0084%. The average surface temperature of the Acetabular shell trials
was recorded at 132.72°C which is requires for the deactivation of the microorganisms and the
average pressure maintained during the sterilization phase is also stabilized to 3.2 bar using a
user defined function. This study provides valuable insights that support the new product
development phase by guiding the design of trays to enhance steam penetration and to
minimize the necessity for comprehensive tray testing. This simulation technique demonstrates
an actual use of simulation methods in the design process. By incorporating simulation from
the beginning, companies can identify potential design defects before they happen, employ
correction procedures more effectively, and progress towards actual manufacturing with more
confidence. This entire developmental process also benefited by saving time and expenses and
enhancing efficiency in the product's reliability
Metagenomic Phenotyping of Chemical-Induced Metabolic Disease Models
No full textM.Sc. Thesis (Biotechnology)Background: The global burden of chronic metabolic disease is on the rise, and is
attributed to our lifestyle-related choices (e.g., high-calorie diet). Gut microbial dysbiosis that
includes altered microbial abundance, metabolic functions and decreased diversity has been
attributed to the intestinal-level trigger for non-communicable metabolic disease. Chemically
induced in vivo animal models were considered as the gold standard for studying the
mechanistic aspects of disease pathogenesis. For this purpose, several chemical-induced animal
models have been well established to study metabolic diseases. In line, streptozotocin (STZ)
induced diabetes models, acetaminophen (APAP) induced hepatic injury models and dextran
sulfate sodium (DSS) induced colitis models were well established. These chemical models
were orally gavaged to induce tissue-specific injury, thereby triggering metabolic
complications. Despite the well-established role of gut microbiota in triggering metabolic
diseases, it remains inconclusive whether beyond direct tissue damage these chemical models
also negatively impacts the gut microbiota. Therefore, we hypothesized that the metabolic
disease-causing role of these chemicals are in part attributed to the negative impact on the gut
microbiota.
Methodology: We examined the microbiota-modulating effects of STZ, APAP and DSS
using an in vitro pseudo-colonic model (AMMR). For this purpose, we anaerobically cultured
gut microbes using a proprietary in vitro pseudo-colon model for 24h and growth was
monitored periodically. The gut microbial inoculum was collected from individuals without
metabolic diseases. After incubation, the samples were removed, DNA extracted and subjected
to 16s rRNA sequencing of the V3-V4 hypervariable regions. The sequenced data was analysed
using the QIIME2 pipeline and metagenomic annotations were done based on SILVA database.
PICRUSt was used for microbial functional data analysis.
2
Results: The data showed distinct shifts in gut microbial abundance due to the treatments
relative to untreated control. The Firmicutes-to-Bacteroidetes ratio was increased in all groups
in comparison to control. A high abundance of Proteobacteria was observed in STZ group,
which was consistent with clinical data showing increased Proteobacteria in diabetic patients.
An increase in Actinobacteria and Roseburia, and a decrease in Akkermansia in APAP group
was clinically associated with hepatic injury. An increase in Bifidobacterium,
Faecalibacterium, and a decrease in Dubosiella in DSS group. The taurine & hypotaurine
metabolism, fatty acid degradation, D-alanine metabolism, sphingolipid metabolism, and
thiamine metabolism microbial functions were significantly induced upon treatment of STZ in
comparison to control. The upregulation of fatty acid biosynthesis, butanoate metabolism,
glutamine and glutamate metabolism, along with a decrease in krebs cycle, lipoic acid synthesis
in APAP group. The functional activity of fatty acid degradation and biosynthesis, arginine and
proline metabolism, bacterial chemotaxis, and ketone bodies were enhanced in DSS group
compared to control.
Conclusion: The data from these sets of experiments performed in a strictly anaerobic
pseudo-colon system suggest that STZ, APAP and DSS induce dramatic shifts in the gut
microbial population and their metabolic functions. Therefore, the metabolic disease-causing
potentials of these experimental models were also associated with triggering gut microbial
dysbiosis.
Keywords: Streptozotocin, Acetaminophen, Dextran sulfate sodium, Diabetes, Drug induced liver injury, Ulcerative colitis, AMMR, Gut microbiome, Metabolom
Immunoinformatic Aided Design of Conserved Peptides Containing Multi-Epitopes against Severe Fever with Thrombocytopenia Syndrome
No full textSevere Fever with Thrombocytopenia Syndrome Virus (SFTSV) is an emerging zoonotic
pathogen, primarily transmitted by Haemaphysalis longicornis ticks, with additional humanto-human and animal-to-human transmission routes. The case fatality rate associated with
Severe Fever with Thrombocytopenia Syndrome (SFTS) caused by the virus ranges from 5%
to over 30%, varying by demographic and regional factors. Vaccine development remains
challenging due to SFTSV's segmented genome, frequent genetic reassortment, and
considerable diversity; to date, no licensed vaccine is available. In this study, an
immunoinformatics-based strategy was employed to design a multiepitope peptide vaccine
targeting the conserved, immunogenic nucleocapsid (N) protein. A total of 1,872 N protein
sequences were curated to identify four conserved peptide regions (P1–P4). Advanced machine
learning tools (NetMHCpan, MHCflurry, NetMHCIIpan, DeepMHCII, CLBtope) were applied
to predict T and B cell epitopes, enabling broad HLA class I (11,576 alleles) and class II (5,625
alleles) coverage. Further antigenicity and allergenicity assessments refined the selection to
three peptides (P1–P3). Molecular docking analysis (HPEPDOCK) affirmed robust binding
affinity of these peptides (particularly P2 and P3) to a diverse set of HLA molecules (diverse
ten alleles for each HLA class). The three selected peptides were linked via flexible spacers to
generate six construct combinations. Tertiary structure prediction (Robetta) and subsequent
stability evaluations (Robetta confidence, ERRAT, Ramachandran scores) identified four stable
constructs (C1, C2, C5, C6). These constructs were further assessed through docking with Tolllike receptor 4 (TLR-4) using the HDOCK server to evaluate innate immune activation
potential. Notably, the C5 construct (P3-P1-P2 arrangement) displayed superior and native-like
TLR-4 binding efficiency. Collectively, our findings highlight the immunogenic promise of
these multi-epitope peptide constructs (especially C5) as valuable candidates for further
experimental validation in the pursuit of an SFTSV vaccine